{"as_of":"2026-08-22T22:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ff4417c85c48f7be6447feae61eaf9f7b408067d560b36b76233451d3aa2693a","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T21:55:49.404361Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2411.08203/citation-record","integrity":"/paper/2411.08203/integrity","json":"/paper/2411.08203/citation-record.json","paper":"/paper/2411.08203"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"reports/5337600","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T21:55:49.864228Z","title":"Global big data & analytics market by compo- nent,","venue":null,"work_id":"2724868f-aaf5-4883-9d79-0850fad7f798","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.286189Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:8732d4453cb52f9b66102c23a34a7a15fb9ef414973f6c8dd93ab0796d59e5f8","observation_id":"43dcd1ab-63c4-4e9f-8bda-4051f87a5161","resolution":{"observed_at":"2026-08-12T21:55:49.873225Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:50.155108Z","title":"“everyone wants to do the model work, not the data work","venue":null,"work_id":"ec31f7aa-99c7-4121-99e9-5156c4fd5a4c","year":2021},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.292482Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:ec1e64a4701fc78d204bea7cf43e7b62a405c82188977ed3e39e04350280c9f4","observation_id":"d13757e5-5709-4396-bfa3-42b60d5c4d2c","resolution":{"observed_at":"2026-08-12T21:55:50.161671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:50.134315Z","title":"Lakehouse: A new generation of open platforms that unify data warehousing and advanced analytics,","venue":null,"work_id":"3369728c-b190-46c7-b7ad-299b4a356410","year":2021},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.303361Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:4b2d0cb30963f8cfa3a09727b8cdd2ffbb8d52e5116a5541d5e2000419bb9224","observation_id":"c6941102-4624-4c18-84ed-a92f7c35fd45","resolution":{"observed_at":"2026-08-12T21:55:50.139921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:49.308779Z","title":"Towards scalable dataframe systems,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.308779Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:a8100eb8d47b4248c667b2dcae19b80ebd02494054511ec8e76bfe88bbabf587","observation_id":"5af063d1-43fc-4ba7-ae81-28d14dec90cd","resolution":{"observed_at":"2026-08-12T21:55:49.308779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.09125","last_updated":"2022-09-16T16:59:36Z","snapshot_observed_at":"2026-08-22T07:11:24.010107Z","submitted_at":"2022-09-16T16:59:36Z","title":"Operationalizing Machine Learning: An Interview Study","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.09125","snapshot_observed_at":"2026-08-12T21:55:49.315368Z","title":"Operationalizing machine learning: An interview study,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.315368Z"},"links":{"cited_paper":"/paper/2209.09125","citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:57455c3d30f730ccfc550590983fd7061fc30388994513ebfdb72444e59f635e","observation_id":"99f7b894-7191-4c83-a9b2-e75e72916777","resolution":{"observed_at":"2026-08-12T21:55:49.315368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.11761","last_updated":"2023-03-21T11:28:09Z","snapshot_observed_at":"2026-08-19T13:37:54.017725Z","submitted_at":"2023-03-21T11:28:09Z","title":"Reasonable Scale Machine Learning with Open-Source Metaflow","version":1},"cited_work":{"arxiv_id":"2303.11761","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.11761","snapshot_observed_at":"2026-08-12T21:55:49.637037Z","title":"Reasonable Scale Machine Learning with Open-Source Metaflow","venue":"cs.LG","work_id":"421dc544-a020-4f4b-9f32-b380b18010f9","year":2023},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.321287Z"},"links":{"cited_paper":"/paper/2303.11761","citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:358328b53979fa7156cdc8d5c29b14680500e1e3e909cefcb077816b149f3331","observation_id":"39d63e92-4d4f-42df-bda7-47ef18ad6118","resolution":{"observed_at":"2026-08-12T21:55:49.642527Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:50.115521Z","title":"Global data pipeline tools market by