{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:45IWKCYS5FQSXXUQA6WAPSMPRO","short_pith_number":"pith:45IWKCYS","schema_version":"1.0","canonical_sha256":"e751650b12e9612bde9007ac07c98f8b919a5796ff2febb745c8c1d1cc9cd922","source":{"kind":"arxiv","id":"1712.03900","version":1},"attestation_state":"computed","paper":{"title":"Developing a Spatial-Temporal Contextual and Semantic Trajectory Clustering Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Donald Cowan, Ivens Portugal, Paulo Alencar","submitted_at":"2017-12-08T16:29:48Z","abstract_excerpt":"This paper reports on ongoing research investigating more expressive approaches to spatial-temporal trajectory clustering. Spatial-temporal data is increasingly becoming universal as a result of widespread use of GPS and mobile devices, which makes mining and predictive analyses based on trajectories a critical activity in many domains. Trajectory analysis methods based on clustering techniques heavily often rely on a similarity definition to properly provide insights. However, although trajectories are currently described in terms of its two dimensions (space and time), their representation i"},"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":"1712.03900","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2017-12-08T16:29:48Z","cross_cats_sorted":[],"title_canon_sha256":"3de20db987c1a1e520ff01abea45b417c7762ac8b3bc1729142f029bfd149375","abstract_canon_sha256":"d5c4e2ee5e6598cc8fb9eab6656ca2cfd2b79307f037bc8a726fd76a20494765"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:19.604342Z","signature_b64":"ByYKtaw0EQV8hrhGgpm9+qO485S7gRS/2++Wo8b8utmF5a5z6RN65043p5kJSmnp0KyEH8EMzdVlb4JgEmvDDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e751650b12e9612bde9007ac07c98f8b919a5796ff2febb745c8c1d1cc9cd922","last_reissued_at":"2026-05-18T00:28:19.603453Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:19.603453Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Developing a Spatial-Temporal Contextual and Semantic Trajectory Clustering Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Donald Cowan, Ivens Portugal, Paulo Alencar","submitted_at":"2017-12-08T16:29:48Z","abstract_excerpt":"This paper reports on ongoing research investigating more expressive approaches to spatial-temporal trajectory clustering. Spatial-temporal data is increasingly becoming universal as a result of widespread use of GPS and mobile devices, which makes mining and predictive analyses based on trajectories a critical activity in many domains. Trajectory analysis methods based on clustering techniques heavily often rely on a similarity definition to properly provide insights. However, although trajectories are currently described in terms of its two dimensions (space and time), their representation i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.03900","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":"1712.03900","created_at":"2026-05-18T00:28:19.603563+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.03900v1","created_at":"2026-05-18T00:28:19.603563+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.03900","created_at":"2026-05-18T00:28:19.603563+00:00"},{"alias_kind":"pith_short_12","alias_value":"45IWKCYS5FQS","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"45IWKCYS5FQSXXUQ","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"45IWKCYS","created_at":"2026-05-18T12:30:58.224056+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/45IWKCYS5FQSXXUQA6WAPSMPRO","json":"https://pith.science/pith/45IWKCYS5FQSXXUQA6WAPSMPRO.json","graph_json":"https://pith.science/api/pith-number/45IWKCYS5FQSXXUQA6WAPSMPRO/graph.json","events_json":"https://pith.science/api/pith-number/45IWKCYS5FQSXXUQA6WAPSMPRO/events.json","paper":"https://pith.science/paper/45IWKCYS"},"agent_actions":{"view_html":"https://pith.science/pith/45IWKCYS5FQSXXUQA6WAPSMPRO","download_json":"https://pith.science/pith/45IWKCYS5FQSXXUQA6WAPSMPRO.json","view_paper":"https://pith.science/paper/45IWKCYS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.03900&json=true","fetch_graph":"https://pith.science/api/pith-number/45IWKCYS5FQSXXUQA6WAPSMPRO/graph.json","fetch_events":"https://pith.science/api/pith-number/45IWKCYS5FQSXXUQA6WAPSMPRO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/45IWKCYS5FQSXXUQA6WAPSMPRO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/45IWKCYS5FQSXXUQA6WAPSMPRO/action/storage_attestation","attest_author":"https://pith.science/pith/45IWKCYS5FQSXXUQA6WAPSMPRO/action/author_attestation","sign_citation":"https://pith.science/pith/45IWKCYS5FQSXXUQA6WAPSMPRO/action/citation_signature","submit_replication":"https://pith.science/pith/45IWKCYS5FQSXXUQA6WAPSMPRO/action/replication_record"}},"created_at":"2026-05-18T00:28:19.603563+00:00","updated_at":"2026-05-18T00:28:19.603563+00:00"}