{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:4DZUSXT7BHGIFJ5JUHTS3773Z2","short_pith_number":"pith:4DZUSXT7","schema_version":"1.0","canonical_sha256":"e0f3495e7f09cc82a7a9a1e72dfffbcea821e15eb94dd94946049bddcb4135ed","source":{"kind":"arxiv","id":"2111.15020","version":1},"attestation_state":"computed","paper":{"title":"US-Rule: Discovering Utility-driven Sequential Rules","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DB","authors_text":"Gengsen Huang, Jian Weng, Philip S. Yu, Wensheng Gan","submitted_at":"2021-11-29T23:38:28Z","abstract_excerpt":"Utility-driven mining is an important task in data science and has many applications in real life. High utility sequential pattern mining (HUSPM) is one kind of utility-driven mining. HUSPM aims to discover all sequential patterns with high utility. However, the existing algorithms of HUSPM can not provide an accurate probability to deal with some scenarios for prediction or recommendation. High-utility sequential rule mining (HUSRM) was proposed to discover all sequential rules with high utility and high confidence. There is only one algorithm proposed for HUSRM, which is not enough efficient"},"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":"2111.15020","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2021-11-29T23:38:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7888a9d067bfe291f808ed527dd1cd68f39b8c2eb54c160472011da05faae799","abstract_canon_sha256":"31eb979cd893498408237423052486b2cbd8db766916d1fc3bf1fdae1d8ba7db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:36:21.836625Z","signature_b64":"uAujCtzP4ZwGYpj51NUrmwD59FTHDODovdbnK8IwYxzDg5whyrTnWjMViDch0HNLpJ/bPQOaCmQwjb+SfT++CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0f3495e7f09cc82a7a9a1e72dfffbcea821e15eb94dd94946049bddcb4135ed","last_reissued_at":"2026-07-05T03:36:21.836152Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:36:21.836152Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"US-Rule: Discovering Utility-driven Sequential Rules","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DB","authors_text":"Gengsen Huang, Jian Weng, Philip S. Yu, Wensheng Gan","submitted_at":"2021-11-29T23:38:28Z","abstract_excerpt":"Utility-driven mining is an important task in data science and has many applications in real life. High utility sequential pattern mining (HUSPM) is one kind of utility-driven mining. HUSPM aims to discover all sequential patterns with high utility. However, the existing algorithms of HUSPM can not provide an accurate probability to deal with some scenarios for prediction or recommendation. High-utility sequential rule mining (HUSRM) was proposed to discover all sequential rules with high utility and high confidence. There is only one algorithm proposed for HUSRM, which is not enough efficient"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.15020","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/2111.15020/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":"2111.15020","created_at":"2026-07-05T03:36:21.836208+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.15020v1","created_at":"2026-07-05T03:36:21.836208+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.15020","created_at":"2026-07-05T03:36:21.836208+00:00"},{"alias_kind":"pith_short_12","alias_value":"4DZUSXT7BHGI","created_at":"2026-07-05T03:36:21.836208+00:00"},{"alias_kind":"pith_short_16","alias_value":"4DZUSXT7BHGIFJ5J","created_at":"2026-07-05T03:36:21.836208+00:00"},{"alias_kind":"pith_short_8","alias_value":"4DZUSXT7","created_at":"2026-07-05T03:36:21.836208+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/4DZUSXT7BHGIFJ5JUHTS3773Z2","json":"https://pith.science/pith/4DZUSXT7BHGIFJ5JUHTS3773Z2.json","graph_json":"https://pith.science/api/pith-number/4DZUSXT7BHGIFJ5JUHTS3773Z2/graph.json","events_json":"https://pith.science/api/pith-number/4DZUSXT7BHGIFJ5JUHTS3773Z2/events.json","paper":"https://pith.science/paper/4DZUSXT7"},"agent_actions":{"view_html":"https://pith.science/pith/4DZUSXT7BHGIFJ5JUHTS3773Z2","download_json":"https://pith.science/pith/4DZUSXT7BHGIFJ5JUHTS3773Z2.json","view_paper":"https://pith.science/paper/4DZUSXT7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.15020&json=true","fetch_graph":"https://pith.science/api/pith-number/4DZUSXT7BHGIFJ5JUHTS3773Z2/graph.json","fetch_events":"https://pith.science/api/pith-number/4DZUSXT7BHGIFJ5JUHTS3773Z2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4DZUSXT7BHGIFJ5JUHTS3773Z2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4DZUSXT7BHGIFJ5JUHTS3773Z2/action/storage_attestation","attest_author":"https://pith.science/pith/4DZUSXT7BHGIFJ5JUHTS3773Z2/action/author_attestation","sign_citation":"https://pith.science/pith/4DZUSXT7BHGIFJ5JUHTS3773Z2/action/citation_signature","submit_replication":"https://pith.science/pith/4DZUSXT7BHGIFJ5JUHTS3773Z2/action/replication_record"}},"created_at":"2026-07-05T03:36:21.836208+00:00","updated_at":"2026-07-05T03:36:21.836208+00:00"}