compo- nent,","venue":null,"work_id":"926243d8-f6b6-4a4b-aa1f-5aedcd1a0b76","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.327219Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:dd80debad6d6b62985f5c491879922ff5f08d082f3cd0ed0f13054a329045bde","observation_id":"b2d9fd59-6311-4c76-8668-978247122854","resolution":{"observed_at":"2026-08-12T21:55:50.122226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:50.097443Z","title":"Airflow,","venue":null,"work_id":"0a2e9aa7-a1fc-4a80-892c-4c3b7a9c130a","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.331979Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:0910e8ac6706482a24c75e0bbf99cf091f0fd89e9ceeb2ed1da2bd218fc09744","observation_id":"25b004ff-52a3-4fb4-af9d-0949471b06d8","resolution":{"observed_at":"2026-08-12T21:55:50.102662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:50.078354Z","title":null,"venue":null,"work_id":"46d1c976-2274-43c1-88d3-0319b08d0a20","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.336963Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:00d67874645aa67e555bbbb47c050c0a612c9bba90a92cc17b114a13506be88d","observation_id":"0a149d03-895d-48d5-95b2-f21c4b762f53","resolution":{"observed_at":"2026-08-12T21:55:50.083888Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:50.058976Z","title":"Parquet,","venue":null,"work_id":"7a4bc090-be5e-4f5f-b08e-d39f55213726","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.342151Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:5b0257ec1d3d485dbcf3601291313296757b46facb8c80bf9e5dda75fd5c7a6c","observation_id":"701cf59f-35e2-4788-8d57-a62a612cef03","resolution":{"observed_at":"2026-08-12T21:55:50.064458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:50.039006Z","title":"Iceberg,","venue":null,"work_id":"5aef520e-ea14-47d2-8753-4ccc2afd67cc","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.347595Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:27ee8f3c289bcc082fd06bce2f637c0758dd413c72f4ae60e74f3022d4000f16","observation_id":"a002e4d8-0344-489b-9d8c-aa9911f012e4","resolution":{"observed_at":"2026-08-12T21:55:50.046575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:49.352708Z","title":"Reproducible data science over data lakes: replayable data pipelines with bauplan and nessie,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.352708Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:a05dd8bf8ed4304014458b9eb5d1ba4675f9d8d10b62dbcf9df5449406bfbaae","observation_id":"b4a2cd2b-bb9c-47e2-9b95-62d2f3b4bcf2","resolution":{"observed_at":"2026-08-12T21:55:49.352708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05368","last_updated":"2023-08-10T06:24:25Z","snapshot_observed_at":"2026-08-19T13:34:06.665323Z","submitted_at":"2023-08-10T06:24:25Z","title":"Building a serverless Data Lakehouse from spare parts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.05368","snapshot_observed_at":"2026-08-12T21:55:49.358078Z","title":"Building a serverless data lakehouse from spare parts,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.358078Z"},"links":{"cited_paper":"/paper/2308.05368","citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:a73e10d10d296b4642a8ac337b53bb5f82b1836a272c91b5ae8089ac3526343a","observation_id":"b7666284-ed82-4581-90f4-229e524d4c6e","resolution":{"observed_at":"2026-08-12T21:55:49.358078Z","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":"2026-08-12T21:55:50.019321Z","title":"Build end-to-end machine learning workflows with amazon sagemaker and apache airflow,","venue":null,"work_id":"57d3171f-cf5c-41a3-b4e5-1ecad9ff433a","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.364077Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:130480efaa56b97982654421d2ea271cd7fb1d51751f4863724710077feb73cf","observation_id":"de691d0e-2857-4f89-bbf4-7e710c520fe2","resolution":{"observed_at":"2026-08-12T21:55:50.025511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:49.991207Z","title":null,"venue":null,"work_id":"f1272139-f6ed-450c-8a2c-7bba827c8d55","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.369145Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:e82ca3881028af04cd68f99970dd4396529ce266cab9b869cf33b61d26d9fcb0","observation_id":"f59e4f01-7817-4bba-ad13-f1a2874ff2df","resolution":{"observed_at":"2026-08-12T21:55:50.000783Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:49.373994Z","title":"Predicate caching: Query-driven secondary indexing for cloud data