{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:CVIF666BLE4MILONKFZA5F54GO","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":"eb029ebc38930cf4b7bd51f1af113040d855cc43d6bf1aa15d52634fc5a7a9c6","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-10-05T16:30:33Z","title_canon_sha256":"eebb04d5cd27785fdaafef1d6d08024b1546cf99e1a80c91cc9f2852668e2b9b"},"schema_version":"1.0","source":{"id":"1710.02103","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.02103","created_at":"2026-05-18T00:33:37Z"},{"alias_kind":"arxiv_version","alias_value":"1710.02103v1","created_at":"2026-05-18T00:33:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.02103","created_at":"2026-05-18T00:33:37Z"},{"alias_kind":"pith_short_12","alias_value":"CVIF666BLE4M","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CVIF666BLE4MILON","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CVIF666B","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:51e55eb47bcbe846c655720300c7f72581d62c82538b21f2699e0a7f73ec9e53","target":"graph","created_at":"2026-05-18T00:33:37Z","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":"A current challenge for data management systems is to support the construction and maintenance of machine learning models over data that is large, multi-dimensional, and evolving. While systems that could support these tasks are emerging, the need to scale to distributed, streaming data requires new models and algorithms. In this setting, as well as computational scalability and model accuracy, we also need to minimize the amount of communication between distributed processors, which is the chief component of latency. We study Bayesian networks, the workhorse of graphical models, and present a","authors_text":"Graham Cormode, Srikanta Tirthapura, Yu Zhang","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-10-05T16:30:33Z","title":"Learning Graphical Models from a Distributed Stream"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.02103","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":"sha256:243083b5e94e48bbcd15abb76f4fbace6f16c8ff02950dbb8717af59ae122343","target":"record","created_at":"2026-05-18T00:33:37Z","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":"eb029ebc38930cf4b7bd51f1af113040d855cc43d6bf1aa15d52634fc5a7a9c6","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-10-05T16:30:33Z","title_canon_sha256":"eebb04d5cd27785fdaafef1d6d08024b1546cf99e1a80c91cc9f2852668e2b9b"},"schema_version":"1.0","source":{"id":"1710.02103","kind":"arxiv","version":1}},"canonical_sha256":"15505f7bc15938c42dcd51720e97bc339218aea8f64b3c3872e8adf189245867","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"15505f7bc15938c42dcd51720e97bc339218aea8f64b3c3872e8adf189245867","first_computed_at":"2026-05-18T00:33:37.520835Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:33:37.520835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mEd5FMQFdetdEslUesBYUwnpUQiu56e5WiVz0QYdrb9CuM85nSeg59EVu2WZKz4MaeWVAJxqKOiTkSy9qmAeBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:33:37.521499Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.02103","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:243083b5e94e48bbcd15abb76f4fbace6f16c8ff02950dbb8717af59ae122343","sha256:51e55eb47bcbe846c655720300c7f72581d62c82538b21f2699e0a7f73ec9e53"],"state_sha256":"9a117a44adb556f5e5e9c27b73d2aeacea63dab952028fbe5f26620b7779824d"}