{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:VO2DWIATQBP7I6OIOYEXFP4N72","short_pith_number":"pith:VO2DWIAT","schema_version":"1.0","canonical_sha256":"abb43b2013805ff479c8760972bf8dfeb78a4c3e644a23ce2cf2f1f72f235046","source":{"kind":"arxiv","id":"1106.5979","version":1},"attestation_state":"computed","paper":{"title":"Probabilistic Voronoi Diagrams for Probabilistic Moving Nearest Neighbor Queries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Egemen Tanin, Mohammed Eunus Ali, Ramamohanarao Kotagiri, Rui Zhang","submitted_at":"2011-06-29T15:49:36Z","abstract_excerpt":"A large spectrum of applications such as location based services and environmental monitoring demand efficient query processing on uncertain databases. In this paper, we propose the probabilistic Voronoi diagram (PVD) for processing moving nearest neighbor queries on uncertain data, namely the probabilistic moving nearest neighbor (PMNN) queries. A PMNN query finds the most probable nearest neighbor of a moving query point continuously. To process PMNN queries efficiently, we provide two techniques: a pre-computation approach and an incremental approach. In the pre-computation approach, we dev"},"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":"1106.5979","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2011-06-29T15:49:36Z","cross_cats_sorted":[],"title_canon_sha256":"462400a3cb20776df99af091fe74894c83398d2058c391a381a70ac50266856a","abstract_canon_sha256":"5d62f0ac09c7db548440e9422147e837fbe67b4f96401d8218dabd8503097169"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:19:04.730726Z","signature_b64":"i7qC3jjtljEa9xoOMVfJHefQPetDiKVmnpJp4PIecq0feJtUp95zV7NX/p/ykjAi/6nE94AdS6Gx9gNMZNRwAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"abb43b2013805ff479c8760972bf8dfeb78a4c3e644a23ce2cf2f1f72f235046","last_reissued_at":"2026-05-18T04:19:04.730071Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:19:04.730071Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic Voronoi Diagrams for Probabilistic Moving Nearest Neighbor Queries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Egemen Tanin, Mohammed Eunus Ali, Ramamohanarao Kotagiri, Rui Zhang","submitted_at":"2011-06-29T15:49:36Z","abstract_excerpt":"A large spectrum of applications such as location based services and environmental monitoring demand efficient query processing on uncertain databases. In this paper, we propose the probabilistic Voronoi diagram (PVD) for processing moving nearest neighbor queries on uncertain data, namely the probabilistic moving nearest neighbor (PMNN) queries. A PMNN query finds the most probable nearest neighbor of a moving query point continuously. To process PMNN queries efficiently, we provide two techniques: a pre-computation approach and an incremental approach. In the pre-computation approach, we dev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1106.5979","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":""},"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":"1106.5979","created_at":"2026-05-18T04:19:04.730181+00:00"},{"alias_kind":"arxiv_version","alias_value":"1106.5979v1","created_at":"2026-05-18T04:19:04.730181+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1106.5979","created_at":"2026-05-18T04:19:04.730181+00:00"},{"alias_kind":"pith_short_12","alias_value":"VO2DWIATQBP7","created_at":"2026-05-18T12:26:44.992195+00:00"},{"alias_kind":"pith_short_16","alias_value":"VO2DWIATQBP7I6OI","created_at":"2026-05-18T12:26:44.992195+00:00"},{"alias_kind":"pith_short_8","alias_value":"VO2DWIAT","created_at":"2026-05-18T12:26:44.992195+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/VO2DWIATQBP7I6OIOYEXFP4N72","json":"https://pith.science/pith/VO2DWIATQBP7I6OIOYEXFP4N72.json","graph_json":"https://pith.science/api/pith-number/VO2DWIATQBP7I6OIOYEXFP4N72/graph.json","events_json":"https://pith.science/api/pith-number/VO2DWIATQBP7I6OIOYEXFP4N72/events.json","paper":"https://pith.science/paper/VO2DWIAT"},"agent_actions":{"view_html":"https://pith.science/pith/VO2DWIATQBP7I6OIOYEXFP4N72","download_json":"https://pith.science/pith/VO2DWIATQBP7I6OIOYEXFP4N72.json","view_paper":"https://pith.science/paper/VO2DWIAT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1106.5979&json=true","fetch_graph":"https://pith.science/api/pith-number/VO2DWIATQBP7I6OIOYEXFP4N72/graph.json","fetch_events":"https://pith.science/api/pith-number/VO2DWIATQBP7I6OIOYEXFP4N72/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VO2DWIATQBP7I6OIOYEXFP4N72/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VO2DWIATQBP7I6OIOYEXFP4N72/action/storage_attestation","attest_author":"https://pith.science/pith/VO2DWIATQBP7I6OIOYEXFP4N72/action/author_attestation","sign_citation":"https://pith.science/pith/VO2DWIATQBP7I6OIOYEXFP4N72/action/citation_signature","submit_replication":"https://pith.science/pith/VO2DWIATQBP7I6OIOYEXFP4N72/action/replication_record"}},"created_at":"2026-05-18T04:19:04.730181+00:00","updated_at":"2026-05-18T04:19:04.730181+00:00"}