{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:C3SLCKBIMPU2FOVGQIVUU2PZZ3","short_pith_number":"pith:C3SLCKBI","canonical_record":{"source":{"id":"2605.02488","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-04T11:41:31Z","cross_cats_sorted":["cs.DB","cs.LO"],"title_canon_sha256":"c0a26d3c7dbc4b2ac25fe2c77b21b5431ba80aa54a1ee713979536686ccff7fe","abstract_canon_sha256":"ba9f47c0a7ad805ad5b1f69117a677c79c65f9ba477e7263113f84ede84e111f"},"schema_version":"1.0"},"canonical_sha256":"16e4b1282863e9a2baa6822b4a69f9ced9838f57195a3933e061d14ceff5d56b","source":{"kind":"arxiv","id":"2605.02488","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.02488","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"arxiv_version","alias_value":"2605.02488v2","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.02488","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"pith_short_12","alias_value":"C3SLCKBIMPU2","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"pith_short_16","alias_value":"C3SLCKBIMPU2FOVG","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"pith_short_8","alias_value":"C3SLCKBI","created_at":"2026-06-03T01:05:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:C3SLCKBIMPU2FOVGQIVUU2PZZ3","target":"record","payload":{"canonical_record":{"source":{"id":"2605.02488","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-04T11:41:31Z","cross_cats_sorted":["cs.DB","cs.LO"],"title_canon_sha256":"c0a26d3c7dbc4b2ac25fe2c77b21b5431ba80aa54a1ee713979536686ccff7fe","abstract_canon_sha256":"ba9f47c0a7ad805ad5b1f69117a677c79c65f9ba477e7263113f84ede84e111f"},"schema_version":"1.0"},"canonical_sha256":"16e4b1282863e9a2baa6822b4a69f9ced9838f57195a3933e061d14ceff5d56b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:50.847012Z","signature_b64":"dxc3EWBx8UWmIuOTq0UaoRGXohRqceve7ZEJktJ/iqLuIc3joAPPeqKwQtCDm317Uh12LHR7na2Iboi6DGowCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16e4b1282863e9a2baa6822b4a69f9ced9838f57195a3933e061d14ceff5d56b","last_reissued_at":"2026-06-03T01:05:50.846600Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:50.846600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.02488","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-03T01:05:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kf9gAwWj23mJAVZFdDhoMty/QTyJbldFiwhcsusAUfWHHusBfyZ5kmsI56hkoS68QwOUioVbUUSL26WPI9gwCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T20:57:33.268804Z"},"content_sha256":"23c7f9b19f874f45d92aaf3422c70e46adfaec27088f12ad8a8264f9c334d866","schema_version":"1.0","event_id":"sha256:23c7f9b19f874f45d92aaf3422c70e46adfaec27088f12ad8a8264f9c334d866"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:C3SLCKBIMPU2FOVGQIVUU2PZZ3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Temporal Datalog Materialisation for Composite Event Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Mapping fragments of event languages to Temporal Datalog enables one uniform engine for composite event recognition in streams.","cross_cats":["cs.DB","cs.LO"],"primary_cat":"cs.AI","authors_text":"Periklis Mantenoglou","submitted_at":"2026-05-04T11:41:31Z","abstract_excerpt":"Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbolic events. This demand has motivated the development of (i) event specification languages, which define composite events via temporal patterns over simpler events, and (ii) stream reasoning frameworks, evaluating patterns expressed in these languages. However, event specification languages are typically studied in isolation, complicating their comparison in terms of expressivity and obscuring the scope of their associated stream reasoners. To "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our approach yields a uniform composite event recognition mechanism that has the potential to generalise across a wide range of practical event specification languages.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the practical fragments of event specification languages can be mapped to Temporal Datalog->- while preserving their intended semantics and that Streaming Trigger Graphs maintain both correctness and efficiency gains for streaming data.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Maps practical event languages to Temporal Datalog and introduces Streaming Trigger Graphs for efficient composite event recognition in data streams.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Mapping fragments of event languages to Temporal Datalog enables one uniform engine for composite event recognition in streams.