{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:44DTCYZSN3SHMCEEWQAPSMJPZX","short_pith_number":"pith:44DTCYZS","schema_version":"1.0","canonical_sha256":"e7073163326ee4760884b400f9312fcdecf83173b64dd7cb1a8218d489f21d3d","source":{"kind":"arxiv","id":"2405.14267","version":2},"attestation_state":"computed","paper":{"title":"A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Arian Prabowo, Flora D. Salim, Hao Xue, Imran Razzak, Matthew Amos, Sam Behrens, Xiachong Lin","submitted_at":"2024-05-23T07:45:48Z","abstract_excerpt":"The increasing demand for sustainable energy solutions has driven the integration of digitalized buildings into the power grid, leveraging Internet-of-Things (IoT) technologies to enhance energy efficiency and operational performance. Despite their potential, effectively utilizing IoT point data within deep-learning frameworks presents significant challenges, primarily due to its inherent heterogeneity. This study investigates the diverse dimensions of IoT data heterogeneity in both intra-building and inter-building contexts, examining their implications for predictive modeling. A benchmarking"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2405.14267","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-23T07:45:48Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b71601590db4476506d06f27c14a23031a535e2fefdf16917f3cd1bf99df51e1","abstract_canon_sha256":"c2ce129bb06996c0bbc3dd166fd7b0c5103d45e22ed3c120d2cfb35cab476f25"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:37:54.053106Z","signature_b64":"i3FZdPo22P1C/MQQ/U80hcIUCHjSSiQ9bwIsnWfgBBTAmRVuzHCQiWIgXH9sP5VETeiMjydoxvtrd4wBHyOXBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7073163326ee4760884b400f9312fcdecf83173b64dd7cb1a8218d489f21d3d","last_reissued_at":"2026-07-05T09:37:54.052504Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:37:54.052504Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Arian Prabowo, Flora D. Salim, Hao Xue, Imran Razzak, Matthew Amos, Sam Behrens, Xiachong Lin","submitted_at":"2024-05-23T07:45:48Z","abstract_excerpt":"The increasing demand for sustainable energy solutions has driven the integration of digitalized buildings into the power grid, leveraging Internet-of-Things (IoT) technologies to enhance energy efficiency and operational performance. Despite their potential, effectively utilizing IoT point data within deep-learning frameworks presents significant challenges, primarily due to its inherent heterogeneity. This study investigates the diverse dimensions of IoT data heterogeneity in both intra-building and inter-building contexts, examining their implications for predictive modeling. A benchmarking"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.14267","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.14267/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2405.14267","created_at":"2026-07-05T09:37:54.052580+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.14267v2","created_at":"2026-07-05T09:37:54.052580+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.14267","created_at":"2026-07-05T09:37:54.052580+00:00"},{"alias_kind":"pith_short_12","alias_value":"44DTCYZSN3SH","created_at":"2026-07-05T09:37:54.052580+00:00"},{"alias_kind":"pith_short_16","alias_value":"44DTCYZSN3SHMCEE","created_at":"2026-07-05T09:37:54.052580+00:00"},{"alias_kind":"pith_short_8","alias_value":"44DTCYZS","created_at":"2026-07-05T09:37:54.052580+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.14175","citing_title":"BiTSA: Leveraging Time Series Foundation Model for Building Energy Analytics","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/44DTCYZSN3SHMCEEWQAPSMJPZX","json":"https://pith.science/pith/44DTCYZSN3SHMCEEWQAPSMJPZX.json","graph_json":"https://pith.science/api/pith-number/44DTCYZSN3SHMCEEWQAPSMJPZX/graph.json","events_json":"https://pith.science/api/pith-number/44DTCYZSN3SHMCEEWQAPSMJPZX/events.json","paper":"https://pith.science/paper/44DTCYZS"},"agent_actions":{"view_html":"https://pith.science/pith/44DTCYZSN3SHMCEEWQAPSMJPZX","download_json":"https://pith.science/pith/44DTCYZSN3SHMCEEWQAPSMJPZX.json","view_paper":"https://pith.science/paper/44DTCYZS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.14267&json=true","fetch_graph":"https://pith.science/api/pith-number/44DTCYZSN3SHMCEEWQAPSMJPZX/graph.json","fetch_events":"https://pith.science/api/pith-number/44DTCYZSN3SHMCEEWQAPSMJPZX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/44DTCYZSN3SHMCEEWQAPSMJPZX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/44DTCYZSN3SHMCEEWQAPSMJPZX/action/storage_attestation","attest_author":"https://pith.science/pith/44DTCYZSN3SHMCEEWQAPSMJPZX/action/author_attestation","sign_citation":"https://pith.science/pith/44DTCYZSN3SHMCEEWQAPSMJPZX/action/citation_signature","submit_replication":"https://pith.science/pith/44DTCYZSN3SHMCEEWQAPSMJPZX/action/replication_record"}},"created_at":"2026-07-05T09:37:54.052580+00:00","updated_at":"2026-07-05T09:37:54.052580+00:00"}