{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CEU2J5I4NQGL6KO7C5LJVVONKG","short_pith_number":"pith:CEU2J5I4","schema_version":"1.0","canonical_sha256":"1129a4f51c6c0cbf29df17569ad5cd5183252b221d4e7cee8aeb46bccf27d1c1","source":{"kind":"arxiv","id":"2105.03092","version":1},"attestation_state":"computed","paper":{"title":"An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SI"],"primary_cat":"cs.LG","authors_text":"Junjian Ye, Keli Zhang, Marcus Kalander, Min Zhou, Xi Zhang","submitted_at":"2021-05-07T07:41:46Z","abstract_excerpt":"Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery. In practice, accurate and self-adjustable alarm root cause analysis is a great challenge due to network complexity and vast amounts of alarms. A popular approach for failure root cause identification is to construct a graph with approximate edges, commonly based on either event co-occurrences or conditional independence tests. However, considerable expert knowledge is typically required for edge prun"},"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":"2105.03092","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-07T07:41:46Z","cross_cats_sorted":["cs.AI","cs.SI"],"title_canon_sha256":"0a9347b509897e00247da995b68549d967fbfaa554fbafc7746ac9941ea6d6a9","abstract_canon_sha256":"7f1a7739a96b13fb03bf4b6d707a643ba1a5310abbc5d572caeddcb130e91049"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:38:21.527301Z","signature_b64":"fcO8tlCR0tgc0jo2y9HterCFDKXd6lnDV7gm5/Iz0qUNil5KyILWu45QyaqPXgEtL9niFHfh8Vcwlb3YmOlrDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1129a4f51c6c0cbf29df17569ad5cd5183252b221d4e7cee8aeb46bccf27d1c1","last_reissued_at":"2026-07-05T02:38:21.526876Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:38:21.526876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SI"],"primary_cat":"cs.LG","authors_text":"Junjian Ye, Keli Zhang, Marcus Kalander, Min Zhou, Xi Zhang","submitted_at":"2021-05-07T07:41:46Z","abstract_excerpt":"Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery. In practice, accurate and self-adjustable alarm root cause analysis is a great challenge due to network complexity and vast amounts of alarms. A popular approach for failure root cause identification is to construct a graph with approximate edges, commonly based on either event co-occurrences or conditional independence tests. However, considerable expert knowledge is typically required for edge prun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.03092","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/2105.03092/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":"2105.03092","created_at":"2026-07-05T02:38:21.526935+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.03092v1","created_at":"2026-07-05T02:38:21.526935+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.03092","created_at":"2026-07-05T02:38:21.526935+00:00"},{"alias_kind":"pith_short_12","alias_value":"CEU2J5I4NQGL","created_at":"2026-07-05T02:38:21.526935+00:00"},{"alias_kind":"pith_short_16","alias_value":"CEU2J5I4NQGL6KO7","created_at":"2026-07-05T02:38:21.526935+00:00"},{"alias_kind":"pith_short_8","alias_value":"CEU2J5I4","created_at":"2026-07-05T02:38:21.526935+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.18190","citing_title":"TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CEU2J5I4NQGL6KO7C5LJVVONKG","json":"https://pith.science/pith/CEU2J5I4NQGL6KO7C5LJVVONKG.json","graph_json":"https://pith.science/api/pith-number/CEU2J5I4NQGL6KO7C5LJVVONKG/graph.json","events_json":"https://pith.science/api/pith-number/CEU2J5I4NQGL6KO7C5LJVVONKG/events.json","paper":"https://pith.science/paper/CEU2J5I4"},"agent_actions":{"view_html":"https://pith.science/pith/CEU2J5I4NQGL6KO7C5LJVVONKG","download_json":"https://pith.science/pith/CEU2J5I4NQGL6KO7C5LJVVONKG.json","view_paper":"https://pith.science/paper/CEU2J5I4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.03092&json=true","fetch_graph":"https://pith.science/api/pith-number/CEU2J5I4NQGL6KO7C5LJVVONKG/graph.json","fetch_events":"https://pith.science/api/pith-number/CEU2J5I4NQGL6KO7C5LJVVONKG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CEU2J5I4NQGL6KO7C5LJVVONKG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CEU2J5I4NQGL6KO7C5LJVVONKG/action/storage_attestation","attest_author":"https://pith.science/pith/CEU2J5I4NQGL6KO7C5LJVVONKG/action/author_attestation","sign_citation":"https://pith.science/pith/CEU2J5I4NQGL6KO7C5LJVVONKG/action/citation_signature","submit_replication":"https://pith.science/pith/CEU2J5I4NQGL6KO7C5LJVVONKG/action/replication_record"}},"created_at":"2026-07-05T02:38:21.526935+00:00","updated_at":"2026-07-05T02:38:21.526935+00:00"}