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1a345d4e469c8a4c4414373978c9be2aa7d22be63ce27655d6cfe7befb5283eb"},"source":{"id":"2605.02488","kind":"arxiv","version":2},"verdict":{"id":"1070c4ad-4805-4001-87fd-88e640868955","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T18:31:52.665245Z","strongest_claim":"Our approach yields a uniform composite event recognition mechanism that has the potential to generalise across a wide range of practical event specification languages.","one_line_summary":"Maps practical event languages to Temporal Datalog and introduces Streaming Trigger Graphs for efficient composite event recognition in data streams.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the practical fragments of event specification languages can be mapped to Temporal Datalog->- while preserving their intended semantics and that Streaming Trigger Graphs maintain both correctness and efficiency gains for streaming data.","pith_extraction_headline":"Mapping fragments of event languages to Temporal Datalog enables one uniform engine for composite event recognition in streams."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.02488/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T15:39:49.890926Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-20T03:01:22.821412Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T16:21:02.960341Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"b8cc2196d4db955ae11b33803658f3b31170858473245933fd287bf6cbf8fa6c"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"656722bac0a1e60a306e36adf0bf6a826b62e95389d95167b4a927bdfac3a5bd"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"1070c4ad-4805-4001-87fd-88e640868955"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-03T01:05:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4drUfhSt97JcZhxsYSpyH/dzcflqINN9Z5R1uHfZw+9Jr9/kMBks+72gDzG+z9zSW0ruHoLkHkR9BXRFeCYNCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T20:57:33.269823Z"},"content_sha256":"2e64a4b3cf84f42cbc61598b560528a69845569066262c3d3fccfce356e8c7de","schema_version":"1.0","event_id":"sha256:2e64a4b3cf84f42cbc61598b560528a69845569066262c3d3fccfce356e8c7de"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C3SLCKBIMPU2FOVGQIVUU2PZZ3/bundle.json","state_url":"https://pith.science/pith/C3SLCKBIMPU2FOVGQIVUU2PZZ3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C3SLCKBIMPU2FOVGQIVUU2PZZ3/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":"2026-06-08T20:57:33Z","links":{"resolver":"https://pith.science/pith/C3SLCKBIMPU2FOVGQIVUU2PZZ3","bundle":"https://pith.science/pith/C3SLCKBIMPU2FOVGQIVUU2PZZ3/bundle.json","state":"https://pith.science/pith/C3SLCKBIMPU2FOVGQIVUU2PZZ3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C3SLCKBIMPU2FOVGQIVUU2PZZ3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:C3SLCKBIMPU2FOVGQIVUU2PZZ3","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":"ba9f47c0a7ad805ad5b1f69117a677c79c65f9ba477e7263113f84ede84e111f","cross_cats_sorted":["cs.DB","cs.LO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-04T11:41:31Z","title_canon_sha256":"c0a26d3c7dbc4b2ac25fe2c77b21b5431ba80aa54a1ee713979536686ccff7fe"},"schema_version":"1.0","source":{"id":"2605.02488","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.02488","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"arxiv_version","alias_value":"2605.02488v2","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.02488","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"pith_short_12","alias_value":"C3SLCKBIMPU2","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"pith_short_16","alias_value":"C3SLCKBIMPU2FOVG","created_at":"2026-06-03T01:05:50Z"},{"alias_kind":"pith_short_8","alias_value":"C3SLCKBI","created_at":"2026-06-03T01:05:50Z"}],"graph_snapshots":[{"event_id":"sha256:2e64a4b3cf84f42cbc61598b560528a69845569066262c3d3fccfce356e8c7de","target":"graph","created_at":"2026-06-03T01:05:50Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Our approach yields a uniform composite event recognition mechanism that has the potential to generalise across a wide range of practical event specification languages."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the practical fragments of event specification languages can be mapped to Temporal Datalog->- while preserving their intended semantics and that Streaming Trigger Graphs maintain both correctness and efficiency gains for streaming data."