{"as_of":"2026-08-19T00:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:23013db2429a6c8749f646a5b0288fbdfd31c0b203dfb227294dc6b4603d7e15","coverage":[{"denominator":78,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":78,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T16:55:54.969995Z","state":"measured"},{"denominator":78,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":78,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.12720/citation-record","integrity":"/paper/2501.12720/integrity","json":"/paper/2501.12720/citation-record.json","paper":"/paper/2501.12720"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.456892Z","title":"Challenges of big data analysis","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.456892Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:35c3b0ace2f7617bb5676e305e3ef23023c9ada71d913d73d902b835e820ca33","observation_id":"efe009c8-1286-44a0-ac3e-28125f1c76df","resolution":{"observed_at":"2026-08-10T16:55:54.456892Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.462791Z","title":"Fair data enabling new horizons for materials research","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.462791Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:add7f8da15cdcdd731bc94430a1c1da232f047d7972a07062da30b05d1ca4560","observation_id":"0317019c-d57a-4acb-a9b0-b52d4eba358b","resolution":{"observed_at":"2026-08-10T16:55:54.462791Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s40537-019-0206-3","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.710086Z","title":"Uncertainty in big data analytics: Survey, opportunities, and challenges","venue":null,"work_id":"37d7595f-8f64-4acc-b267-767cb5826589","year":2019},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.473572Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:2cf5662f208998848fd421376e329293b0a7d2cb8283797ef1fe87694eebcf02","observation_id":"3af1e252-9aa5-48b4-9cc6-1cddd317bb17","resolution":{"observed_at":"2026-08-10T16:55:55.715146Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.626710Z","title":"https://www.statista.com/statistics/871513/worldwide-data-created/","venue":null,"work_id":"cb41c212-2932-4a63-9c68-6e32d61b7b1c","year":2010},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.478889Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:d4867d6ae10f7f841d5b93322f63599669ca0756cd92f7f9d002a6937eea0e52","observation_id":"095b5c7d-ac6f-4bf9-a009-23063f9913b1","resolution":{"observed_at":"2026-08-10T16:55:58.631308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.11691","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.477188Z","title":"Big data for healthcare industry 4.0: Applications, challenges and future perspectives","venue":null,"work_id":"052dfb39-e24c-470b-8fe6-0becf048a351","year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.486032Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:3e078ca1c40b1a5d0772f380d9c324e3cd04b45559bd85725c8dd659f2ba359e","observation_id":"54d69622-32e4-41cb-a28d-399ef250e52b","resolution":{"observed_at":"2026-08-10T16:55:58.488171Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.ijinfomgt.2014.10.007","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.690958Z","title":"Beyond the hype: Big data concepts, methods, and analytics","venue":null,"work_id":"c50b99a1-8f8d-45d0-ac9f-6f8871bf812d","year":2015},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.496238Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:ad58fa6002ee791b8c96aee456f56a515afdf3f2611190010cfb1e76d6ba53b4","observation_id":"5a791655-bf74-4f17-b1b8-1699730b679e","resolution":{"observed_at":"2026-08-10T16:55:55.696249Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s40537-021-00468-0","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.672895Z","title":"Big data quality framework: A holistic approach to continuous quality management","venue":null,"work_id":"8664adfb-c843-4ff9-881f-d3d170545777","year":2021},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.503767Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:372227f4bc0123b83b81d043b05989fcc1230192a7e94b840cf73cf779fd84a1","observation_id":"da15de82-2a44-4531-88f3-4301f252ec8d","resolution":{"observed_at":"2026-08-10T16:55:55.678510Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.510334Z","title":"A novel rigorous measurement model for big data quality characteristics","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.510334Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:2e0570c9a61b0d5d903676fac0d34ec9837562114e94516508a81fd384d217d1","observation_id":"bbc0085e-5fd0-4d6c-b64d-12377ecece63","resolution":{"observed_at":"2026-08-10T16:55:54.510334Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-981-16-5036-9_30","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:08:50.044864Z","title":"Trends and future perspective challenges in big data","venue":"Smart innovation, systems and technologies","work_id":"89ee0023-9d10-4115-9453-56b775bf2e2a","year":2019},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.516862Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:9610144bfec51bbce2843763caac480fe3461da0b1d10ba33a95a7adacc32ed2","observation_id":"7051ed3d-6dd9-4187-9563-49212b4d44e9","resolution":{"observed_at":"2026-08-10T16:55:55.659907Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.529097Z","title":"big