{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:TNWYLYEZLKWTPCRYT7ZV3RBVER","short_pith_number":"pith:TNWYLYEZ","canonical_record":{"source":{"id":"1809.00677","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2018-09-03T18:05:12Z","cross_cats_sorted":[],"title_canon_sha256":"05b754ff114f6d749178004858a9ea926549dfb7d1acfb228d84a962d2f88636","abstract_canon_sha256":"d164baf2f63f14fa343d0081f62a1feaa8598c8dbcdd5be1e81f0ad4e16ca559"},"schema_version":"1.0"},"canonical_sha256":"9b6d85e0995aad378a389ff35dc435246e29e33bf3b4e5994d69ae6f7dabe7e2","source":{"kind":"arxiv","id":"1809.00677","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.00677","created_at":"2026-05-17T23:58:03Z"},{"alias_kind":"arxiv_version","alias_value":"1809.00677v2","created_at":"2026-05-17T23:58:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00677","created_at":"2026-05-17T23:58:03Z"},{"alias_kind":"pith_short_12","alias_value":"TNWYLYEZLKWT","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TNWYLYEZLKWTPCRY","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TNWYLYEZ","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:TNWYLYEZLKWTPCRYT7ZV3RBVER","target":"record","payload":{"canonical_record":{"source":{"id":"1809.00677","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2018-09-03T18:05:12Z","cross_cats_sorted":[],"title_canon_sha256":"05b754ff114f6d749178004858a9ea926549dfb7d1acfb228d84a962d2f88636","abstract_canon_sha256":"d164baf2f63f14fa343d0081f62a1feaa8598c8dbcdd5be1e81f0ad4e16ca559"},"schema_version":"1.0"},"canonical_sha256":"9b6d85e0995aad378a389ff35dc435246e29e33bf3b4e5994d69ae6f7dabe7e2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:03.995737Z","signature_b64":"mJXJk+rvzVFxpeKfB1bNb+IqauJtijJRUtxmSgyb4Ynpn921KQz5PqjW3AadG6UlO9p7axFehTRuzu/gIInpAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b6d85e0995aad378a389ff35dc435246e29e33bf3b4e5994d69ae6f7dabe7e2","last_reissued_at":"2026-05-17T23:58:03.995360Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:03.995360Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.00677","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":"2026-05-17T23:58:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m5HNabQwN3dR+0WcDrsNXNTz5MAtfvutPUjgRjqDsg1nHAXKNGMRjdIKrr4ZQip1HX96eZIJFLI62F+rpr2ZCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:33:08.029846Z"},"content_sha256":"619d648c48b2ed9c6fb7c446585c92d844b22698660f1535a4bb45449cd8ce84","schema_version":"1.0","event_id":"sha256:619d648c48b2ed9c6fb7c446585c92d844b22698660f1535a4bb45449cd8ce84"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:TNWYLYEZLKWTPCRYT7ZV3RBVER","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learned Cardinalities: Estimating Correlated Joins with Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Alfons Kemper, Andreas Kipf, Bernhard Radke, Peter Boncz, Thomas Kipf, Viktor Leis","submitted_at":"2018-09-03T18:05:12Z","abstract_excerpt":"We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set semantics to capture query features and true cardinalities. MSCN builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Our evaluation of MSCN using a real-world dataset shows that deep learning significantly enhances the quality of cardinality estimation, which is the core problem in query optimization."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.00677","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":""},"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":"2026-05-17T23:58:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YDjzkgJQxxk/gRseQfG9m92ZwOiodFrAA2meBzKuWg6EFBAwTifjXHXoz1fzPUJyPoG1hYfLaNs9dDTcQ1FHCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:33:08.030362Z"},"content_sha256":"bca881fa71870b83416f476a74153d9d5c7289de5c52c849ca523240d6fc2b55","schema_version":"1.0","event_id":"sha256:bca881fa71870b83416f476a74153d9d5c7289de5c52c849ca523240d6fc2b55"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TNWYLYEZLKWTPCRYT7ZV3RBVER/bundle.json","state_url":"https://pith