{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:CQAWLNGHC6GOJKZXUBPDNFPZUS","short_pith_number":"pith:CQAWLNGH","canonical_record":{"source":{"id":"1808.08877","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-08-27T15:11:28Z","cross_cats_sorted":[],"title_canon_sha256":"14420f88ef3e2dc7965f91864e2dc9e3f81c1aa24fe199598502dab37a33de02","abstract_canon_sha256":"0ae9d88bd32d000e53f5f6bed2c61722928e88226bd8388b9e1254dfbff65194"},"schema_version":"1.0"},"canonical_sha256":"140165b4c7178ce4ab37a05e3695f9a4855bb836e8f704413311a271e28292f5","source":{"kind":"arxiv","id":"1808.08877","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.08877","created_at":"2026-05-18T00:03:45Z"},{"alias_kind":"arxiv_version","alias_value":"1808.08877v2","created_at":"2026-05-18T00:03:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.08877","created_at":"2026-05-18T00:03:45Z"},{"alias_kind":"pith_short_12","alias_value":"CQAWLNGHC6GO","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CQAWLNGHC6GOJKZX","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CQAWLNGH","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:CQAWLNGHC6GOJKZXUBPDNFPZUS","target":"record","payload":{"canonical_record":{"source":{"id":"1808.08877","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-08-27T15:11:28Z","cross_cats_sorted":[],"title_canon_sha256":"14420f88ef3e2dc7965f91864e2dc9e3f81c1aa24fe199598502dab37a33de02","abstract_canon_sha256":"0ae9d88bd32d000e53f5f6bed2c61722928e88226bd8388b9e1254dfbff65194"},"schema_version":"1.0"},"canonical_sha256":"140165b4c7178ce4ab37a05e3695f9a4855bb836e8f704413311a271e28292f5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:45.377022Z","signature_b64":"Pge39xjfOjF/Sdau7mIeFlgTQOo0KMlbDXz0ID8gaLwfINmUmipGW4ryDtSs3NobJoz3msL2CRwPpMktxS+FAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"140165b4c7178ce4ab37a05e3695f9a4855bb836e8f704413311a271e28292f5","last_reissued_at":"2026-05-18T00:03:45.376538Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:45.376538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.08877","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":"2026-05-18T00:03:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RR3CLb2RLPiKVAp5eyZics+bwqNrVTMRqL0fVqjCztoytfxKya/JM43tdS/wavtt9q3TvbBjuuijs3lkRXo9Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T01:28:33.067418Z"},"content_sha256":"290fa431d82a83ee4d14d8320a9c40edf9371bf4276b37b7eb9c96d46cee78c6","schema_version":"1.0","event_id":"sha256:290fa431d82a83ee4d14d8320a9c40edf9371bf4276b37b7eb9c96d46cee78c6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:CQAWLNGHC6GOJKZXUBPDNFPZUS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Piecewise Linear Approximation in Data Streaming: Algorithmic Implementations and Experimental Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Marina Papatriantafilou, Romaric Duvignau, Vincenzo Gulisano, Vladimir Savic","submitted_at":"2018-08-27T15:11:28Z","abstract_excerpt":"Piecewise Linear Approximation (PLA) is a well-established tool to reduce the size of the representation of time series by approximating the series by a sequence of line segments while keeping the error introduced by the approximation within some predetermined threshold. With the recent rise of edge computing, PLA algorithms find a complete new set of applications with the emphasis on reducing the volume of streamed data. In this study, we identify two scenarios set in a data-stream processing context: data reduction in sensor transmissions and datacenter storage. In connection to those scenar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.08877","kind":"arxiv","version":2},"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":"2026-05-18T00:03:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CwfQeuTMzZJzwtEGggUMLAqiPJzeks8H0bEeKLSOD9w4LFg2JaUgw0P8JHX5udi93FhmrZCV99hw3OoUrT9FCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T01:28:33.067763Z"},"content_sha256":"f144224a6c566cbf5276b799e69ca28c49a5b5e69d76dfd3ec42a9facb5a3532","schema_version":"1.0","event_id":"sha256:f144224a6c566cbf5276b799e69ca28c49a5b5e69d76dfd3ec42a9facb5a3532"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CQAWLNGHC6GOJKZXUBPDNFPZUS/bundle.json","state_url":"https://pith.science/pith