{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:LMNVYK5TFOSHW4QIXABWHKL725","short_pith_number":"pith:LMNVYK5T","canonical_record":{"source":{"id":"2001.03541","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PL","submitted_at":"2020-01-10T16:14:44Z","cross_cats_sorted":["cs.DB","cs.LG"],"title_canon_sha256":"3d41ed1c62a7482a3fc7b0365d06b8e301c81d70655f4b0d2091fda46309cc14","abstract_canon_sha256":"096ee404d6000344c30538c5cc576127dc70754833d0db447bbb3a3bebb8dbc0"},"schema_version":"1.0"},"canonical_sha256":"5b1b5c2bb32ba47b7208b80363a97fd7754137119d7808869b804493b19225c6","source":{"kind":"arxiv","id":"2001.03541","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.03541","created_at":"2026-07-05T00:32:44Z"},{"alias_kind":"arxiv_version","alias_value":"2001.03541v1","created_at":"2026-07-05T00:32:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.03541","created_at":"2026-07-05T00:32:44Z"},{"alias_kind":"pith_short_12","alias_value":"LMNVYK5TFOSH","created_at":"2026-07-05T00:32:44Z"},{"alias_kind":"pith_short_16","alias_value":"LMNVYK5TFOSHW4QI","created_at":"2026-07-05T00:32:44Z"},{"alias_kind":"pith_short_8","alias_value":"LMNVYK5T","created_at":"2026-07-05T00:32:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:LMNVYK5TFOSHW4QIXABWHKL725","target":"record","payload":{"canonical_record":{"source":{"id":"2001.03541","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PL","submitted_at":"2020-01-10T16:14:44Z","cross_cats_sorted":["cs.DB","cs.LG"],"title_canon_sha256":"3d41ed1c62a7482a3fc7b0365d06b8e301c81d70655f4b0d2091fda46309cc14","abstract_canon_sha256":"096ee404d6000344c30538c5cc576127dc70754833d0db447bbb3a3bebb8dbc0"},"schema_version":"1.0"},"canonical_sha256":"5b1b5c2bb32ba47b7208b80363a97fd7754137119d7808869b804493b19225c6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:32:44.873777Z","signature_b64":"Iasn0x4eA6qUMIwpSQP6szV6okcoV35clVyk0leCnHOc6kWi7FKS4G2qH7BsgYE0+oOooq1Cwpi90extPD88BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b1b5c2bb32ba47b7208b80363a97fd7754137119d7808869b804493b19225c6","last_reissued_at":"2026-07-05T00:32:44.873371Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:32:44.873371Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2001.03541","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:32:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7VtxWa31VAhC4IszSFwUklzd/x57TLR89yrxawD6d/hqT7v4F6DQn1DPgL7J2+wQFhmbO75gFq9o6N+Fg7HKBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:02:48.465334Z"},"content_sha256":"e2681a2b54c0f74998ef44b48ce65d3f80f95620dbd9b8033a4b9a47457b4267","schema_version":"1.0","event_id":"sha256:e2681a2b54c0f74998ef44b48ce65d3f80f95620dbd9b8033a4b9a47457b4267"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:LMNVYK5TFOSHW4QIXABWHKL725","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-layer Optimizations for End-to-End Data Analytics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB","cs.LG"],"primary_cat":"cs.PL","authors_text":"Alexandru Ghita, Amir Shaikhha, Dan Olteanu, Maximilian Schleich","submitted_at":"2020-01-10T16:14:44Z","abstract_excerpt":"We consider the problem of training machine learning models over multi-relational data. The mainstream approach is to first construct the training dataset using a feature extraction query over input database and then use a statistical software package of choice to train the model. In this paper we introduce Iterative Functional Aggregate Queries (IFAQ), a framework that realizes an alternative approach. IFAQ treats the feature extraction query and the learning task as one program given in the IFAQ's domain-specific language, which captures a subset of Python commonly used in Jupyter notebooks "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.03541","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/2001.03541/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":"2026-07-05T00:32:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gC1Fl1UzlSUbtwg6bf85174ltsEng1rzkrq5OhQSjL4XcDUffqGaO1ibKS8bm5CcRWL7z7EQej3X9oa37X2oDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:02:48.466217Z"},"content_sha256":"c1a9c9f9aa788ff394c4bb4943235ed729f0987d13ad8fed5bff67f873ce0aa7","schema_version":"1.0","event_id":"sha256:c1a9c9f9aa788ff394c4bb4943235ed729f0987d13ad8fed5bff67f873ce0aa7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LMNVYK5TFOSHW4QIXABWHKL725/bundle.json","state_url":"https://pith.science/pith/LMNVYK5TFOSHW4QIXABWHKL725/