data","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.529097Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:7cad87e95df42eea01333f1c666028ab5ca4f9cb015204b28417be9542e352e6","observation_id":"075b215b-52e5-4a80-8d66-7c2a6351ab60","resolution":{"observed_at":"2026-08-10T16:55:54.529097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.612691Z","title":"3D data management: Controlling data volume, velocity and variety","venue":null,"work_id":"4dcb3af6-9e15-452c-bad7-181bb3765bd3","year":2001},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.538119Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:d019405a3a14a87437c07226d664421c7f582c57830eff6cab5a1bdc6613bc46","observation_id":"3de3f526-4fc7-4711-8cb5-5ac14b7f9443","resolution":{"observed_at":"2026-08-10T16:55:58.617325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2018.85396","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.296939Z","title":"A zero emission neighbourhoods data management architecture for smart city scenarios: Discussions toward 6vs challenges","venue":null,"work_id":"b02202e9-f893-4ca9-989c-66e965206357","year":2018},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.543782Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:31b20d5cc7f55a799b3818c6033f01596ca9647db5a880377356b00baab3c4ea","observation_id":"0f1ce658-37eb-4623-9410-842ddb2e7d83","resolution":{"observed_at":"2026-08-10T16:55:58.305980Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.jbusres.2016.08.003","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.621213Z","title":"A model for unpacking big data analytics in high -frequency trading","venue":null,"work_id":"9f40aefc-928d-4a4b-9a60-9ea0b6347f4b","year":2017},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.550251Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:bf6cd306f27c2d5f5bd9416051276bd28524d6452ba66107af9dd7d977db774f","observation_id":"cbfe1e3c-e961-4f08-8dd9-78922f05b715","resolution":{"observed_at":"2026-08-10T16:55:55.628951Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.555315Z","title":"A comprehensive scenario agnostic data lifecycle model for an eﬃcient data complexity management","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.555315Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:a8782de8d89f6c6aa90330be22ab0e1a701065cd379cfb510c9b383f4a98a51c","observation_id":"56f9a820-d21f-4fb4-9329-07599e8ca787","resolution":{"observed_at":"2026-08-10T16:55:54.555315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.561867Z","title":"Toward a novel measurement framework for big data (mega)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.561867Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:e825fb296fa41518ab85f7d563ccc1e339625a9b7ab399b0357ea9c7d973b679","observation_id":"6f35e37b-81e2-48b6-9e40-f14971c60d9d","resolution":{"observed_at":"2026-08-10T16:55:54.561867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2014.68206","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.059606Z","title":"Seven v's of big data understanding big data to extract value","venue":null,"work_id":"d6cc7d45-da62-4315-b7b5-945e63f2999f","year":2014},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.569012Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:2a652032884bc265b7be1e33253f5cd1880d64f65d5923eea631c8d2704faa72","observation_id":"32b6500c-bc1b-4057-abd9-af1b68279576","resolution":{"observed_at":"2026-08-10T16:55:58.067031Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"9785.2020","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:57.956116Z","title":"A study of big data analytics using apache spark with python and scala","venue":null,"work_id":"ea1d3a3c-218a-401f-9538-151d3f3ee904","year":2020},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.577926Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:b4dec489d289b0d726ba683845fe82561f28261ee2eaeacaa16da1eba8716c06","observation_id":"fa3ce7ee-d163-434d-a1d3-7fce390a05e1","resolution":{"observed_at":"2026-08-10T16:55:57.963720Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.585923Z","title":"Recent quality models in bigdata applications","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.585923Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:392f49c89390b927f742cac072ce958c69556d4ffaad7da586b817d56603507d","observation_id":"4c3f0964-3a2c-4ad2-8073-79763cf58c31","resolution":{"observed_at":"2026-08-10T16:55:54.585923Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0089.30100","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:57.748642Z","title":"Deﬁning big data","venue":null,"work_id":"93d8997c-1c66-40ae-bbd1-9428fc46bd2a","year":2016},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.593097Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:9787478b19b238071bf91a8297497c838fe5ddf5cdaedf0260e707233998819b","observation_id":"953c9a2f-2cf4-4cac-8538-7ff7848241c2","resolution":{"observed_at":"2026-08-10T16:55:57.761959Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6299.30063","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:57.639244Z","title":"Towards a comprehensive data lifecycle model for big data environments","venue":null,"work_id":"161a7e68-eae6-44f7-a823-0770cf8dfa6b","year":2016},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.598653Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:7b395ccdaeb76a79e674d3bc96676ef8762b3c7598b8df5464f2ff70e449f90b","observation_id":"96a3ac1b-1cab-49fa-87c9-94932502daa3","resolution":{"observed_at":"2026-08-10T16:55:57.646163Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.10205","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:57.555050Z","title":"Understanding