/CQAWLNGHC6GOJKZXUBPDNFPZUS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CQAWLNGHC6GOJKZXUBPDNFPZUS/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":"2026-06-03T01:28:33Z","links":{"resolver":"https://pith.science/pith/CQAWLNGHC6GOJKZXUBPDNFPZUS","bundle":"https://pith.science/pith/CQAWLNGHC6GOJKZXUBPDNFPZUS/bundle.json","state":"https://pith.science/pith/CQAWLNGHC6GOJKZXUBPDNFPZUS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CQAWLNGHC6GOJKZXUBPDNFPZUS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:CQAWLNGHC6GOJKZXUBPDNFPZUS","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":"0ae9d88bd32d000e53f5f6bed2c61722928e88226bd8388b9e1254dfbff65194","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-08-27T15:11:28Z","title_canon_sha256":"14420f88ef3e2dc7965f91864e2dc9e3f81c1aa24fe199598502dab37a33de02"},"schema_version":"1.0","source":{"id":"1808.08877","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.08877","created_at":"2026-05-18T00:03:45Z"},{"alias_kind":"arxiv_version","alias_value":"1808.08877v2","created_at":"2026-05-18T00:03:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.08877","created_at":"2026-05-18T00:03:45Z"},{"alias_kind":"pith_short_12","alias_value":"CQAWLNGHC6GO","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CQAWLNGHC6GOJKZX","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CQAWLNGH","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:f144224a6c566cbf5276b799e69ca28c49a5b5e69d76dfd3ec42a9facb5a3532","target":"graph","created_at":"2026-05-18T00:03:45Z","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":"Piecewise Linear Approximation (PLA) is a well-established tool to reduce the size of the representation of time series by approximating the series by a sequence of line segments while keeping the error introduced by the approximation within some predetermined threshold. With the recent rise of edge computing, PLA algorithms find a complete new set of applications with the emphasis on reducing the volume of streamed data. In this study, we identify two scenarios set in a data-stream processing context: data reduction in sensor transmissions and datacenter storage. In connection to those scenar","authors_text":"Marina Papatriantafilou, Romaric Duvignau, Vincenzo Gulisano, Vladimir Savic","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-08-27T15:11:28Z","title":"Piecewise Linear Approximation in Data Streaming: Algorithmic Implementations and Experimental Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.08877","kind":"arxiv","version":2},"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":"sha256:290fa431d82a83ee4d14d8320a9c40edf9371bf4276b37b7eb9c96d46cee78c6","target":"record","created_at":"2026-05-18T00:03:45Z","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":"0ae9d88bd32d000e53f5f6bed2c61722928e88226bd8388b9e1254dfbff65194","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-08-27T15:11:28Z","title_canon_sha256":"14420f88ef3e2dc7965f91864e2dc9e3f81c1aa24fe199598502dab37a33de02"},"schema_version":"1.0","source":{"id":"1808.08877","kind":"arxiv","version":2}},"canonical_sha256":"140165b4c7178ce4ab37a05e3695f9a4855bb836e8f704413311a271e28292f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"140165b4c7178ce4ab37a05e3695f9a4855bb836e8f704413311a271e28292f5","first_computed_at":"2026-05-18T00:03:45.376538Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:03:45.376538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pge39xjfOjF/Sdau7mIeFlgTQOo0KMlbDXz0ID8gaLwfINmUmipGW4ryDtSs3NobJoz3msL2CRwPpMktxS+FAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:03:45.377022Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.08877","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:290fa431d82a83ee4d14d8320a9c40edf9371bf4276b37b7eb9c96d46cee78c6","sha256:f144224a6c566cbf5276b799e69ca28c49a5b5e69d76dfd3ec42a9facb5a3532"],"state_sha256":"4d536ed050ecbfd75e1384b8cd80388d603345a6447e42d4d650357643b762f1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZHlWPBVLbpOAf5Ou3MgwtbCbFutncMD80QX1CYwI8+8iVDjn7m52iUINWaMivd/SVvygT2ii8Ja7Locs2KFVBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T01:28:33.069653Z","bundle_sha256":"f121b25ffd67bccfc3626543b7f4fdc40898b0b2e60ea757fa2649555ab2273f"}}