{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5QHAVJSSHLKE6URKJSMEPKI2SK","short_pith_number":"pith:5QHAVJSS","schema_version":"1.0","canonical_sha256":"ec0e0aa6523ad44f522a4c9847a91a928e0792a860cc8f063dddc0b44eb1696f","source":{"kind":"arxiv","id":"2606.19034","version":1},"attestation_state":"computed","paper":{"title":"Evaluating Learned Spatial Indexes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Jun Yang, Michael Mathioudakis, Sachith Pai","submitted_at":"2026-06-17T13:05:08Z","abstract_excerpt":"Learned indexes improve query performance by adapting search structures to data and workload distributions. Although many learned indexes have been proposed, their trade-offs remain insufficiently understood for spatial range queries, where performance depends not only on model accuracy but also on data and query skew, layout granularity, selectivity, and storage behavior.\n  In this work, we perform an experimental study of learned indexes for spatial range queries. We examine a representative set of indexes and address seven fundamental questions: (1) How does block size influence query laten"},"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":"2606.19034","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2026-06-17T13:05:08Z","cross_cats_sorted":[],"title_canon_sha256":"ef42ee91c9d177e7422fd7f3e07c88b68fe1e39481d640907d9a4779669ca639","abstract_canon_sha256":"1d154d11a4aad6012b3147d43332b519887efe1ca2145be30b7024da32359adf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:11:54.981115Z","signature_b64":"L1sc9PsuAuregELMSy4T9A+cESwjh55fddVV+b1GV1/jBhEkBjEbVSoGEwh2QNPgFeQmcNFv60kaBaAJNQXeBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec0e0aa6523ad44f522a4c9847a91a928e0792a860cc8f063dddc0b44eb1696f","last_reissued_at":"2026-06-19T16:11:54.980773Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:11:54.980773Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Learned Spatial Indexes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Jun Yang, Michael Mathioudakis, Sachith Pai","submitted_at":"2026-06-17T13:05:08Z","abstract_excerpt":"Learned indexes improve query performance by adapting search structures to data and workload distributions. Although many learned indexes have been proposed, their trade-offs remain insufficiently understood for spatial range queries, where performance depends not only on model accuracy but also on data and query skew, layout granularity, selectivity, and storage behavior.\n  In this work, we perform an experimental study of learned indexes for spatial range queries. We examine a representative set of indexes and address seven fundamental questions: (1) How does block size influence query laten"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.19034","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.19034/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":"2606.19034","created_at":"2026-06-19T16:11:54.980834+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.19034v1","created_at":"2026-06-19T16:11:54.980834+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.19034","created_at":"2026-06-19T16:11:54.980834+00:00"},{"alias_kind":"pith_short_12","alias_value":"5QHAVJSSHLKE","created_at":"2026-06-19T16:11:54.980834+00:00"},{"alias_kind":"pith_short_16","alias_value":"5QHAVJSSHLKE6URK","created_at":"2026-06-19T16:11:54.980834+00:00"},{"alias_kind":"pith_short_8","alias_value":"5QHAVJSS","created_at":"2026-06-19T16:11:54.980834+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/5QHAVJSSHLKE6URKJSMEPKI2SK","json":"https://pith.science/pith/5QHAVJSSHLKE6URKJSMEPKI2SK.json","graph_json":"https://pith.science/api/pith-number/5QHAVJSSHLKE6URKJSMEPKI2SK/graph.json","events_json":"https://pith.science/api/pith-number/5QHAVJSSHLKE6URKJSMEPKI2SK/events.json","paper":"https://pith.science/paper/5QHAVJSS"},"agent_actions":{"view_html":"https://pith.science/pith/5QHAVJSSHLKE6URKJSMEPKI2SK","download_json":"https://pith.science/pith/5QHAVJSSHLKE6URKJSMEPKI2SK.json","view_paper":"https://pith.science/paper/5QHAVJSS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.19034&json=true","fetch_graph":"https://pith.science/api/pith-number/5QHAVJSSHLKE6URKJSMEPKI2SK/graph.json","fetch_events":"https://pith.science/api/pith-number/5QHAVJSSHLKE6URKJSMEPKI2SK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5QHAVJSSHLKE6URKJSMEPKI2SK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5QHAVJSSHLKE6URKJSMEPKI2SK/action/storage_attestation","attest_author":"https://pith.science/pith/5QHAVJSSHLKE6URKJSMEPKI2SK/action/author_attestation","sign_citation":"https://pith.science/pith/5QHAVJSSHLKE6URKJSMEPKI2SK/action/citation_signature","submit_replication":"https://pith.science/pith/5QHAVJSSHLKE6URKJSMEPKI2SK/action/replication_record"}},"created_at":"2026-06-19T16:11:54.980834+00:00","updated_at":"2026-06-19T16:11:54.980834+00:00"}