{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:RIG4SHFDIIJV7RGSPUMXFXH32W","short_pith_number":"pith:RIG4SHFD","schema_version":"1.0","canonical_sha256":"8a0dc91ca342135fc4d27d1972dcfbd58493356f86bd06e8e518c7e7e744d4d9","source":{"kind":"arxiv","id":"1704.07852","version":1},"attestation_state":"computed","paper":{"title":"Sub-string/Pattern Matching in Sub-linear Time Using a Sparse Fourier Transform Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","math.IT"],"primary_cat":"cs.IT","authors_text":"Avinash Vem, Jean-Francois Chamberland, Krishna R. Narayanan, Nagaraj T. Janakiraman","submitted_at":"2017-04-25T18:17:07Z","abstract_excerpt":"We consider the problem of querying a string (or, a database) of length $N$ bits to determine all the locations where a substring (query) of length $M$ appears either exactly or is within a Hamming distance of $K$ from the query. We assume that sketches of the original signal can be computed off line and stored. Using the sparse Fourier transform computation based approach introduced by Pawar and Ramchandran, we show that all such matches can be determined with high probability in sub-linear time. Specifically, if the query length $M = O(N^\\mu)$ and the number of matches $L=O(N^\\lambda)$, we s"},"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":"1704.07852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-04-25T18:17:07Z","cross_cats_sorted":["cs.DS","math.IT"],"title_canon_sha256":"3c0024f23909ddf6731914937fea6a41cebef5e6acb603bf366b64bd4fb16c61","abstract_canon_sha256":"07ca3fe210991d80164e4cd8c94c2a0a61edc307cb5feb0d9ab764bbb5a876f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:31.710105Z","signature_b64":"sCeVToNMCxohOMB/0gXlUHRRaXyKhszDrf5iIE1WGHsQRFM9AD7OyO7UKIetRk4u8cYkMRYI/ifVUCizuDCiCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a0dc91ca342135fc4d27d1972dcfbd58493356f86bd06e8e518c7e7e744d4d9","last_reissued_at":"2026-05-18T00:45:31.709551Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:31.709551Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sub-string/Pattern Matching in Sub-linear Time Using a Sparse Fourier Transform Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","math.IT"],"primary_cat":"cs.IT","authors_text":"Avinash Vem, Jean-Francois Chamberland, Krishna R. Narayanan, Nagaraj T. Janakiraman","submitted_at":"2017-04-25T18:17:07Z","abstract_excerpt":"We consider the problem of querying a string (or, a database) of length $N$ bits to determine all the locations where a substring (query) of length $M$ appears either exactly or is within a Hamming distance of $K$ from the query. We assume that sketches of the original signal can be computed off line and stored. Using the sparse Fourier transform computation based approach introduced by Pawar and Ramchandran, we show that all such matches can be determined with high probability in sub-linear time. Specifically, if the query length $M = O(N^\\mu)$ and the number of matches $L=O(N^\\lambda)$, we s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.07852","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":"1704.07852","created_at":"2026-05-18T00:45:31.709632+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.07852v1","created_at":"2026-05-18T00:45:31.709632+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.07852","created_at":"2026-05-18T00:45:31.709632+00:00"},{"alias_kind":"pith_short_12","alias_value":"RIG4SHFDIIJV","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"RIG4SHFDIIJV7RGS","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"RIG4SHFD","created_at":"2026-05-18T12:31:39.905425+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/RIG4SHFDIIJV7RGSPUMXFXH32W","json":"https://pith.science/pith/RIG4SHFDIIJV7RGSPUMXFXH32W.json","graph_json":"https://pith.science/api/pith-number/RIG4SHFDIIJV7RGSPUMXFXH32W/graph.json","events_json":"https://pith.science/api/pith-number/RIG4SHFDIIJV7RGSPUMXFXH32W/events.json","paper":"https://pith.science/paper/RIG4SHFD"},"agent_actions":{"view_html":"https://pith.science/pith/RIG4SHFDIIJV7RGSPUMXFXH32W","download_json":"https://pith.science/pith/RIG4SHFDIIJV7RGSPUMXFXH32W.json","view_paper":"https://pith.science/paper/RIG4SHFD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.07852&json=true","fetch_graph":"https://pith.science/api/pith-number/RIG4SHFDIIJV7RGSPUMXFXH32W/graph.json","fetch_events":"https://pith.science/api/pith-number/RIG4SHFDIIJV7RGSPUMXFXH32W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RIG4SHFDIIJV7RGSPUMXFXH32W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RIG4SHFDIIJV7RGSPUMXFXH32W/action/storage_attestation","attest_author":"https://pith.science/pith/RIG4SHFDIIJV7RGSPUMXFXH32W/action/author_attestation","sign_citation":"https://pith.science/pith/RIG4SHFDIIJV7RGSPUMXFXH32W/action/citation_signature","submit_replication":"https://pith.science/pith/RIG4SHFDIIJV7RGSPUMXFXH32W/action/replication_record"}},"created_at":"2026-05-18T00:45:31.709632+00:00","updated_at":"2026-05-18T00:45:31.709632+00:00"}