.science/pith/TNWYLYEZLKWTPCRYT7ZV3RBVER/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TNWYLYEZLKWTPCRYT7ZV3RBVER/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":"2026-05-31T22:33:08Z","links":{"resolver":"https://pith.science/pith/TNWYLYEZLKWTPCRYT7ZV3RBVER","bundle":"https://pith.science/pith/TNWYLYEZLKWTPCRYT7ZV3RBVER/bundle.json","state":"https://pith.science/pith/TNWYLYEZLKWTPCRYT7ZV3RBVER/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TNWYLYEZLKWTPCRYT7ZV3RBVER/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TNWYLYEZLKWTPCRYT7ZV3RBVER","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":"d164baf2f63f14fa343d0081f62a1feaa8598c8dbcdd5be1e81f0ad4e16ca559","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2018-09-03T18:05:12Z","title_canon_sha256":"05b754ff114f6d749178004858a9ea926549dfb7d1acfb228d84a962d2f88636"},"schema_version":"1.0","source":{"id":"1809.00677","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.00677","created_at":"2026-05-17T23:58:03Z"},{"alias_kind":"arxiv_version","alias_value":"1809.00677v2","created_at":"2026-05-17T23:58:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00677","created_at":"2026-05-17T23:58:03Z"},{"alias_kind":"pith_short_12","alias_value":"TNWYLYEZLKWT","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TNWYLYEZLKWTPCRY","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TNWYLYEZ","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:bca881fa71870b83416f476a74153d9d5c7289de5c52c849ca523240d6fc2b55","target":"graph","created_at":"2026-05-17T23:58:03Z","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"},"paper":{"abstract_excerpt":"We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set semantics to capture query features and true cardinalities. MSCN builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Our evaluation of MSCN using a real-world dataset shows that deep learning significantly enhances the quality of cardinality estimation, which is the core problem in query optimization.","authors_text":"Alfons Kemper, Andreas Kipf, Bernhard Radke, Peter Boncz, Thomas Kipf, Viktor Leis","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2018-09-03T18:05:12Z","title":"Learned Cardinalities: Estimating Correlated Joins with Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.00677","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":"sha256:619d648c48b2ed9c6fb7c446585c92d844b22698660f1535a4bb45449cd8ce84","target":"record","created_at":"2026-05-17T23:58:03Z","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":"d164baf2f63f14fa343d0081f62a1feaa8598c8dbcdd5be1e81f0ad4e16ca559","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2018-09-03T18:05:12Z","title_canon_sha256":"05b754ff114f6d749178004858a9ea926549dfb7d1acfb228d84a962d2f88636"},"schema_version":"1.0","source":{"id":"1809.00677","kind":"arxiv","version":2}},"canonical_sha256":"9b6d85e0995aad378a389ff35dc435246e29e33bf3b4e5994d69ae6f7dabe7e2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b6d85e0995aad378a389ff35dc435246e29e33bf3b4e5994d69ae6f7dabe7e2","first_computed_at":"2026-05-17T23:58:03.995360Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:03.995360Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mJXJk+rvzVFxpeKfB1bNb+IqauJtijJRUtxmSgyb4Ynpn921KQz5PqjW3AadG6UlO9p7axFehTRuzu/gIInpAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:03.995737Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.00677","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:619d648c48b2ed9c6fb7c446585c92d844b22698660f1535a4bb45449cd8ce84","sha256:bca881fa71870b83416f476a74153d9d5c7289de5c52c849ca523240d6fc2b55"],"state_sha256":"e1a9f3582f3a46ba78e26b23d6bdd90659c5b8795fa2a9e7911b55fc44c6b7bf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y4scrN6qnAL4M0wEMAEwfqFD8kSwWHrm3PVnJtzGk8sqHCEmER67POmKdup7aVpEnKQ1O0C/0WSC+uWscZbnAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T22:33:08.033221Z","bundle_sha256":"e4f842fcc1c7fa01f9c6d7ea656bd2cfd07b7271bd40707873e2f66ce1f3151e"}}