the impact of big data on ﬁrm performance: The necessity of conceptually diﬀerentiating among big data characteristics","venue":null,"work_id":"ba5f50fa-e5ef-4c29-801b-79caa18336da","year":2021},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.609500Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:4b9b13a7634af1f7fec178de42891d6ce9797c66510b366c95b4e4388d1e2131","observation_id":"7bd8bb4f-900c-4b0a-8991-02443da60f06","resolution":{"observed_at":"2026-08-10T16:55:57.561438Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.29158","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:57.447961Z","title":"A global manufacturing big data ecosystem for fault detection in predictive maintenance","venue":null,"work_id":"4890ecd6-b2d0-427d-86b0-a0c12f580f95","year":2019},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.618207Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:a206772f9b2539beea621d0d9299bccfd663019771fab4ee67b1ddaeb8a3d641","observation_id":"9510155e-6c47-4691-8f20-e87a928a047b","resolution":{"observed_at":"2026-08-10T16:55:57.458243Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2018.28792","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:57.353438Z","title":"Big data perspective for driver/driving behavior","venue":null,"work_id":"be90d3de-465e-4c26-afc9-707402d990d7","year":2018},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.626493Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:789ac79e42eff16e10d2d550a672382d99ad2beb36e756f858e6267c0a65b5b5","observation_id":"be0ce2ef-78cd-4d1f-a5eb-9b693783fe71","resolution":{"observed_at":"2026-08-10T16:55:57.360622Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.630498Z","title":"A trustworthy privacy preserving framework for machine learning in industrial IoT systems","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.630498Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:3294272baa9e77d754a31760692641e94f831778a804bd13286cdc501a9ff95e","observation_id":"14dc0f7b-9b0f-47e6-b922-b3c04c429014","resolution":{"observed_at":"2026-08-10T16:55:54.630498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.597071Z","title":"Mu l ti-sensor information fusion based on machine learning for real applications in human activity recognition: State -of-the-art and research challenges","venue":null,"work_id":"06ee8c59-6edb-4600-8f4a-186344ce18e8","year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.636918Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:e0a0ea9d84a298b8fe5ca72a3b8d73270aa9e72c30988b2f900ce0e492e28a9b","observation_id":"0f5b567a-9413-4cf9-9215-33ad0071b760","resolution":{"observed_at":"2026-08-10T16:55:58.602531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2014.23007","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:57.170937Z","title":"Internet of things in industries: A survey","venue":null,"work_id":"aa8be971-56b7-4fed-854a-6f7a57595712","year":2014},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.642543Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:07252b7612f4f72150d6cb0c1e805d3775bc4dde4a42512eeadde24cc4860f94","observation_id":"a07e96f8-fccb-46ef-bfa6-a7929fe17872","resolution":{"observed_at":"2026-08-10T16:55:57.177647Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.647217Z","title":"Industry 4.0: A survey on technologies, applications and open research issues","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.647217Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:b0d2dc473b3b76f99d7ce7960737621dc031d26fa0fea8d8444d0c9963b1fd1b","observation_id":"8ec2f6db-5cd9-4de3-9312-c67e6fd36252","resolution":{"observed_at":"2026-08-10T16:55:54.647217Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3511904","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.597062Z","title":"Privacy-aware traﬃc ﬂow prediction based on multi-party sensor data with zero trust in smart city","venue":null,"work_id":"25a71963-fe6b-4376-8e89-f3145ff8b733","year":2023},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.652954Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:ad57d24b835096564beb849431631d8dc5ddb5ed44e6708bb83e962e81b55949","observation_id":"e9b87867-dd42-45b7-8074-7e4c19af6ec1","resolution":{"observed_at":"2026-08-10T16:55:55.602072Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.future.2021.12.012","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.575891Z","title":"Serverless data pipeline approaches for IoT data in fog and cloud computing","venue":null,"work_id":"ff32253c-4c59-4e69-9d19-751df8e89ab1","year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.657013Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:b56c54ce797f86377a72697b13c2569ded518143f45395d5a635504973f53eaa","observation_id":"8a8ad733-e8d8-4b3b-8ea9-32e4665e6af5","resolution":{"observed_at":"2026-08-10T16:55:55.580869Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.12251","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:57.073165Z","title":null,"venue":null,"work_id":"a6d64997-730f-4e81-adc6-ccc2999f1739","year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.663109Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:3f0131ae5406d7cebaa5bd94ed8adfdeb3c6095077d8c0c6c4d4e6a5b01d03ce","observation_id":"6950e287-33c9-4508-8b13-f7daf43ffc8c","resolution":{"observed_at":"2026-08-10T16:55:57.079097Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.671146Z","title":"Human