{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:QSPDEFWHHDOJZQZS6RNJERPEWJ","short_pith_number":"pith:QSPDEFWH","schema_version":"1.0","canonical_sha256":"849e3216c738dc9cc332f45a9245e4b25080ae82cbf545f965ff6c97a8bcabdb","source":{"kind":"arxiv","id":"2006.16411","version":2},"attestation_state":"computed","paper":{"title":"Hands-off Model Integration in Spatial Index Structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DB","authors_text":"Ali Hadian, Ankit Kumar, Thomas Heinis","submitted_at":"2020-06-29T22:05:28Z","abstract_excerpt":"Spatial indexes are crucial for the analysis of the increasing amounts of spatial data, for example generated through IoT applications. The plethora of indexes that has been developed in recent decades has primarily been optimised for disk. With increasing amounts of memory even on commodity machines, however, moving them to main memory is an option. Doing so opens up the opportunity to use additional optimizations that are only amenable to main memory. In this paper we thus explore the opportunity to use light-weight machine learning models to accelerate queries on spatial indexes. We do so b"},"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":"2006.16411","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2020-06-29T22:05:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dad3216ea882a5728e89b151121fa3562e0be78ebd58baebbdbf8b239817b2ba","abstract_canon_sha256":"358c043c85860e03a301d3ac8a03cf23254609449452bdd292a9a27d9cb0732c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:25:39.687534Z","signature_b64":"VfbGpYvJaOf6H1jFunzakBbbnkKSZ1plX2YmAVZ3fBMzoWsDy4wGaEt1LPoPxkzYmFBfTJwy94kM/YhVP56XDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"849e3216c738dc9cc332f45a9245e4b25080ae82cbf545f965ff6c97a8bcabdb","last_reissued_at":"2026-07-05T01:25:39.687072Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:25:39.687072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hands-off Model Integration in Spatial Index Structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DB","authors_text":"Ali Hadian, Ankit Kumar, Thomas Heinis","submitted_at":"2020-06-29T22:05:28Z","abstract_excerpt":"Spatial indexes are crucial for the analysis of the increasing amounts of spatial data, for example generated through IoT applications. The plethora of indexes that has been developed in recent decades has primarily been optimised for disk. With increasing amounts of memory even on commodity machines, however, moving them to main memory is an option. Doing so opens up the opportunity to use additional optimizations that are only amenable to main memory. In this paper we thus explore the opportunity to use light-weight machine learning models to accelerate queries on spatial indexes. We do so b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.16411","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.16411/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":"2006.16411","created_at":"2026-07-05T01:25:39.687129+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.16411v2","created_at":"2026-07-05T01:25:39.687129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.16411","created_at":"2026-07-05T01:25:39.687129+00:00"},{"alias_kind":"pith_short_12","alias_value":"QSPDEFWHHDOJ","created_at":"2026-07-05T01:25:39.687129+00:00"},{"alias_kind":"pith_short_16","alias_value":"QSPDEFWHHDOJZQZS","created_at":"2026-07-05T01:25:39.687129+00:00"},{"alias_kind":"pith_short_8","alias_value":"QSPDEFWH","created_at":"2026-07-05T01:25:39.687129+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/QSPDEFWHHDOJZQZS6RNJERPEWJ","json":"https://pith.science/pith/QSPDEFWHHDOJZQZS6RNJERPEWJ.json","graph_json":"https://pith.science/api/pith-number/QSPDEFWHHDOJZQZS6RNJERPEWJ/graph.json","events_json":"https://pith.science/api/pith-number/QSPDEFWHHDOJZQZS6RNJERPEWJ/events.json","paper":"https://pith.science/paper/QSPDEFWH"},"agent_actions":{"view_html":"https://pith.science/pith/QSPDEFWHHDOJZQZS6RNJERPEWJ","download_json":"https://pith.science/pith/QSPDEFWHHDOJZQZS6RNJERPEWJ.json","view_paper":"https://pith.science/paper/QSPDEFWH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.16411&json=true","fetch_graph":"https://pith.science/api/pith-number/QSPDEFWHHDOJZQZS6RNJERPEWJ/graph.json","fetch_events":"https://pith.science/api/pith-number/QSPDEFWHHDOJZQZS6RNJERPEWJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QSPDEFWHHDOJZQZS6RNJERPEWJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QSPDEFWHHDOJZQZS6RNJERPEWJ/action/storage_attestation","attest_author":"https://pith.science/pith/QSPDEFWHHDOJZQZS6RNJERPEWJ/action/author_attestation","sign_citation":"https://pith.science/pith/QSPDEFWHHDOJZQZS6RNJERPEWJ/action/citation_signature","submit_replication":"https://pith.science/pith/QSPDEFWHHDOJZQZS6RNJERPEWJ/action/replication_record"}},"created_at":"2026-07-05T01:25:39.687129+00:00","updated_at":"2026-07-05T01:25:39.687129+00:00"}