{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:BFIO6UIEUKJG5RF5Q42AJBTTND","short_pith_number":"pith:BFIO6UIE","canonical_record":{"source":{"id":"2306.04846","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2023-06-08T00:42:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5a0f59ff15b38e617ddcce867eb5def7c6f8c94d05dbcdfc560250c992cc8044","abstract_canon_sha256":"f983da5b3595e3922b0aaa2a323502b20f90c5c4ad292dc02d044337d7ca4d7a"},"schema_version":"1.0"},"canonical_sha256":"0950ef5104a2926ec4bd873404867368de8db625294e7840308118e5e4e7b7c4","source":{"kind":"arxiv","id":"2306.04846","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.04846","created_at":"2026-07-05T06:22:06Z"},{"alias_kind":"arxiv_version","alias_value":"2306.04846v2","created_at":"2026-07-05T06:22:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.04846","created_at":"2026-07-05T06:22:06Z"},{"alias_kind":"pith_short_12","alias_value":"BFIO6UIEUKJG","created_at":"2026-07-05T06:22:06Z"},{"alias_kind":"pith_short_16","alias_value":"BFIO6UIEUKJG5RF5","created_at":"2026-07-05T06:22:06Z"},{"alias_kind":"pith_short_8","alias_value":"BFIO6UIE","created_at":"2026-07-05T06:22:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:BFIO6UIEUKJG5RF5Q42AJBTTND","target":"record","payload":{"canonical_record":{"source":{"id":"2306.04846","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2023-06-08T00:42:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5a0f59ff15b38e617ddcce867eb5def7c6f8c94d05dbcdfc560250c992cc8044","abstract_canon_sha256":"f983da5b3595e3922b0aaa2a323502b20f90c5c4ad292dc02d044337d7ca4d7a"},"schema_version":"1.0"},"canonical_sha256":"0950ef5104a2926ec4bd873404867368de8db625294e7840308118e5e4e7b7c4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:22:06.573036Z","signature_b64":"B6OgUjGUiew/aP7vXUI1EzTbVwctwhpjvvJgPyrO53xcDy1wwGKLFYpfjq97nEgdsMGd1ibUn8eF80pM2BdeCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0950ef5104a2926ec4bd873404867368de8db625294e7840308118e5e4e7b7c4","last_reissued_at":"2026-07-05T06:22:06.572560Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:22:06.572560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.04846","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:22:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gxRXiSGWFM2iazZHRb3z3n/ncwqlLzdU/PVe9l3/jk9CGr1tXme0ujjwJwKYa+j0vbSj7gbG5KTZT3u4VtWqBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:10:14.598679Z"},"content_sha256":"f4bf52ee2f9f2e5845c01018de5012f5131ecc3f332822475c03a333100c84e7","schema_version":"1.0","event_id":"sha256:f4bf52ee2f9f2e5845c01018de5012f5131ecc3f332822475c03a333100c84e7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:BFIO6UIEUKJG5RF5Q42AJBTTND","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learned spatial data partitioning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DB","authors_text":"Daichi Amagata, Keizo Hori, Makoto Onizuka, Yuki Murosaki, Yuya Sasaki","submitted_at":"2023-06-08T00:42:10Z","abstract_excerpt":"Due to the significant increase in the size of spatial data, it is essential to use distributed parallel processing systems to efficiently analyze spatial data. In this paper, we first study learned spatial data partitioning, which effectively assigns groups of big spatial data to computers based on locations of data by using machine learning techniques. We formalize spatial data partitioning in the context of reinforcement learning and develop a novel deep reinforcement learning algorithm. Our learning algorithm leverages features of spatial data partitioning and prunes ineffective learning p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.04846","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/2306.04846/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:22:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tgJ70si9JVl/oim1NzokXBXlH4HrWIEPacTYOiB8nOKtOuho3y4PeJCV2fcr7DM0TYSpABtXkqLPRcbOv9cgCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:10:14.599202Z"},"content_sha256":"84efa8dd293ead477c6bf5d6177094312bf4d93c91c51d1a7190f1c05931cb05","schema_version":"1.0","event_id":"sha256:84efa8dd293ead477c6bf5d6177094312bf4d93c91c51d1a7190f1c05931cb05"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BFIO6UIEUKJG5RF5Q42AJBTTND/bundle.json","state_url":"https://pith