{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:5OUVIWAFWF4NQRCOBG2BNHRZEU","short_pith_number":"pith:5OUVIWAF","canonical_record":{"source":{"id":"1612.07448","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2016-12-22T05:41:27Z","cross_cats_sorted":[],"title_canon_sha256":"dd125dc581814fac1efc6d0f8115ff8ed397f2a06d2db81f04b8a6c3a5f890ea","abstract_canon_sha256":"dc01560fab2ff06065e38dcc68ec25fee8eea19fbad81e2bbc3c9ee120c3b181"},"schema_version":"1.0"},"canonical_sha256":"eba9545805b178d8444e09b4169e392539617c6aecbe5b66eecfd9d917b39032","source":{"kind":"arxiv","id":"1612.07448","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.07448","created_at":"2026-05-18T00:41:37Z"},{"alias_kind":"arxiv_version","alias_value":"1612.07448v6","created_at":"2026-05-18T00:41:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.07448","created_at":"2026-05-18T00:41:37Z"},{"alias_kind":"pith_short_12","alias_value":"5OUVIWAFWF4N","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_16","alias_value":"5OUVIWAFWF4NQRCO","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_8","alias_value":"5OUVIWAF","created_at":"2026-05-18T12:30:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:5OUVIWAFWF4NQRCOBG2BNHRZEU","target":"record","payload":{"canonical_record":{"source":{"id":"1612.07448","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2016-12-22T05:41:27Z","cross_cats_sorted":[],"title_canon_sha256":"dd125dc581814fac1efc6d0f8115ff8ed397f2a06d2db81f04b8a6c3a5f890ea","abstract_canon_sha256":"dc01560fab2ff06065e38dcc68ec25fee8eea19fbad81e2bbc3c9ee120c3b181"},"schema_version":"1.0"},"canonical_sha256":"eba9545805b178d8444e09b4169e392539617c6aecbe5b66eecfd9d917b39032","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:37.904525Z","signature_b64":"aAkAXvpGioj/o7gu1ObggtxLN0JdCwiLUjhXdCcW1QX9Se0jF50wy7qHl/TqFMwoijl6j6z3tUZWgTS/UYORDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eba9545805b178d8444e09b4169e392539617c6aecbe5b66eecfd9d917b39032","last_reissued_at":"2026-05-18T00:41:37.903870Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:37.903870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.07448","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:41:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1QxLxVs3tJ08yiZN8vPDCYsq109+22v6vD2UHwEXuAJOwg3XpGIO+chntlxBTd5fiB1graCvRLXA3lSWzRFBBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:38:31.406794Z"},"content_sha256":"2e5a399197da924cb05c67bdbd495d460ec0e70c4e2b175065e4ca681aae1749","schema_version":"1.0","event_id":"sha256:2e5a399197da924cb05c67bdbd495d460ec0e70c4e2b175065e4ca681aae1749"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:5OUVIWAFWF4NQRCOBG2BNHRZEU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Linear Algebra over Normalized Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Arun Kumar, Jeffrey Naughton, Jignesh M. Patel, Lingjiao Chen","submitted_at":"2016-12-22T05:41:27Z","abstract_excerpt":"Providing machine learning (ML) over relational data is a mainstream requirement for data analytics systems. While almost all the ML tools require the input data to be presented as a single table, many datasets are multi-table, which forces data scientists to join those tables first, leading to data redundancy and runtime waste. Recent works on \"factorized\" ML mitigate this issue for a few specific ML algorithms by pushing ML through joins. But their approaches require a manual rewrite of ML implementations. Such piecemeal methods create a massive development overhead when extending such ideas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.07448","kind":"arxiv","version":6},"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":"2026-05-18T00:41:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MWlYv63sa4HSJ+IlTsl3G2PXeMZj8/a/wUHgBdhC1fCaKvJqVuEnKLGknypjejYMqojn16PLV05P7oDFlxB8DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:38:31.407490Z"},"content_sha256":"29c1cf7b4e02af93df4965809be2898dcba4e9deb553d36cab4c63d1ce1de4a9","schema_version":"1.0","event_id":"sha256:29c1cf7b4e02af93df4965809be2898dcba4e9deb553d36cab4c63d1ce1de4a9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5OUVIWAFWF4NQRCOBG2BNHRZEU/bundle.json","state_url":"https://pith.science/pith/5OUVIWAFWF4NQRCOBG2BNHRZEU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5OUVIWAFWF4NQRCOBG2BNHRZEU/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":"2026-05-25T17:38:31Z","links":{"resolver":"https://pith.science/pith/5OUVIWAFWF4NQRCOBG2BNHRZEU","bundle":"https://pith.science/pith/5OUVIWAFWF4NQRCOBG2BNHRZEU/bundle.json","state":"https://pith.science/pith/5OUVIWAFWF4NQRCOBG2BNHRZEU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5OUVIWAFWF4NQRCOBG2BNHRZEU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:5OUVIWAFWF4NQRCOBG2BNHRZEU","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":"dc01560fab2ff06065e38dcc68ec25fee8eea19fbad81e2bbc3c9ee120c3b181","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2016-12-22T05:41:27Z","title_canon_sha256":"dd125dc581814fac1efc6d0f8115ff8ed397f2a06d2db81f04b8a6c3a5f890ea"},"schema_version":"1.0","source":{"id":"1612.07448","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.07448","created_at":"2026-05-18T00:41:37Z"},{"alias_kind":"arxiv_version","alias_value":"1612.07448v6","created_at":"2026-05-18T00:41:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.07448","created_at":"2026-05-18T00:41:37Z"},{"alias_kind":"pith_short_12","alias_value":"5OUVIWAFWF4N","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_16","alias_value":"5OUVIWAFWF4NQRCO","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_8","alias_value":"5OUVIWAF","created_at":"2026-05-18T12:30:01Z"}],"graph_snapshots":[{"event_id":"sha256:29c1cf7b4e02af93df4965809be2898dcba4e9deb553d36cab4c63d1ce1de4a9","target":"graph","created_at":"2026-05-18T00:41:37Z","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":"Providing machine learning (ML) over relational data is a mainstream requirement for data analytics systems. While almost all the ML tools require the input data to be presented as a single table, many datasets are multi-table, which forces data scientists to join those tables first, leading to data redundancy and runtime waste. Recent works on \"factorized\" ML mitigate this issue for a few specific ML algorithms by pushing ML through joins. But their approaches require a manual rewrite of ML implementations. Such piecemeal methods create a massive development overhead when extending such ideas","authors_text":"Arun Kumar, Jeffrey Naughton, Jignesh M. Patel, Lingjiao Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2016-12-22T05:41:27Z","title":"Towards Linear Algebra over Normalized Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.07448","kind":"arxiv","version":6},"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":"sha256:2e5a399197da924cb05c67bdbd495d460ec0e70c4e2b175065e4ca681aae1749","target":"record","created_at":"2026-05-18T00:41:37Z","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":"dc01560fab2ff06065e38dcc68ec25fee8eea19fbad81e2bbc3c9ee120c3b181","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2016-12-22T05:41:27Z","title_canon_sha256":"dd125dc581814fac1efc6d0f8115ff8ed397f2a06d2db81f04b8a6c3a5f890ea"},"schema_version":"1.0","source":{"id":"1612.07448","kind":"arxiv","version":6}},"canonical_sha256":"eba9545805b178d8444e09b4169e392539617c6aecbe5b66eecfd9d917b39032","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eba9545805b178d8444e09b4169e392539617c6aecbe5b66eecfd9d917b39032","first_computed_at":"2026-05-18T00:41:37.903870Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:37.903870Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aAkAXvpGioj/o7gu1ObggtxLN0JdCwiLUjhXdCcW1QX9Se0jF50wy7qHl/TqFMwoijl6j6z3tUZWgTS/UYORDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:37.904525Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.07448","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2e5a399197da924cb05c67bdbd495d460ec0e70c4e2b175065e4ca681aae1749","sha256:29c1cf7b4e02af93df4965809be2898dcba4e9deb553d36cab4c63d1ce1de4a9"],"state_sha256":"d4d8199b92352cc3e15c314b71358ec074f907189b3d1ebf8fbbfe8cc7e8c35a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BJf8jKDPKuO9E1QmhfNpdsp8BSl9LXSAQ6KMU2WgmZvdDhy5ykm41qIsPIAtd6z/SaDKfOga1PWbjp1diw38Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T17:38:31.411214Z","bundle_sha256":"01108c2b330ae222c909157aa5da802bac202679209e220df1021ad5f5b3c633"}}