warehouses,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.373994Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:9d0b36972d34fdcab3084ccc40e365bb6f7dc6dc5c13726f2420032148008885","observation_id":"15551153-ab53-4f39-847c-da00311d0db1","resolution":{"observed_at":"2026-08-12T21:55:49.373994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.10311","last_updated":"2018-05-17T22:16:31Z","snapshot_observed_at":"2026-08-17T23:29:30.818668Z","submitted_at":"2018-03-27T20:38:05Z","title":"How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature","version":2},"cited_work":{"arxiv_id":"1803.10311","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.10311","snapshot_observed_at":"2026-08-12T21:55:49.443905Z","title":"How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature","venue":"cs.LG","work_id":"585e78d4-5591-43ef-b4ef-2c5ad00788b8","year":2018},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.378829Z"},"links":{"cited_paper":"/paper/1803.10311","citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:ab07bae52afa5bda14c6993dbfc2456472b66f2510acea8a731754977a89a7ab","observation_id":"78b2363e-db65-44ff-8a89-286e2655a6b7","resolution":{"observed_at":"2026-08-12T21:55:49.451399Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:49.969146Z","title":"Why tpc is not enough: An analysis of the amazon redshift fleet,","venue":null,"work_id":"9740a586-406a-45f7-bfea-0b56a037e9be","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.383908Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:1dee0b2928d4594c9808fb1d9a77c9c663589197f5e78711917706f6788c7005","observation_id":"41f5b660-1338-4172-adf3-4a52579c9cf0","resolution":{"observed_at":"2026-08-12T21:55:49.976739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:49.951185Z","title":"dbt-core,","venue":null,"work_id":"4ddc2a00-2bd8-4340-87ef-6724a283d7a0","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.389152Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:e66093a912c298e456aeaca5d05d6e8620ebdefbc5ee82d144ca506690688ea9","observation_id":"fa58435e-7702-4279-b26a-3fa8793932c7","resolution":{"observed_at":"2026-08-12T21:55:49.956307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:49.933039Z","title":"Semantic data caching and replacement,","venue":null,"work_id":"ac3dcc5b-5e86-4080-96bd-a6a74ef6cf44","year":1996},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.394154Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:27216655b052e175814a42c892ed6c1aaf0af67204a583cc86f030730f6085e3","observation_id":"bb82439b-fab4-4354-8986-a840bb54ef52","resolution":{"observed_at":"2026-08-12T21:55:49.938813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:49.905267Z","title":"Data caching for Enterprise-Grade Petabyte-Scale OLAP,","venue":null,"work_id":"36be3448-5ec0-48d6-910f-1434dcb5e142","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.399306Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:27d4f02bb64600ab236a4134d3ae297850139064bd15dfae5599279686b68f9d","observation_id":"f5d827e0-70d3-4323-8a3c-2185347268c9","resolution":{"observed_at":"2026-08-12T21:55:49.918897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:49.886572Z","title":"Differential storage: A key building block for a duckdb-based data warehouse,","venue":null,"work_id":"49fe84fd-c08e-4411-bdbd-b8feb1cac78c","year":2024},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.404361Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:26250c7a2a952c3d664aec3d7149976b24bc2693f6f2c0837513bb5cdd18636d","observation_id":"495155e6-77a1-4ec6-b23a-d720e8463bf6","resolution":{"observed_at":"2026-08-12T21:55:49.893008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-12T21:55:49.297762Z","title":"Available: https://doi.org/10.1145/3411764.3445518","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-12T21:55:49.297762Z"},"links":{"citing_paper":"/paper/2411.08203"},"observation_digest":"sha256:7065106f634bc23a667192473291cb6b837ad3af1e2d43f2f198b94a250d0f97","observation_id":"3a8c800b-d72e-4984-aebf-730145939e59","resolution":{"observed_at":"2026-08-12T21:55:49.297762Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.08203","last_updated":"2024-11-12T21:50:03Z","latest_version":1,"primary_category":"cs.DB","snapshot_observed_at":"2026-08-19T13:34:44.530866Z","submitted_at":"2024-11-12T21:50:03Z","title":"FaaS and Furious: abstractions and differential caching for efficient data pre-processing"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":3,"verified_fuzzy":12},"total_outbound_references":23},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2411.08203."}