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LMNVYK5TFOSHW4QIXABWHKL725/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":"2026-07-09T06:02:48Z","links":{"resolver":"https://pith.science/pith/LMNVYK5TFOSHW4QIXABWHKL725","bundle":"https://pith.science/pith/LMNVYK5TFOSHW4QIXABWHKL725/bundle.json","state":"https://pith.science/pith/LMNVYK5TFOSHW4QIXABWHKL725/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LMNVYK5TFOSHW4QIXABWHKL725/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:LMNVYK5TFOSHW4QIXABWHKL725","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":"096ee404d6000344c30538c5cc576127dc70754833d0db447bbb3a3bebb8dbc0","cross_cats_sorted":["cs.DB","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PL","submitted_at":"2020-01-10T16:14:44Z","title_canon_sha256":"3d41ed1c62a7482a3fc7b0365d06b8e301c81d70655f4b0d2091fda46309cc14"},"schema_version":"1.0","source":{"id":"2001.03541","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.03541","created_at":"2026-07-05T00:32:44Z"},{"alias_kind":"arxiv_version","alias_value":"2001.03541v1","created_at":"2026-07-05T00:32:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.03541","created_at":"2026-07-05T00:32:44Z"},{"alias_kind":"pith_short_12","alias_value":"LMNVYK5TFOSH","created_at":"2026-07-05T00:32:44Z"},{"alias_kind":"pith_short_16","alias_value":"LMNVYK5TFOSHW4QI","created_at":"2026-07-05T00:32:44Z"},{"alias_kind":"pith_short_8","alias_value":"LMNVYK5T","created_at":"2026-07-05T00:32:44Z"}],"graph_snapshots":[{"event_id":"sha256:c1a9c9f9aa788ff394c4bb4943235ed729f0987d13ad8fed5bff67f873ce0aa7","target":"graph","created_at":"2026-07-05T00:32:44Z","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/2001.03541/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We consider the problem of training machine learning models over multi-relational data. The mainstream approach is to first construct the training dataset using a feature extraction query over input database and then use a statistical software package of choice to train the model. In this paper we introduce Iterative Functional Aggregate Queries (IFAQ), a framework that realizes an alternative approach. IFAQ treats the feature extraction query and the learning task as one program given in the IFAQ's domain-specific language, which captures a subset of Python commonly used in Jupyter notebooks ","authors_text":"Alexandru Ghita, Amir Shaikhha, Dan Olteanu, Maximilian Schleich","cross_cats":["cs.DB","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PL","submitted_at":"2020-01-10T16:14:44Z","title":"Multi-layer Optimizations for End-to-End Data Analytics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.03541","kind":"arxiv","version":1},"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:e2681a2b54c0f74998ef44b48ce65d3f80f95620dbd9b8033a4b9a47457b4267","target":"record","created_at":"2026-07-05T00:32:44Z","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":"096ee404d6000344c30538c5cc576127dc70754833d0db447bbb3a3bebb8dbc0","cross_cats_sorted":["cs.DB","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PL","submitted_at":"2020-01-10T16:14:44Z","title_canon_sha256":"3d41ed1c62a7482a3fc7b0365d06b8e301c81d70655f4b0d2091fda46309cc14"},"schema_version":"1.0","source":{"id":"2001.03541","kind":"arxiv","version":1}},"canonical_sha256":"5b1b5c2bb32ba47b7208b80363a97fd7754137119d7808869b804493b19225c6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5b1b5c2bb32ba47b7208b80363a97fd7754137119d7808869b804493b19225c6","first_computed_at":"2026-07-05T00:32:44.873371Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:32:44.873371Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Iasn0x4eA6qUMIwpSQP6szV6okcoV35clVyk0leCnHOc6kWi7FKS4G2qH7BsgYE0+oOooq1Cwpi90extPD88BA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:32:44.873777Z","signed_message":"canonical_sha256_bytes"},"source_id":"2001.03541","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e2681a2b54c0f74998ef44b48ce65d3f80f95620dbd9b8033a4b9a47457b4267","sha256:c1a9c9f9aa788ff394c4bb4943235ed729f0987d13ad8fed5bff67f873ce0aa7"],"state_sha256":"4af4f61990f452d47df4606a2d5cbe0ba3f66018d2f2e915659dc9bdf645b64e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rP4hcmk5jKivgPPoGIWrXb2G0G/A3vBoFgEB5DEj0z4lANXOWfH9Ybek45Yv2sP1cXM5V3sxcZIQ7n1UisklBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T06:02:48.469570Z","bundle_sha256":"c4eb2d47892dd083dafbb10799c417135f254d5eb7d542e918f0d5db14ef67bc"}}