{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:44G3BYIKXVSN2HQJA6Y6EXY5GJ","short_pith_number":"pith:44G3BYIK","schema_version":"1.0","canonical_sha256":"e70db0e10abd64dd1e0907b1e25f1d3270b16668f58900029f39ce9ea5e46182","source":{"kind":"arxiv","id":"1012.5755","version":1},"attestation_state":"computed","paper":{"title":"DD-EbA: An algorithm for determining the number of neighbors in cost estimation by analogy using distance distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Lefteris Angelis, Makrina Viola Kosti, Nikolaos Mittas","submitted_at":"2010-12-28T13:12:14Z","abstract_excerpt":"Case Based Reasoning and particularly Estimation by Analogy, has been used in a number of problem-solving areas, such as cost estimation. Conventional methods, despite the lack of a sound criterion for choosing nearest projects, were based on estimation using a fixed and predetermined number of neighbors from the entire set of historical instances. This approach puts boundaries to the estimation ability of such algorithms, for they do not take into consideration that every project under estimation is unique and requires different handling. The notion of distributions of distances together with"},"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":"1012.5755","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2010-12-28T13:12:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"355ffe34f7843facb26eafa869e51ee7aabd22f5f36eccc7a0b5e3ed30966994","abstract_canon_sha256":"3589371c0035e1e2626d37769b84e7fc0bb63ddd6602b07c2eabe7dc135eef65"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:32:21.625113Z","signature_b64":"88JXTu0+8l5iggt3t6rIYcVBYbh3nl27dmFMmEdlTsAHc4mnGXSdDHFdd7rHvLPMhfks2oVnNoLq3L/NGg1gBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e70db0e10abd64dd1e0907b1e25f1d3270b16668f58900029f39ce9ea5e46182","last_reissued_at":"2026-05-18T04:32:21.624307Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:32:21.624307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DD-EbA: An algorithm for determining the number of neighbors in cost estimation by analogy using distance distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Lefteris Angelis, Makrina Viola Kosti, Nikolaos Mittas","submitted_at":"2010-12-28T13:12:14Z","abstract_excerpt":"Case Based Reasoning and particularly Estimation by Analogy, has been used in a number of problem-solving areas, such as cost estimation. Conventional methods, despite the lack of a sound criterion for choosing nearest projects, were based on estimation using a fixed and predetermined number of neighbors from the entire set of historical instances. This approach puts boundaries to the estimation ability of such algorithms, for they do not take into consideration that every project under estimation is unique and requires different handling. The notion of distributions of distances together with"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1012.5755","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":"1012.5755","created_at":"2026-05-18T04:32:21.624427+00:00"},{"alias_kind":"arxiv_version","alias_value":"1012.5755v1","created_at":"2026-05-18T04:32:21.624427+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1012.5755","created_at":"2026-05-18T04:32:21.624427+00:00"},{"alias_kind":"pith_short_12","alias_value":"44G3BYIKXVSN","created_at":"2026-05-18T12:26:03.138858+00:00"},{"alias_kind":"pith_short_16","alias_value":"44G3BYIKXVSN2HQJ","created_at":"2026-05-18T12:26:03.138858+00:00"},{"alias_kind":"pith_short_8","alias_value":"44G3BYIK","created_at":"2026-05-18T12:26:03.138858+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/44G3BYIKXVSN2HQJA6Y6EXY5GJ","json":"https://pith.science/pith/44G3BYIKXVSN2HQJA6Y6EXY5GJ.json","graph_json":"https://pith.science/api/pith-number/44G3BYIKXVSN2HQJA6Y6EXY5GJ/graph.json","events_json":"https://pith.science/api/pith-number/44G3BYIKXVSN2HQJA6Y6EXY5GJ/events.json","paper":"https://pith.science/paper/44G3BYIK"},"agent_actions":{"view_html":"https://pith.science/pith/44G3BYIKXVSN2HQJA6Y6EXY5GJ","download_json":"https://pith.science/pith/44G3BYIKXVSN2HQJA6Y6EXY5GJ.json","view_paper":"https://pith.science/paper/44G3BYIK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1012.5755&json=true","fetch_graph":"https://pith.science/api/pith-number/44G3BYIKXVSN2HQJA6Y6EXY5GJ/graph.json","fetch_events":"https://pith.science/api/pith-number/44G3BYIKXVSN2HQJA6Y6EXY5GJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/44G3BYIKXVSN2HQJA6Y6EXY5GJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/44G3BYIKXVSN2HQJA6Y6EXY5GJ/action/storage_attestation","attest_author":"https://pith.science/pith/44G3BYIKXVSN2HQJA6Y6EXY5GJ/action/author_attestation","sign_citation":"https://pith.science/pith/44G3BYIKXVSN2HQJA6Y6EXY5GJ/action/citation_signature","submit_replication":"https://pith.science/pith/44G3BYIKXVSN2HQJA6Y6EXY5GJ/action/replication_record"}},"created_at":"2026-05-18T04:32:21.624427+00:00","updated_at":"2026-05-18T04:32:21.624427+00:00"}