{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:UNEJFQIO2RYCNKRCOZBXEQAL5F","short_pith_number":"pith:UNEJFQIO","schema_version":"1.0","canonical_sha256":"a34892c10ed47026aa22764372400be959653378eb57845f6928f5f9e7cbfe15","source":{"kind":"arxiv","id":"1705.01660","version":1},"attestation_state":"computed","paper":{"title":"MapReduce Particle Filtering with Exact Resampling and Deterministic Runtime","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"stat.CO","authors_text":"Jeyarajan Thiyagalingam, Lykourgos Kekempanos, Simon Maskell","submitted_at":"2017-05-04T00:05:30Z","abstract_excerpt":"Particle filtering is a numerical Bayesian technique that has great potential for solving sequential estimation problems involving non-linear and non-Gaussian models. Since the estimation accuracy achieved by particle filters improves as the number of particles increases, it is natural to consider as many particles as possible. MapReduce is a generic programming model that makes it possible to scale a wide variety of algorithms to Big data. However, despite the application of particle filters across many domains, little attention has been devoted to implementing particle filters using MapReduc"},"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":"1705.01660","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-05-04T00:05:30Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"aee07421c91bef9105463767dcad302558c25c2e3e76ba6424c0c8aeadb2c81f","abstract_canon_sha256":"e858eba42de38a5ebccf7e54075992355c740bf155b4c621100ef2e93d999e6b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:04.554782Z","signature_b64":"mfyfgvrR5VUi1YknfcpAjZX2gXPWNmNdwSLfBDv3DVvI54D3rMqgZ4JQeAyFlB2eTS6QpUeuH+aM3dqGE5dBCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a34892c10ed47026aa22764372400be959653378eb57845f6928f5f9e7cbfe15","last_reissued_at":"2026-05-18T00:30:04.554294Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:04.554294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MapReduce Particle Filtering with Exact Resampling and Deterministic Runtime","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"stat.CO","authors_text":"Jeyarajan Thiyagalingam, Lykourgos Kekempanos, Simon Maskell","submitted_at":"2017-05-04T00:05:30Z","abstract_excerpt":"Particle filtering is a numerical Bayesian technique that has great potential for solving sequential estimation problems involving non-linear and non-Gaussian models. Since the estimation accuracy achieved by particle filters improves as the number of particles increases, it is natural to consider as many particles as possible. MapReduce is a generic programming model that makes it possible to scale a wide variety of algorithms to Big data. However, despite the application of particle filters across many domains, little attention has been devoted to implementing particle filters using MapReduc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.01660","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":"1705.01660","created_at":"2026-05-18T00:30:04.554399+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.01660v1","created_at":"2026-05-18T00:30:04.554399+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.01660","created_at":"2026-05-18T00:30:04.554399+00:00"},{"alias_kind":"pith_short_12","alias_value":"UNEJFQIO2RYC","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"UNEJFQIO2RYCNKRC","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"UNEJFQIO","created_at":"2026-05-18T12:31:46.661854+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/UNEJFQIO2RYCNKRCOZBXEQAL5F","json":"https://pith.science/pith/UNEJFQIO2RYCNKRCOZBXEQAL5F.json","graph_json":"https://pith.science/api/pith-number/UNEJFQIO2RYCNKRCOZBXEQAL5F/graph.json","events_json":"https://pith.science/api/pith-number/UNEJFQIO2RYCNKRCOZBXEQAL5F/events.json","paper":"https://pith.science/paper/UNEJFQIO"},"agent_actions":{"view_html":"https://pith.science/pith/UNEJFQIO2RYCNKRCOZBXEQAL5F","download_json":"https://pith.science/pith/UNEJFQIO2RYCNKRCOZBXEQAL5F.json","view_paper":"https://pith.science/paper/UNEJFQIO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.01660&json=true","fetch_graph":"https://pith.science/api/pith-number/UNEJFQIO2RYCNKRCOZBXEQAL5F/graph.json","fetch_events":"https://pith.science/api/pith-number/UNEJFQIO2RYCNKRCOZBXEQAL5F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UNEJFQIO2RYCNKRCOZBXEQAL5F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UNEJFQIO2RYCNKRCOZBXEQAL5F/action/storage_attestation","attest_author":"https://pith.science/pith/UNEJFQIO2RYCNKRCOZBXEQAL5F/action/author_attestation","sign_citation":"https://pith.science/pith/UNEJFQIO2RYCNKRCOZBXEQAL5F/action/citation_signature","submit_replication":"https://pith.science/pith/UNEJFQIO2RYCNKRCOZBXEQAL5F/action/replication_record"}},"created_at":"2026-05-18T00:30:04.554399+00:00","updated_at":"2026-05-18T00:30:04.554399+00:00"}