activity recognition in artiﬁcial intelligence framework: A narrative review","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.671146Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:4160e159e91775642f0f22b542cdb0729b385ef34eb939e898e2b16271c0dd4b","observation_id":"85ad143e-bbd2-4ad7-b423-166f68689171","resolution":{"observed_at":"2026-08-10T16:55:54.671146Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s40537-021-00481-3","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.534357Z","title":"Dalif: A data lifecycle framework for data-driven governments","venue":null,"work_id":"838b530a-f1eb-482e-b9e3-cf06a1fbe27c","year":2021},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.676875Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:a6cc9ed31be0c43274bc4ef2642bd399b51140cf2f08e5efd464f889a2d103b8","observation_id":"c93a18cf-85c2-420e-8a41-7def744bea64","resolution":{"observed_at":"2026-08-10T16:55:55.541174Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1126/science.abg1780","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.513588Z","title":"Big-data approaches lead to an increased understanding of the ecology of animal movement","venue":null,"work_id":"591e1d45-06cb-4092-a12c-1cbced398cb4","year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.681939Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:ad869d22b008308223c216fa9d5fddb43520356974f7873a11ca62cb27971c94","observation_id":"6910cea9-fe5f-4554-9897-3e6770d0dff0","resolution":{"observed_at":"2026-08-10T16:55:55.521369Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1111/jpim.12545","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.495350Z","title":"Big data for creating and capturing value in the digitalized environment: Unpacking the eﬀects of volume, variety, and veracity on ﬁrm performance","venue":null,"work_id":"b197f6b5-e8da-4cd5-a978-1733f5d54d4f","year":2021},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.690283Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:b62a71bec7c34728d98ad5d9e1047b2cb36d05849d583c971c999aa80356e4b7","observation_id":"7b0b979b-3dee-46c6-81f3-08ca30c5e4c4","resolution":{"observed_at":"2026-08-10T16:55:55.501490Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/0950-5849(93)90069-f","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.471079Z","title":"A model of the data (life) cycles with application to quality","venue":null,"work_id":"e6d68c1d-1910-49f1-b76e-581de79ec6e4","year":1993},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.702988Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:18bdca2a761c47d6b675f6922822de64af3bc6991231a31346b1b9fba9a6e8a2","observation_id":"aaef9993-60a3-44c0-bc89-eab88295c66b","resolution":{"observed_at":"2026-08-10T16:55:55.480857Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.bdr.2015.01.001","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.451773Z","title":"Reference architecture and classiﬁcation of technologies, products and services for big data systems","venue":null,"work_id":"78c2efc4-313c-4a27-b7cf-b0a02871dedd","year":2015},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.721521Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:af9098d2cadc8b2c4718e9e6ba057cee56a5dd7fdebb18b5c371f4629abc0ee8","observation_id":"a7c4f93a-4cb0-4177-8ae2-5533aea6fcd9","resolution":{"observed_at":"2026-08-10T16:55:55.457503Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.is.2008.04.003","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.426884Z","title":"Toward data mining engineering: A software engineering approach","venue":null,"work_id":"67da7994-675e-42c3-a58a-61f883a5cd29","year":2009},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.727987Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:c604b57184bd8794a545db72593d6c9a24786b3faded03963ded42ede05329b5","observation_id":"75ae81cc-843a-49d9-9213-0c1931847943","resolution":{"observed_at":"2026-08-10T16:55:55.432026Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.732466Z","title":"CRISP-DM twenty years later: From data mining processes to data science trajectories","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.732466Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:7798026327a46c85e8862e8089e8b635f7587088a0330683c2404924c890f13f","observation_id":"1301bbcc-58b3-4a44-a6e9-4db2484a89d5","resolution":{"observed_at":"2026-08-10T16:55:54.732466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.574395Z","title":"C RISP-DM: Towards a standard process model for data mining","venue":null,"work_id":"f7ad461e-2985-47f1-89ad-848868870fb2","year":2000},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.738754Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:ae4ece8296ea741c31238a3c4365e205a3b44055291561f85b70149b3dfb6a60","observation_id":"c980303a-b0e9-4007-af55-da351ebb1041","resolution":{"observed_at":"2026-08-10T16:55:58.579267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.87307","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:56.866125Z","title":"APREP-DM: A framework for automating the pre-processing of a sensor data analysis based on