{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:62Q56WKVK67PDPVTRCURLXIKFJ","short_pith_number":"pith:62Q56WKV","schema_version":"1.0","canonical_sha256":"f6a1df595557bef1beb388a915dd0a2a5ca3191bcb10a897173c9570d4b1eef1","source":{"kind":"arxiv","id":"1503.06384","version":1},"attestation_state":"computed","paper":{"title":"Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Matthias Boehm","submitted_at":"2015-03-22T05:00:08Z","abstract_excerpt":"Declarative large-scale machine learning (ML) aims at the specification of ML algorithms in a high-level language and automatic generation of hybrid runtime execution plans ranging from single node, in-memory computations to distributed computations on MapReduce (MR) or similar frameworks like Spark. The compilation of large-scale ML programs exhibits many opportunities for automatic optimization. Advanced cost-based optimization techniques require---as a fundamental precondition---an accurate cost model for evaluating the impact of optimization decisions. In this paper, we share insights into"},"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":"1503.06384","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2015-03-22T05:00:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e8c081730055323e12306c51d50629fd7014245f7ea4a33b8bb3b73f5549efce","abstract_canon_sha256":"fa656decfef61c001612e45c40c8cd50c580770314fdc037f930f485a6644f03"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:20:38.559076Z","signature_b64":"XkWriQDzGaUMzvdXMlcuY2Oj8IahzFkMWEYzLvtJq+BRYIDGnuaN9ZUE45h+mD2D61189EzHlCRI4mfFEBItAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6a1df595557bef1beb388a915dd0a2a5ca3191bcb10a897173c9570d4b1eef1","last_reissued_at":"2026-05-18T02:20:38.558452Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:20:38.558452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Matthias Boehm","submitted_at":"2015-03-22T05:00:08Z","abstract_excerpt":"Declarative large-scale machine learning (ML) aims at the specification of ML algorithms in a high-level language and automatic generation of hybrid runtime execution plans ranging from single node, in-memory computations to distributed computations on MapReduce (MR) or similar frameworks like Spark. The compilation of large-scale ML programs exhibits many opportunities for automatic optimization. Advanced cost-based optimization techniques require---as a fundamental precondition---an accurate cost model for evaluating the impact of optimization decisions. In this paper, we share insights into"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.06384","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":""},"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":"1503.06384","created_at":"2026-05-18T02:20:38.558538+00:00"},{"alias_kind":"arxiv_version","alias_value":"1503.06384v1","created_at":"2026-05-18T02:20:38.558538+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.06384","created_at":"2026-05-18T02:20:38.558538+00:00"},{"alias_kind":"pith_short_12","alias_value":"62Q56WKVK67P","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_16","alias_value":"62Q56WKVK67PDPVT","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_8","alias_value":"62Q56WKV","created_at":"2026-05-18T12:29:07.941421+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/62Q56WKVK67PDPVTRCURLXIKFJ","json":"https://pith.science/pith/62Q56WKVK67PDPVTRCURLXIKFJ.json","graph_json":"https://pith.science/api/pith-number/62Q56WKVK67PDPVTRCURLXIKFJ/graph.json","events_json":"https://pith.science/api/pith-number/62Q56WKVK67PDPVTRCURLXIKFJ/events.json","paper":"https://pith.science/paper/62Q56WKV"},"agent_actions":{"view_html":"https://pith.science/pith/62Q56WKVK67PDPVTRCURLXIKFJ","download_json":"https://pith.science/pith/62Q56WKVK67PDPVTRCURLXIKFJ.json","view_paper":"https://pith.science/paper/62Q56WKV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1503.06384&json=true","fetch_graph":"https://pith.science/api/pith-number/62Q56WKVK67PDPVTRCURLXIKFJ/graph.json","fetch_events":"https://pith.science/api/pith-number/62Q56WKVK67PDPVTRCURLXIKFJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/62Q56WKVK67PDPVTRCURLXIKFJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/62Q56WKVK67PDPVTRCURLXIKFJ/action/storage_attestation","attest_author":"https://pith.science/pith/62Q56WKVK67PDPVTRCURLXIKFJ/action/author_attestation","sign_citation":"https://pith.science/pith/62Q56WKVK67PDPVTRCURLXIKFJ/action/citation_signature","submit_replication":"https://pith.science/pith/62Q56WKVK67PDPVTRCURLXIKFJ/action/replication_record"}},"created_at":"2026-05-18T02:20:38.558538+00:00","updated_at":"2026-05-18T02:20:38.558538+00:00"}