{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:FKTVPF7W74ZAKBNKHPMJETK7B2","short_pith_number":"pith:FKTVPF7W","schema_version":"1.0","canonical_sha256":"2aa75797f6ff320505aa3bd8924d5f0e84f76a92e92a5949f0ea5a9a21e6d85f","source":{"kind":"arxiv","id":"2207.08640","version":2},"attestation_state":"computed","paper":{"title":"Lightweight Automated Feature Monitoring for Data Streams","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hugo R.C. Ferreira, Jo\\~ao Conde, Jo\\~ao Tiago Ascens\\~ao, Jo\\~ao Torres, Marco O.P. Sampaio, Pedro Bizarro, Pedro Cardoso, Ricardo Moreira","submitted_at":"2022-07-18T14:38:11Z","abstract_excerpt":"Monitoring the behavior of automated real-time stream processing systems has become one of the most relevant problems in real world applications. Such systems have grown in complexity relying heavily on high dimensional input data, and data hungry Machine Learning (ML) algorithms. We propose a flexible system, Feature Monitoring (FM), that detects data drifts in such data sets, with a small and constant memory footprint and a small computational cost in streaming applications. The method is based on a multi-variate statistical test and is data driven by design (full reference distributions are"},"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":"2207.08640","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-18T14:38:11Z","cross_cats_sorted":[],"title_canon_sha256":"69b047481971e9af560cf1a5a0edb2913b7cf7767dd5a500d20676fadf47252f","abstract_canon_sha256":"be1307e53a00100bf98ff3f9c6d9aad7e58c510750a55870c4461609c68a637c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:41:47.346722Z","signature_b64":"+i3XiwIxp/60F1S39tADpS6kyi9bnRV0o3+6CNgGyBMWSUWOFu9zWK1+HA5LEJcnreGZBNZW+TAx5EJ5dqU4CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2aa75797f6ff320505aa3bd8924d5f0e84f76a92e92a5949f0ea5a9a21e6d85f","last_reissued_at":"2026-07-05T04:41:47.346310Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:41:47.346310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lightweight Automated Feature Monitoring for Data Streams","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hugo R.C. Ferreira, Jo\\~ao Conde, Jo\\~ao Tiago Ascens\\~ao, Jo\\~ao Torres, Marco O.P. Sampaio, Pedro Bizarro, Pedro Cardoso, Ricardo Moreira","submitted_at":"2022-07-18T14:38:11Z","abstract_excerpt":"Monitoring the behavior of automated real-time stream processing systems has become one of the most relevant problems in real world applications. Such systems have grown in complexity relying heavily on high dimensional input data, and data hungry Machine Learning (ML) algorithms. We propose a flexible system, Feature Monitoring (FM), that detects data drifts in such data sets, with a small and constant memory footprint and a small computational cost in streaming applications. The method is based on a multi-variate statistical test and is data driven by design (full reference distributions are"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.08640","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/2207.08640/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":"2207.08640","created_at":"2026-07-05T04:41:47.346380+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.08640v2","created_at":"2026-07-05T04:41:47.346380+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.08640","created_at":"2026-07-05T04:41:47.346380+00:00"},{"alias_kind":"pith_short_12","alias_value":"FKTVPF7W74ZA","created_at":"2026-07-05T04:41:47.346380+00:00"},{"alias_kind":"pith_short_16","alias_value":"FKTVPF7W74ZAKBNK","created_at":"2026-07-05T04:41:47.346380+00:00"},{"alias_kind":"pith_short_8","alias_value":"FKTVPF7W","created_at":"2026-07-05T04:41:47.346380+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FKTVPF7W74ZAKBNKHPMJETK7B2","json":"https://pith.science/pith/FKTVPF7W74ZAKBNKHPMJETK7B2.json","graph_json":"https://pith.science/api/pith-number/FKTVPF7W74ZAKBNKHPMJETK7B2/graph.json","events_json":"https://pith.science/api/pith-number/FKTVPF7W74ZAKBNKHPMJETK7B2/events.json","paper":"https://pith.science/paper/FKTVPF7W"},"agent_actions":{"view_html":"https://pith.science/pith/FKTVPF7W74ZAKBNKHPMJETK7B2","download_json":"https://pith.science/pith/FKTVPF7W74ZAKBNKHPMJETK7B2.json","view_paper":"https://pith.science/paper/FKTVPF7W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.08640&json=true","fetch_graph":"https://pith.science/api/pith-number/FKTVPF7W74ZAKBNKHPMJETK7B2/graph.json","fetch_events":"https://pith.science/api/pith-number/FKTVPF7W74ZAKBNKHPMJETK7B2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FKTVPF7W74ZAKBNKHPMJETK7B2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FKTVPF7W74ZAKBNKHPMJETK7B2/action/storage_attestation","attest_author":"https://pith.science/pith/FKTVPF7W74ZAKBNKHPMJETK7B2/action/author_attestation","sign_citation":"https://pith.science/pith/FKTVPF7W74ZAKBNKHPMJETK7B2/action/citation_signature","submit_replication":"https://pith.science/pith/FKTVPF7W74ZAKBNKHPMJETK7B2/action/replication_record"}},"created_at":"2026-07-05T04:41:47.346380+00:00","updated_at":"2026-07-05T04:41:47.346380+00:00"}