CRISP-DM","venue":null,"work_id":"f4f1f0cd-d8a9-459f-919c-b3e6c1511018","year":2019},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.743753Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:93dfedc3c6c2433f35b858f2518c5ae0d2c336e3bab26c8a6d5a9ccf91bd5231","observation_id":"bfd56f4e-71bf-4bd2-967b-31a16884f9b9","resolution":{"observed_at":"2026-08-10T16:55:56.874491Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.10141","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:56.753937Z","title":"Variations of length of stay: A case study using control charts in the CRISP-DM framework","venue":null,"work_id":"98f18fe2-501b-45ef-9256-7d78e0d0bdcd","year":2019},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.751520Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:b508439d8b7ad5e93b860c69f3afacbc953e139798a7928ff70f08a66bd8a014","observation_id":"5656adff-df21-4a52-947b-dffc2de44620","resolution":{"observed_at":"2026-08-10T16:55:56.761755Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.554346Z","title":"C RISP data mining methodology extension for medical domain","venue":null,"work_id":"3aa9eb65-c311-4c6c-b648-39e78bb43ea1","year":2015},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.758833Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:fd4c71985d536d3ce41e7669206d649405ae603c8de4a90684aedeec341a00a7","observation_id":"aa5d667f-3708-41a7-8cce-62c9f0e15282","resolution":{"observed_at":"2026-08-10T16:55:58.560003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.537229Z","title":"Evaluating frameworks for implementing machine learning in signal processing: A comparative study of CRISP-DM, SEMMA and KDD","venue":null,"work_id":"d0f25941-043b-4456-92b8-34428995a2ae","year":2018},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.764585Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:88c540b9e0e80ad1936107b9ea9ea39d22979454a826ec7b5bfbc33eb8390f34","observation_id":"e77767b8-1cd6-4e9d-90ed-6fe34a65d838","resolution":{"observed_at":"2026-08-10T16:55:58.542412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2018.86912","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:56.626093Z","title":"Synthesizing CRISP-DM and quality management: A data mining approach for production processes","venue":null,"work_id":"5e910492-f173-4208-b223-35a41baa15f2","year":2018},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.769650Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:84ac126dcc84261aa5422c29e7a64e7bc4294165a9b78f9431a40cdd022e7c56","observation_id":"b8a47d36-4e2f-4a0e-81a7-0d9ace4546d2","resolution":{"observed_at":"2026-08-10T16:55:56.641049Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-319-54660-5_41","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-13T11:03:48.094449Z","title":"On applicability of big data analytics in the closed -loop product lifecycle: Integration of crisp-dm standard","venue":"IFIP advances in information and communication technology","work_id":"4e5e77ec-c61a-44c1-b882-6985d2616a07","year":2016},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.778759Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:c7b0c6916c3aeb6891e687756f6449d0da59eabf715347b07014d7b0ccafba78","observation_id":"48fc2c06-55b3-4ef8-90ed-9ae43d74cac3","resolution":{"observed_at":"2026-08-10T16:55:55.414513Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.789848Z","title":"Addressing big data issues in scientiﬁc data infrastructure","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.789848Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:c9c1c0837b9577ca2371973ccb2209784a6f9388da391bbd96a08a64732ee643","observation_id":"9e4dd6dc-f515-4f4a-9a1d-98078d5d14f8","resolution":{"observed_at":"2026-08-10T16:55:54.789848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10489-021-02550-9","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.391213Z","title":"A comprehensive survey on feature selection in the various ﬁelds of machine learning","venue":null,"work_id":"f78224cd-bd15-413e-ac7e-0c5c18791563","year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.795552Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:bfee3b15f98d33fa9a4fb5d7f28a58e9903c72696c9c86197ade00a945af90d4","observation_id":"f9a857bf-f9e6-4ae7-a6c7-6aff1e272de8","resolution":{"observed_at":"2026-08-10T16:55:55.397271Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.523207Z","title":"Feature dimensionality reduction: A review","venue":null,"work_id":"fbfd2714-d095-4584-9af2-9a7066ee667b","year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.799780Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:f81b0730eeaba53151fb99dbce1aec2ad2245d1b17e7819ec521f7d3d1a05616","observation_id":"a9d7a30d-1e33-4de1-8c81-db98d5ac3b31","resolution":{"observed_at":"2026-08-10T16:55:58.527486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s11136-017-1599-0","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.355568Z","title":"Use of large-scale hrqol datasets to generate individualised predictions and inform patients about the likely beneﬁt of surgery","venue":null,"work_id":"549c3197-5549-4db4-a541-cf3333928057","year":2017},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.810209Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:531989d5ee89ce6e1425506ebdb6613e4471879698c11507766e26a51e7f23ac","observation_id":"279b7db0-06ab-43c1-9539-e23ce34b25de","resolution":{"observed_at":"2026-08-10T16:55:55.360372Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.jestch.2021.06.001","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.338906Z","title":"A review of industrial big data for decision making in intelligent