{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:FJ26XY75TFLZKWNRXASQY73ECO","short_pith_number":"pith:FJ26XY75","schema_version":"1.0","canonical_sha256":"2a75ebe3fd99579559b1b8250c7f64139cd0b86a62831229f804a86eb127ccca","source":{"kind":"arxiv","id":"1411.3212","version":1},"attestation_state":"computed","paper":{"title":"Manycore processing of repeated range queries over massive moving objects observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.DS"],"primary_cat":"cs.DB","authors_text":"Christian S. Jensen, Claudio Silvestri, Francesco Lettich, Salvatore Orlando","submitted_at":"2014-11-12T15:46:39Z","abstract_excerpt":"The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. Parallelism enables such applications to face this data-intensive challenge and allows the devised systems to feature low latency and high scalability. In this paper we focus on a specific data-intensive problem, concerning the repeated processing of huge amounts of range queries over massive sets of moving objects, where the spatial extents of queries and objects are continuously modified over time. To tackle this problem and significantly accelerate qu"},"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":"1411.3212","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2014-11-12T15:46:39Z","cross_cats_sorted":["cs.DC","cs.DS"],"title_canon_sha256":"b1a71a7ea8d0b3f12d68fbeabff296f4a8951246ec303865a90268530695507f","abstract_canon_sha256":"54688925cf28a0a5c1782086a0f2605299cc07cfa923cb1acdd3f679c1fd0a9e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:37:45.510429Z","signature_b64":"l7xoNEArG8b3mQaCX6LY2jklWmToDTyDjr5tZz3AGijeE0Tsi/RBBF/r+7pUst6Z45pPIDQb9Td62nCWQfubAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2a75ebe3fd99579559b1b8250c7f64139cd0b86a62831229f804a86eb127ccca","last_reissued_at":"2026-05-18T02:37:45.509831Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:37:45.509831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Manycore processing of repeated range queries over massive moving objects observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.DS"],"primary_cat":"cs.DB","authors_text":"Christian S. Jensen, Claudio Silvestri, Francesco Lettich, Salvatore Orlando","submitted_at":"2014-11-12T15:46:39Z","abstract_excerpt":"The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. Parallelism enables such applications to face this data-intensive challenge and allows the devised systems to feature low latency and high scalability. In this paper we focus on a specific data-intensive problem, concerning the repeated processing of huge amounts of range queries over massive sets of moving objects, where the spatial extents of queries and objects are continuously modified over time. To tackle this problem and significantly accelerate qu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.3212","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":"1411.3212","created_at":"2026-05-18T02:37:45.509909+00:00"},{"alias_kind":"arxiv_version","alias_value":"1411.3212v1","created_at":"2026-05-18T02:37:45.509909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.3212","created_at":"2026-05-18T02:37:45.509909+00:00"},{"alias_kind":"pith_short_12","alias_value":"FJ26XY75TFLZ","created_at":"2026-05-18T12:28:28.263976+00:00"},{"alias_kind":"pith_short_16","alias_value":"FJ26XY75TFLZKWNR","created_at":"2026-05-18T12:28:28.263976+00:00"},{"alias_kind":"pith_short_8","alias_value":"FJ26XY75","created_at":"2026-05-18T12:28:28.263976+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/FJ26XY75TFLZKWNRXASQY73ECO","json":"https://pith.science/pith/FJ26XY75TFLZKWNRXASQY73ECO.json","graph_json":"https://pith.science/api/pith-number/FJ26XY75TFLZKWNRXASQY73ECO/graph.json","events_json":"https://pith.science/api/pith-number/FJ26XY75TFLZKWNRXASQY73ECO/events.json","paper":"https://pith.science/paper/FJ26XY75"},"agent_actions":{"view_html":"https://pith.science/pith/FJ26XY75TFLZKWNRXASQY73ECO","download_json":"https://pith.science/pith/FJ26XY75TFLZKWNRXASQY73ECO.json","view_paper":"https://pith.science/paper/FJ26XY75","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1411.3212&json=true","fetch_graph":"https://pith.science/api/pith-number/FJ26XY75TFLZKWNRXASQY73ECO/graph.json","fetch_events":"https://pith.science/api/pith-number/FJ26XY75TFLZKWNRXASQY73ECO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FJ26XY75TFLZKWNRXASQY73ECO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FJ26XY75TFLZKWNRXASQY73ECO/action/storage_attestation","attest_author":"https://pith.science/pith/FJ26XY75TFLZKWNRXASQY73ECO/action/author_attestation","sign_citation":"https://pith.science/pith/FJ26XY75TFLZKWNRXASQY73ECO/action/citation_signature","submit_replication":"https://pith.science/pith/FJ26XY75TFLZKWNRXASQY73ECO/action/replication_record"}},"created_at":"2026-05-18T02:37:45.509909+00:00","updated_at":"2026-05-18T02:37:45.509909+00:00"}