{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:DQJJB7GIHVXPBZVFKRNX2C6NY2","short_pith_number":"pith:DQJJB7GI","canonical_record":{"source":{"id":"1509.00504","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2015-08-28T22:36:32Z","cross_cats_sorted":[],"title_canon_sha256":"791972c1366f1018eb79076310c29bf33f25cf2990c6c6745a1a7dbbbcdcaf5c","abstract_canon_sha256":"f3694245ba40eeba46edf8d9a2d1a4bfacdd4f67166f6a126683c67907aeb417"},"schema_version":"1.0"},"canonical_sha256":"1c1290fcc83d6ef0e6a5545b7d0bcdc6826bb1f5b0f3ebcb57661a0d80138217","source":{"kind":"arxiv","id":"1509.00504","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.00504","created_at":"2026-05-18T00:52:17Z"},{"alias_kind":"arxiv_version","alias_value":"1509.00504v1","created_at":"2026-05-18T00:52:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.00504","created_at":"2026-05-18T00:52:17Z"},{"alias_kind":"pith_short_12","alias_value":"DQJJB7GIHVXP","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_16","alias_value":"DQJJB7GIHVXPBZVF","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_8","alias_value":"DQJJB7GI","created_at":"2026-05-18T12:29:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:DQJJB7GIHVXPBZVFKRNX2C6NY2","target":"record","payload":{"canonical_record":{"source":{"id":"1509.00504","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2015-08-28T22:36:32Z","cross_cats_sorted":[],"title_canon_sha256":"791972c1366f1018eb79076310c29bf33f25cf2990c6c6745a1a7dbbbcdcaf5c","abstract_canon_sha256":"f3694245ba40eeba46edf8d9a2d1a4bfacdd4f67166f6a126683c67907aeb417"},"schema_version":"1.0"},"canonical_sha256":"1c1290fcc83d6ef0e6a5545b7d0bcdc6826bb1f5b0f3ebcb57661a0d80138217","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:52:17.699183Z","signature_b64":"ASK5ZCwyxOa6Dt2XUp7FKLIDe2oTZaYLJ5jNfhAv5CIXgNMMj9d0wxICGGq2I8s/HIZGim/yLjCxGQuORv9+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c1290fcc83d6ef0e6a5545b7d0bcdc6826bb1f5b0f3ebcb57661a0d80138217","last_reissued_at":"2026-05-18T00:52:17.698664Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:52:17.698664Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1509.00504","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":"2026-05-18T00:52:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ga+PpnO9JdSsQTAEUHbN2P0X3R/jnSS1QuNNVRcm20LYHJgpWHZmv2cY6rqfiU4Kf3/FQ4BUk2Frf1kfEoRVCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T18:10:15.758004Z"},"content_sha256":"1335e0a9f9f1ab6eb231a282c44e0c5a0f9d8651f08cc0b606e8e0e762b75495","schema_version":"1.0","event_id":"sha256:1335e0a9f9f1ab6eb231a282c44e0c5a0f9d8651f08cc0b606e8e0e762b75495"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:DQJJB7GIHVXPBZVFKRNX2C6NY2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Using a Power Law Distribution to describe Big Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Jeremy Kepner, Vijay Gadepally","submitted_at":"2015-08-28T22:36:32Z","abstract_excerpt":"The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This difficulty often wastes valuable researcher and computational time by expending resources on uninteresting parts of data. Social sensors, or sensors which produce data based on human activity, such as Wikipedia, Twitter, and Facebook have an underlying structure which can be t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.00504","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:52:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sDbTxt2KLVKIGbDnMk6o/yhr4ROTqxHIMhhJhg6njFZIlhs1J8XZFmDjR4rQj3NHuyS4l9RvM6u33dDql/LvAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T18:10:15.758633Z"},"content_sha256":"ebb654b27af522141419b8742f0353f11ace2f24efd1d107bc37a9917eb41afa","schema_version":"1.0","event_id":"sha256:ebb654b27af522141419b8742f0353f11ace2f24efd1d107bc37a9917eb41afa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DQJJB7GIHVXPBZVFKRNX2C6NY2/bundle.json","state_url":"https://pith.science/pith/DQJJB7