manufacturing","venue":null,"work_id":"c574a8ad-e38f-4cd4-9e07-35793bbfabc4","year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.814762Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:323d4a378506ca6fab165bba08d776825f8dde1d370b991906545b0b9e14e255","observation_id":"7c2721c1-764e-4e31-acd5-5dfd72d11fd7","resolution":{"observed_at":"2026-08-10T16:55:55.345566Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.819953Z","title":"Combining structured and unstructured data for predictive models: A deep learning approach","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.819953Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:dd8e9052157c6b54a2c49d206d86a0439930f0acaf51f747615c1da72656c838","observation_id":"66a79c5b-4731-4d77-b480-2d31ba0ab9e8","resolution":{"observed_at":"2026-08-10T16:55:54.819953Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.procir.2021.11.164","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.303390Z","title":"Knowledge discovery in heterogeneous and unstructured data of industry 4.0 systems: Challenges and approaches","venue":null,"work_id":"c9c52ba4-217b-4661-b2ca-ced48f6cbefb","year":2021},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.825542Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:d2a2d0715f8cfb3bf063a431384dbc31cf5e80bafda359e44d4881ea43face89","observation_id":"1091a2f4-f131-4e17-b99d-ab3a6f3fe0b1","resolution":{"observed_at":"2026-08-10T16:55:55.311175Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.petlm.2018.11.001","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.288628Z","title":"Big data analytics in oil and gas industry: An emerging trend","venue":null,"work_id":"f5b64002-6c52-443d-8655-9d091f4f44d0","year":2020},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.830540Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:40b875112f5e96674acdb1f6677156a0633e1d4839ef5bd5ce69b5057ab3d20e","observation_id":"6518328d-b154-4dd6-83f4-296f8ce5f1b7","resolution":{"observed_at":"2026-08-10T16:55:55.293413Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[{"edge_observation":{"observed_at":"2026-08-10T18:17:58.316949+00:00","source":"paper_reference_links","state":"open"},"event_date":"2020-12-06","event_type":"correction","notice_doi":"10.1016/j.petlm.2020.12.003","provenance":{"observed_at":"2026-07-11T03:16:36.88513+00:00","source":"crossref","source_record_id":"10.1016/j.petlm.2020.12.003->10.1016/j.petlm.2018.11.001:correction"}}],"reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.tree.2019.08.006","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.270087Z","title":"Data integration for large - scale models of species distributions","venue":null,"work_id":"052ce1c2-3fc9-4415-8a56-f00b489c513c","year":2020},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.838235Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:ba7edbd05c3f7f98f517af4dc72e8b8eb940efa081d4ea0828d4b717074a49f6","observation_id":"9576b6ce-e699-462f-bac0-604fb0c68055","resolution":{"observed_at":"2026-08-10T16:55:55.274371Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s40537-021-00553-4","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.255486Z","title":"The use of big data analytics in healthcare","venue":null,"work_id":"729afbfb-3837-4172-bdd0-d04217253f0d","year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.844352Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:eda519a0d0899c2deacaac42a2d11527d52ce309ccd23c923a248268611ac290","observation_id":"9efd77bd-7e9d-46c7-9fc9-c2e8cdce6f92","resolution":{"observed_at":"2026-08-10T16:55:55.260040Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.851121Z","title":"Cost -eﬀective bad synchrophasor data detection based on unsupervised time -series data analytic","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.851121Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:0bc178a055b928ee128a19336bcb0546f7317d65f54080a0786d5f18f7d19f58","observation_id":"0af032a0-439c-4215-a229-eff3b83487cf","resolution":{"observed_at":"2026-08-10T16:55:54.851121Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.33154","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:56.360450Z","title":"Crossfun: Multi-v i e w j o i n t c r o s s f u s i o n n e t w o r k f o r ti m e s e r i e s anomaly detection","venue":null,"work_id":"155687b7-801d-442e-9408-e641b759276b","year":2023},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.856589Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:f3b8264f951fc98a60f88a3f8787cd7e1042c4ac4735d0bea3d6841563bb520e","observation_id":"42d917c1-d854-4350-8e5d-f373c376b230","resolution":{"observed_at":"2026-08-10T16:55:56.371659Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.jmsy.2019.11.004","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.235773Z","title":"Big data and stream processing platforms for industry 4.0 requirements mapping for a predictive maintenance use case","venue":null,"work_id":"be70ab8a-04b1-4a91-8e5a-44c56dd63b71","year":2020},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.860852Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:37ea9c7e8f8e303054a7dafb86f9bbc9d85023a50a0eb17218a6af00d09c89dd","observation_id":"c9a04fd8-d4d9-4a32-baf7-48ccf20f2368","resolution":{"observed_at":"2026-08-10T16:55:55.242028Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.868908Z","title":"Sice: An improved missing data imputation