.science/pith/BFIO6UIEUKJG5RF5Q42AJBTTND/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BFIO6UIEUKJG5RF5Q42AJBTTND/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T14:10:14Z","links":{"resolver":"https://pith.science/pith/BFIO6UIEUKJG5RF5Q42AJBTTND","bundle":"https://pith.science/pith/BFIO6UIEUKJG5RF5Q42AJBTTND/bundle.json","state":"https://pith.science/pith/BFIO6UIEUKJG5RF5Q42AJBTTND/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BFIO6UIEUKJG5RF5Q42AJBTTND/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:BFIO6UIEUKJG5RF5Q42AJBTTND","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f983da5b3595e3922b0aaa2a323502b20f90c5c4ad292dc02d044337d7ca4d7a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2023-06-08T00:42:10Z","title_canon_sha256":"5a0f59ff15b38e617ddcce867eb5def7c6f8c94d05dbcdfc560250c992cc8044"},"schema_version":"1.0","source":{"id":"2306.04846","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.04846","created_at":"2026-07-05T06:22:06Z"},{"alias_kind":"arxiv_version","alias_value":"2306.04846v2","created_at":"2026-07-05T06:22:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.04846","created_at":"2026-07-05T06:22:06Z"},{"alias_kind":"pith_short_12","alias_value":"BFIO6UIEUKJG","created_at":"2026-07-05T06:22:06Z"},{"alias_kind":"pith_short_16","alias_value":"BFIO6UIEUKJG5RF5","created_at":"2026-07-05T06:22:06Z"},{"alias_kind":"pith_short_8","alias_value":"BFIO6UIE","created_at":"2026-07-05T06:22:06Z"}],"graph_snapshots":[{"event_id":"sha256:84efa8dd293ead477c6bf5d6177094312bf4d93c91c51d1a7190f1c05931cb05","target":"graph","created_at":"2026-07-05T06:22:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2306.04846/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Due to the significant increase in the size of spatial data, it is essential to use distributed parallel processing systems to efficiently analyze spatial data. In this paper, we first study learned spatial data partitioning, which effectively assigns groups of big spatial data to computers based on locations of data by using machine learning techniques. We formalize spatial data partitioning in the context of reinforcement learning and develop a novel deep reinforcement learning algorithm. Our learning algorithm leverages features of spatial data partitioning and prunes ineffective learning p","authors_text":"Daichi Amagata, Keizo Hori, Makoto Onizuka, Yuki Murosaki, Yuya Sasaki","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2023-06-08T00:42:10Z","title":"Learned spatial data partitioning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.04846","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f4bf52ee2f9f2e5845c01018de5012f5131ecc3f332822475c03a333100c84e7","target":"record","created_at":"2026-07-05T06:22:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f983da5b3595e3922b0aaa2a323502b20f90c5c4ad292dc02d044337d7ca4d7a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DB","submitted_at":"2023-06-08T00:42:10Z","title_canon_sha256":"5a0f59ff15b38e617ddcce867eb5def7c6f8c94d05dbcdfc560250c992cc8044"},"schema_version":"1.0","source":{"id":"2306.04846","kind":"arxiv","version":2}},"canonical_sha256":"0950ef5104a2926ec4bd873404867368de8db625294e7840308118e5e4e7b7c4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0950ef5104a2926ec4bd873404867368de8db625294e7840308118e5e4e7b7c4","first_computed_at":"2026-07-05T06:22:06.572560Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:22:06.572560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B6OgUjGUiew/aP7vXUI1EzTbVwctwhpjvvJgPyrO53xcDy1wwGKLFYpfjq97nEgdsMGd1ibUn8eF80pM2BdeCA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:22:06.573036Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.04846","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f4bf52ee2f9f2e5845c01018de5012f5131ecc3f332822475c03a333100c84e7","sha256:84efa8dd293ead477c6bf5d6177094312bf4d93c91c51d1a7190f1c05931cb05"],"state_sha256":"e5d42797d344edee6dbb32b4e325f9eb1d8c903f199a352932bc44ffd0497a89"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UWFOlGR2+uKoLWrb9l6GvaIjsnkYfTN703MU96xmgPqdtlBOjfsXO/fIAePiyEM69SdagKmkJKcmryJXrcOLAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T14:10:14.603005Z","bundle_sha256":"bbe417aee7305833a8cff85cffde5b58b89454aa3f1ae6d7fcd8148bb5d03db8"}}