{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:XNFV5S27LVTV2PSDPBL5R4H3WS","short_pith_number":"pith:XNFV5S27","schema_version":"1.0","canonical_sha256":"bb4b5ecb5f5d675d3e437857d8f0fbb4bd0a18c3a49ac1d41b77c6036118624f","source":{"kind":"arxiv","id":"2011.13454","version":1},"attestation_state":"computed","paper":{"title":"TKUS: Mining Top-K High-Utility Sequential Patterns","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Chunkai Zhang, Philip S. Yu, Wensheng Gan, Zilin Du","submitted_at":"2020-11-26T19:36:20Z","abstract_excerpt":"High-utility sequential pattern mining (HUSPM) has recently emerged as a focus of intense research interest. The main task of HUSPM is to find all subsequences, within a quantitative sequential database, that have high utility with respect to a user-defined minimum utility threshold. However, it is difficult to specify the minimum utility threshold, especially when database features, which are invisible in most cases, are not understood. To handle this problem, top-k HUSPM was proposed. Up to now, only very preliminary work has been conducted to capture top-k HUSPs, and existing strategies req"},"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":"2011.13454","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2020-11-26T19:36:20Z","cross_cats_sorted":[],"title_canon_sha256":"437146faf675ca5f62c97b6d22afb2493c85f2692e1b6571e9431051545f1b23","abstract_canon_sha256":"cfd1630e5d04eb2d476b4d04685c950dd54ed67888121c3ba9ef81cfddd058a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:54:54.370456Z","signature_b64":"kzMXZ+wUgnYt+U+qvyozQnrYMNE7ckZBtGnajjRIjOEv4YdkV+TL68rNwuF478UTVxLsNlDNolr+wzrlDvFKCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb4b5ecb5f5d675d3e437857d8f0fbb4bd0a18c3a49ac1d41b77c6036118624f","last_reissued_at":"2026-07-05T01:54:54.369938Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:54:54.369938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TKUS: Mining Top-K High-Utility Sequential Patterns","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Chunkai Zhang, Philip S. Yu, Wensheng Gan, Zilin Du","submitted_at":"2020-11-26T19:36:20Z","abstract_excerpt":"High-utility sequential pattern mining (HUSPM) has recently emerged as a focus of intense research interest. The main task of HUSPM is to find all subsequences, within a quantitative sequential database, that have high utility with respect to a user-defined minimum utility threshold. However, it is difficult to specify the minimum utility threshold, especially when database features, which are invisible in most cases, are not understood. To handle this problem, top-k HUSPM was proposed. Up to now, only very preliminary work has been conducted to capture top-k HUSPs, and existing strategies req"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.13454","kind":"arxiv","version":1},"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/2011.13454/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":"2011.13454","created_at":"2026-07-05T01:54:54.370014+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.13454v1","created_at":"2026-07-05T01:54:54.370014+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.13454","created_at":"2026-07-05T01:54:54.370014+00:00"},{"alias_kind":"pith_short_12","alias_value":"XNFV5S27LVTV","created_at":"2026-07-05T01:54:54.370014+00:00"},{"alias_kind":"pith_short_16","alias_value":"XNFV5S27LVTV2PSD","created_at":"2026-07-05T01:54:54.370014+00:00"},{"alias_kind":"pith_short_8","alias_value":"XNFV5S27","created_at":"2026-07-05T01:54:54.370014+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/XNFV5S27LVTV2PSDPBL5R4H3WS","json":"https://pith.science/pith/XNFV5S27LVTV2PSDPBL5R4H3WS.json","graph_json":"https://pith.science/api/pith-number/XNFV5S27LVTV2PSDPBL5R4H3WS/graph.json","events_json":"https://pith.science/api/pith-number/XNFV5S27LVTV2PSDPBL5R4H3WS/events.json","paper":"https://pith.science/paper/XNFV5S27"},"agent_actions":{"view_html":"https://pith.science/pith/XNFV5S27LVTV2PSDPBL5R4H3WS","download_json":"https://pith.science/pith/XNFV5S27LVTV2PSDPBL5R4H3WS.json","view_paper":"https://pith.science/paper/XNFV5S27","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.13454&json=true","fetch_graph":"https://pith.science/api/pith-number/XNFV5S27LVTV2PSDPBL5R4H3WS/graph.json","fetch_events":"https://pith.science/api/pith-number/XNFV5S27LVTV2PSDPBL5R4H3WS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XNFV5S27LVTV2PSDPBL5R4H3WS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XNFV5S27LVTV2PSDPBL5R4H3WS/action/storage_attestation","attest_author":"https://pith.science/pith/XNFV5S27LVTV2PSDPBL5R4H3WS/action/author_attestation","sign_citation":"https://pith.science/pith/XNFV5S27LVTV2PSDPBL5R4H3WS/action/citation_signature","submit_replication":"https://pith.science/pith/XNFV5S27LVTV2PSDPBL5R4H3WS/action/replication_record"}},"created_at":"2026-07-05T01:54:54.370014+00:00","updated_at":"2026-07-05T01:54:54.370014+00:00"}