technique","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.868908Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:ccbe1c9357eb3546a2e725653169ac80b24188e5779b2e9a9658920b2395461f","observation_id":"0dc88242-a8e0-4b1d-be08-d9ef3f65663c","resolution":{"observed_at":"2026-08-10T16:55:54.868908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.872756Z","title":"Generative adversarial networks for imputing missing data for big data clinical research","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.872756Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:e47293f896baff591307a788b57f3943bf91b264f2e95b8a58117ebc7ce10074","observation_id":"3db868e6-7c7c-4e76-b2ee-d1584984c515","resolution":{"observed_at":"2026-08-10T16:55:54.872756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.509657Z","title":"Statistical analysis with missing data: John Wiley & Sons; 2019","venue":null,"work_id":"fda0b172-cd6e-4620-bbe0-6f882fb3f2bc","year":2019},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.876954Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:46c3e689c5fd33a0b9a65a3a3164c3b0cff9beb8ce4e305306e6066638afb89e","observation_id":"6a746c69-f605-4d4a-91d6-f75e4083c1e8","resolution":{"observed_at":"2026-08-10T16:55:58.513802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.11998","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:56.211620Z","title":"Load image inpainting: An improved u-net based load missing data recovery method","venue":null,"work_id":"6cf1e064-659b-408c-a16c-a6a2e3ae240e","year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.884755Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:7d9699c5a6d4fd501feac77c410810644028b147f8b91da2a10ad90a520c57f3","observation_id":"29400612-2697-47f4-9404-48d642ec32ad","resolution":{"observed_at":"2026-08-10T16:55:56.231983Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:58.497109Z","title":"Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies: MIT press; 2020","venue":null,"work_id":"91efd511-c36f-44ea-8222-74e3a4ae991d","year":2020},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.894149Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:4b566f402977159d1ff59cf592e8100c4946748510a5522d467292d0a8641772","observation_id":"5bede8a0-f1b8-4627-8f3b-8d500cf8dd03","resolution":{"observed_at":"2026-08-10T16:55:58.501826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.898458Z","title":"Characteristic-based clustering for time series data","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.898458Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:483376dba9069a2fcddca1b85003272c3b9143e28124cece600ec5a0b6587946","observation_id":"a9db05bb-ec67-4aad-92aa-1690c7f4e11d","resolution":{"observed_at":"2026-08-10T16:55:54.898458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.903827Z","title":"Gratis: Generating time series with diverse and controllable characteristics","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.903827Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:bcd1751aa3818b66b1f1a64ca3b2536a9c9f8ca0065b34317c03d2152ad4b5f7","observation_id":"376167e3-54aa-47d1-b25e-73fd0b3c61ce","resolution":{"observed_at":"2026-08-10T16:55:54.903827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.910546Z","title":"A review on outlier/anomaly detection in time series data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.910546Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:fafede8712f002c95cd81cc48737aac31c7fc23d5a964c2892fbe71ae6fe6dd1","observation_id":"58a02469-623d-485c-8173-437c03bf7baa","resolution":{"observed_at":"2026-08-10T16:55:54.910546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.916115Z","title":"Anomaly detection in time series: A comprehensive evaluation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.916115Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:c203977b5f3796731336d423682194076a4419459942db50b6ec4390ac4b3024","observation_id":"9701ac3f-fa7c-43a4-b043-ec19f42b7b84","resolution":{"observed_at":"2026-08-10T16:55:54.916115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1111/add.14643","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.154277Z","title":"Understanding and using time series analyses in addiction research","venue":null,"work_id":"9f9ce4f6-8c27-4671-beaa-abac68852e20","year":2019},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.922433Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:9e69f841b5e7d0ec6a8ece0084f99c945d83b42ac4a332c924515abaf64b74a8","observation_id":"16c0930f-48b5-4dad-ab67-914fe8775209","resolution":{"observed_at":"2026-08-10T16:55:55.159379Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2018.15506","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.970673Z","title":"Framework and modelling of inclusive manufacturing system","venue":null,"work_id":"ace3da8e-b23d-451e-be8e-69a19ac04e1b","year":2019},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.927913Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:f6c1b03e2fcecfdee2bf50cda4481fe316a44cbbddfcf7f0ca32f8c24e95545c","observation_id":"e33fbf94-b924-41c2-80c3-ea36db646d56","resolution":{"observed_at":"2026-08-10T16:55:55.981167Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1109/3pgcic.2014.136","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.135721Z","title":"Challenges and trends of big data