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Maps practical event languages to Temporal Datalog and introduces Streaming Trigger Graphs for efficient composite event recognition in data streams."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Mapping fragments of event languages to Temporal Datalog enables one uniform engine for composite event recognition in streams."}],"snapshot_sha256":"1a345d4e469c8a4c4414373978c9be2aa7d22be63ce27655d6cfe7befb5283eb"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"656722bac0a1e60a306e36adf0bf6a826b62e95389d95167b4a927bdfac3a5bd"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-20T15:39:49.890926Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-20T03:01:22.821412Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T16:21:02.960341Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.02488/integrity.json","findings":[],"snapshot_sha256":"b8cc2196d4db955ae11b33803658f3b31170858473245933fd287bf6cbf8fa6c","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbolic events. This demand has motivated the development of (i) event specification languages, which define composite events via temporal patterns over simpler events, and (ii) stream reasoning frameworks, evaluating patterns expressed in these languages. However, event specification languages are typically studied in isolation, complicating their comparison in terms of expressivity and obscuring the scope of their associated stream reasoners. To ","authors_text":"Periklis Mantenoglou","cross_cats":["cs.DB","cs.LO"],"headline":"Mapping fragments of event languages to Temporal Datalog enables one uniform engine for composite event recognition in streams.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-04T11:41:31Z","title":"Efficient Temporal Datalog Materialisation for Composite Event Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.02488","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-08T18:31:52.665245Z","id":"1070c4ad-4805-4001-87fd-88e640868955","model_set":{"reader":"grok-4.3"},"one_line_summary":"Maps practical event languages to Temporal Datalog and introduces Streaming Trigger Graphs for efficient composite event recognition in data streams.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Mapping fragments of event languages to Temporal Datalog enables one uniform engine for composite event recognition in streams.","strongest_claim":"Our approach yields a uniform composite event recognition mechanism that has the potential to generalise across a wide range of practical event specification languages.","weakest_assumption":"That the practical fragments of event specification languages can be mapped to Temporal Datalog->- while preserving their intended semantics and that Streaming Trigger Graphs maintain both correctness and efficiency gains for streaming data."}},"verdict_id":"1070c4ad-4805-4001-87fd-88e640868955"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:23c7f9b19f874f45d92aaf3422c70e46adfaec27088f12ad8a8264f9c334d866","target":"record","created_at":"2026-06-03T01:05:50Z","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":"ba9f47c0a7ad805ad5b1f69117a677c79c65f9ba477e7263113f84ede84e111f","cross_cats_sorted":["cs.DB","cs.LO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-04T11:41:31Z","title_canon_sha256":"c0a26d3c7dbc4b2ac25fe2c77b21b5431ba80aa54a1ee713979536686ccff7fe"},"schema_version":"1.0","source":{"id":"2605.02488","kind":"arxiv","version":2}},"canonical_sha256":"16e4b1282863e9a2baa6822b4a69f9ced9838f57195a3933e061d14ceff5d56b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"16e4b1282863e9a2baa6822b4a69f9ced9838f57195a3933e061d14ceff5d56b","first_computed_at":"2026-06-03T01:05:50.846600Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-03T01:05:50.846600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dxc3EWBx8UWmIuOTq0UaoRGXohRqceve7ZEJktJ/iqLuIc3joAPPeqKwQtCDm317Uh12LHR7na2Iboi6DGowCQ==","signature_status":"signed_v1","signed_at":"2026-06-03T01:05:50.847012Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.02488","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:23c7f9b19f874f45d92aaf3422c70e46adfaec27088f12ad8a8264f9c334d866","sha256:2e64a4b3cf84f42cbc61598b560528a69845569066262c3d3fccfce356e8c7de"],"state_sha256":"60f76c628d55150e571dc2ef5bae612f48d606ab650335df0767901dece51521"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SuPc8QuriR97+xabAAgBh+CVvGs9yxckUCFDDEewZte920K1zT5g6E/EtaNNxYhQh8HeKCjF0reSBGV+qtKPCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T20:57:33.275264Z","bundle_sha256":"e21ff80c8a81a56b4694459eb31c9909a81edb8ae1f634c18b3a35963d88c15a"}}