GIHVXPBZVFKRNX2C6NY2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DQJJB7GIHVXPBZVFKRNX2C6NY2/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":"2026-06-06T18:10:15Z","links":{"resolver":"https://pith.science/pith/DQJJB7GIHVXPBZVFKRNX2C6NY2","bundle":"https://pith.science/pith/DQJJB7GIHVXPBZVFKRNX2C6NY2/bundle.json","state":"https://pith.science/pith/DQJJB7GIHVXPBZVFKRNX2C6NY2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DQJJB7GIHVXPBZVFKRNX2C6NY2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:DQJJB7GIHVXPBZVFKRNX2C6NY2","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":"f3694245ba40eeba46edf8d9a2d1a4bfacdd4f67166f6a126683c67907aeb417","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2015-08-28T22:36:32Z","title_canon_sha256":"791972c1366f1018eb79076310c29bf33f25cf2990c6c6745a1a7dbbbcdcaf5c"},"schema_version":"1.0","source":{"id":"1509.00504","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.00504","created_at":"2026-05-18T00:52:17Z"},{"alias_kind":"arxiv_version","alias_value":"1509.00504v1","created_at":"2026-05-18T00:52:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.00504","created_at":"2026-05-18T00:52:17Z"},{"alias_kind":"pith_short_12","alias_value":"DQJJB7GIHVXP","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_16","alias_value":"DQJJB7GIHVXPBZVF","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_8","alias_value":"DQJJB7GI","created_at":"2026-05-18T12:29:17Z"}],"graph_snapshots":[{"event_id":"sha256:ebb654b27af522141419b8742f0353f11ace2f24efd1d107bc37a9917eb41afa","target":"graph","created_at":"2026-05-18T00:52:17Z","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":"The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This difficulty often wastes valuable researcher and computational time by expending resources on uninteresting parts of data. Social sensors, or sensors which produce data based on human activity, such as Wikipedia, Twitter, and Facebook have an underlying structure which can be t","authors_text":"Jeremy Kepner, Vijay Gadepally","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2015-08-28T22:36:32Z","title":"Using a Power Law Distribution to describe Big Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.00504","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":"sha256:1335e0a9f9f1ab6eb231a282c44e0c5a0f9d8651f08cc0b606e8e0e762b75495","target":"record","created_at":"2026-05-18T00:52:17Z","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":"f3694245ba40eeba46edf8d9a2d1a4bfacdd4f67166f6a126683c67907aeb417","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2015-08-28T22:36:32Z","title_canon_sha256":"791972c1366f1018eb79076310c29bf33f25cf2990c6c6745a1a7dbbbcdcaf5c"},"schema_version":"1.0","source":{"id":"1509.00504","kind":"arxiv","version":1}},"canonical_sha256":"1c1290fcc83d6ef0e6a5545b7d0bcdc6826bb1f5b0f3ebcb57661a0d80138217","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c1290fcc83d6ef0e6a5545b7d0bcdc6826bb1f5b0f3ebcb57661a0d80138217","first_computed_at":"2026-05-18T00:52:17.698664Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:52:17.698664Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ASK5ZCwyxOa6Dt2XUp7FKLIDe2oTZaYLJ5jNfhAv5CIXgNMMj9d0wxICGGq2I8s/HIZGim/yLjCxGQuORv9+Cw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:52:17.699183Z","signed_message":"canonical_sha256_bytes"},"source_id":"1509.00504","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1335e0a9f9f1ab6eb231a282c44e0c5a0f9d8651f08cc0b606e8e0e762b75495","sha256:ebb654b27af522141419b8742f0353f11ace2f24efd1d107bc37a9917eb41afa"],"state_sha256":"08ac733e812c048f5dd2303943aff6d1d5e0b5b1ae7416e2b140e02a0aa579d5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q2vlayh7AxS1ofsQKErDHOhEBHzk9i3Ws/iseO2PLK4O3A7n7h8pNDjTDEG7cq8fa47jSbkG7VfuSb5bj5EsDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T18:10:15.761873Z","bundle_sha256":"57d9c5df64887d30e2bdcaf4b2311b351b13dcb5a50c0ed3610549e2a13a0aa8"}}