analytics","venue":null,"work_id":"ff10cf48-2441-43f1-bee6-afd1d27a0e8f","year":2014},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.932741Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:91bb7e0b303a5c3d6cbff1b2323a49133b22b1164de9662f5626b850c8117c7b","observation_id":"43758453-6fe3-40fd-aa35-093224fc3c3b","resolution":{"observed_at":"2026-08-10T16:55:55.142489Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.936363Z","title":"Initiating predictive maintenance for a conveyor motor in a bottling plant using industry 4.0 concepts","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.936363Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:47f08efdbe61397625011cb04d20acf4872f5f49482b6ee81d7ee1c548d45ea8","observation_id":"9be3a009-0a9d-40ae-896d-b67a101bd523","resolution":{"observed_at":"2026-08-10T16:55:54.936363Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s40436-017-0203-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.090256Z","title":"Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario","venue":null,"work_id":"8538d810-0d02-4928-bbef-3106fe757710","year":2017},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.941690Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:c678741411e7fd7227443b0b7e1f5c60d6241901154cbe83680c6e7b0451af2c","observation_id":"3c0bdb23-f5d4-4601-8fd4-88e0ae168354","resolution":{"observed_at":"2026-08-10T16:55:55.097126Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.01548","last_updated":"2021-01-05T05:53:21Z","snapshot_observed_at":"2026-08-17T01:33:15.161283Z","submitted_at":"2020-07-03T08:22:42Z","title":"Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding","version":2},"cited_work":{"arxiv_id":"2007.01548","doi":null,"metadata_source":"pith","pith_arxiv_id":"2007.01548","snapshot_observed_at":"2026-08-10T16:55:55.865891Z","title":"Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding","venue":"cs.CV","work_id":"c860db8d-c5d6-43f5-aeac-516c5c136c5d","year":2020},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.947600Z"},"links":{"cited_paper":"/paper/2007.01548","citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:0784efb163aa247555271aebfcd7c5eea911cd185ebae3e390fb559e19b137db","observation_id":"e0213c40-b6a4-4a42-a372-51b38228dd50","resolution":{"observed_at":"2026-08-10T16:55:55.871815Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.ins.2020.11.035","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.065381Z","title":"Missing value imputation in multivariate time series with end-to-end generative adversarial networks","venue":null,"work_id":"45943e53-2ee5-41c6-a0d0-a48276db26fe","year":2021},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.954027Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:caa1bcc84e69cd67763469fbf2f0543c7a58b74fe2c2aa0be731177c0ae091b2","observation_id":"e3c97445-4b94-4942-aa48-21226223ca34","resolution":{"observed_at":"2026-08-10T16:55:55.072023Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.959483Z","title":"A novel hybrid feature importance and feature interaction detection framework for predictive optimization in industry 4.0 applications","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.959483Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:b1db93d718a4662faf21f1c2ffdfb8a012ca9efbdf8226db896ad05082973ef7","observation_id":"83f29b1d-a59e-43b6-975f-cd64779de914","resolution":{"observed_at":"2026-08-10T16:55:54.959483Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/sym15050982","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.040337Z","title":"A data-driven two-phase multi-split causal ensemble model for time series","venue":null,"work_id":"aa850bee-06d4-4b0b-bccb-5d0bad8d3802","year":2023},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.965854Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:a835b89befb8fe79353a3708422ccf2adc416d419b807e4268b2e6db4c1ce488","observation_id":"3cc74ea0-0346-4dec-861c-11644ef2383a","resolution":{"observed_at":"2026-08-10T16:55:55.051977Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:54.969995Z","title":"Statsmodels: Econometric and statistical modeling with python","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.969995Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:f6ddc90dbe8235762af598c320cee43ecb3bd38e1cf968f721bcae61cb15b4ed","observation_id":"1a129c03-78c9-41f3-ba54-f19944f96055","resolution":{"observed_at":"2026-08-10T16:55:54.969995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s40747-021-00637-x","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:55:55.374125Z","title":null,"venue":null,"work_id":"d4d123d6-77ab-44f7-9f6b-fd1979d2368d","year":null},"citing_paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-10T16:55:54.803743Z"},"links":{"citing_paper":"/paper/2501.12720"},"observation_digest":"sha256:6119ab02a0939bebfb48bc2684242bd2b12714bafc9c3c03a65cd4e9d5ce045c","observation_id":"11aab017-3013-4d33-a11f-8e3ffe07ea94","resolution":{"observed_at":"2026-08-10T16:55:55.380705Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.12720","last_updated":"2025-01-22T08:49:44Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-18T16:47:00.361178Z","submitted_at":"2025-01-22T08:49:44Z","title":"A systematic data characteristic understanding framework towards physical-sensor big data challenges"},"reference_resolution":{"displayed":78,"state_counts":{"malformed_identifier":6,"metadata_mismatch":17,"parse_uncertain":0,"unresolved":18,"verified_exact":28,"verified_fuzzy":9},"total_outbound_references":78},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 0 inbound Pith citation observations for arXiv:2501.12720."}