{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:KYB4HLB2GRBG5CRMNX7EJPD62B","short_pith_number":"pith:KYB4HLB2","schema_version":"1.0","canonical_sha256":"5603c3ac3a34426e8a2c6dfe44bc7ed0486d713f695df5cce6332440e76e7da1","source":{"kind":"arxiv","id":"1902.06792","version":2},"attestation_state":"computed","paper":{"title":"Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Arnab Nandi, Mohammad Hossein Samavatian, Rajiv Ramnath, Sobhan Moosavi, Srinivasan Parthasarathy","submitted_at":"2019-02-14T03:07:21Z","abstract_excerpt":"Pattern discovery in geo-spatiotemporal data (such as traffic and weather data) is about finding patterns of collocation, co-occurrence, cascading, or cause and effect between geospatial entities. Using simplistic definitions of spatiotemporal neighborhood (a common characteristic of the existing general-purpose frameworks) is not semantically representative of geo-spatiotemporal data. We therefore introduce a new geo-spatiotemporal pattern discovery framework which defines a semantically correct definition of neighborhood; and then provides two capabilities, one to explore propagation pattern"},"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":"1902.06792","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2019-02-14T03:07:21Z","cross_cats_sorted":[],"title_canon_sha256":"aec2697f7f6935255e739c053ebf6c14f51581a07a0bb1952899e76742608681","abstract_canon_sha256":"2839e6b0847264e02c0dcbb2d71ead9a8b7088819242c8aa406e9161ba8991fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:52.025934Z","signature_b64":"2YriLFIJag4ehPaYob+HH7j6kzucBLuiTzyXEwOsdMWJXCVIcCfgsH0j7/lqCL3FYkk8NS3pOj6FE6Sxvsh5Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5603c3ac3a34426e8a2c6dfe44bc7ed0486d713f695df5cce6332440e76e7da1","last_reissued_at":"2026-05-17T23:45:52.025471Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:52.025471Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Arnab Nandi, Mohammad Hossein Samavatian, Rajiv Ramnath, Sobhan Moosavi, Srinivasan Parthasarathy","submitted_at":"2019-02-14T03:07:21Z","abstract_excerpt":"Pattern discovery in geo-spatiotemporal data (such as traffic and weather data) is about finding patterns of collocation, co-occurrence, cascading, or cause and effect between geospatial entities. Using simplistic definitions of spatiotemporal neighborhood (a common characteristic of the existing general-purpose frameworks) is not semantically representative of geo-spatiotemporal data. We therefore introduce a new geo-spatiotemporal pattern discovery framework which defines a semantically correct definition of neighborhood; and then provides two capabilities, one to explore propagation pattern"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.06792","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":""},"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":"1902.06792","created_at":"2026-05-17T23:45:52.025547+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.06792v2","created_at":"2026-05-17T23:45:52.025547+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.06792","created_at":"2026-05-17T23:45:52.025547+00:00"},{"alias_kind":"pith_short_12","alias_value":"KYB4HLB2GRBG","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"KYB4HLB2GRBG5CRM","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"KYB4HLB2","created_at":"2026-05-18T12:33:21.387695+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/KYB4HLB2GRBG5CRMNX7EJPD62B","json":"https://pith.science/pith/KYB4HLB2GRBG5CRMNX7EJPD62B.json","graph_json":"https://pith.science/api/pith-number/KYB4HLB2GRBG5CRMNX7EJPD62B/graph.json","events_json":"https://pith.science/api/pith-number/KYB4HLB2GRBG5CRMNX7EJPD62B/events.json","paper":"https://pith.science/paper/KYB4HLB2"},"agent_actions":{"view_html":"https://pith.science/pith/KYB4HLB2GRBG5CRMNX7EJPD62B","download_json":"https://pith.science/pith/KYB4HLB2GRBG5CRMNX7EJPD62B.json","view_paper":"https://pith.science/paper/KYB4HLB2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.06792&json=true","fetch_graph":"https://pith.science/api/pith-number/KYB4HLB2GRBG5CRMNX7EJPD62B/graph.json","fetch_events":"https://pith.science/api/pith-number/KYB4HLB2GRBG5CRMNX7EJPD62B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KYB4HLB2GRBG5CRMNX7EJPD62B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KYB4HLB2GRBG5CRMNX7EJPD62B/action/storage_attestation","attest_author":"https://pith.science/pith/KYB4HLB2GRBG5CRMNX7EJPD62B/action/author_attestation","sign_citation":"https://pith.science/pith/KYB4HLB2GRBG5CRMNX7EJPD62B/action/citation_signature","submit_replication":"https://pith.science/pith/KYB4HLB2GRBG5CRMNX7EJPD62B/action/replication_record"}},"created_at":"2026-05-17T23:45:52.025547+00:00","updated_at":"2026-05-17T23:45:52.025547+00:00"}