{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:FTLAQSO2W7URXYWJNI5NUBDC54","short_pith_number":"pith:FTLAQSO2","schema_version":"1.0","canonical_sha256":"2cd60849dab7e91be2c96a3ada0462ef33e5a098ccf08d0270d4ee9a1b3c644e","source":{"kind":"arxiv","id":"1802.08254","version":2},"attestation_state":"computed","paper":{"title":"BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.PF"],"primary_cat":"cs.DC","authors_text":"Chen Zheng, Chunjie Luo, Daoyi Zheng, Hainan Ye, Haoning Tang, Jiahui Dai, Jianfeng Zhan, Lei Wang, Rui Ren, Shujie Zhang, Wanling Gao, Xiwen He, Xu Wen, Zheng Cao","submitted_at":"2018-02-23T01:28:44Z","abstract_excerpt":"Several fundamental changes in technology indicate domain-specific hardware and software co-design is the only path left. In this context, architecture, system, data management, and machine learning communities pay greater attention to innovative big data and AI algorithms, architecture, and systems. Unfortunately, complexity, diversity, frequently-changed workloads, and rapid evolution of big data and AI systems raise great challenges. First, the traditional benchmarking methodology that creates a new benchmark or proxy for every possible workload is not scalable, or even impossible for Big D"},"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":"1802.08254","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-02-23T01:28:44Z","cross_cats_sorted":["cs.AI","cs.PF"],"title_canon_sha256":"75a50a2cdf540736eee5fcbe9d3089abc4c1dcc3d48e9cadeedc5ead30fa3231","abstract_canon_sha256":"4ac41232acbd2cf4bc968036e044e5dfc95900c4339487b41736b8123d8ebd49"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:08.743643Z","signature_b64":"Sm8m3aLaKnmpgB41digjx0ruB8rcOQCeZbn8yNLbIUG/CQ6Bf3MBaIHeaUborIJi65twa6+eoAN0JqpMj+aHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cd60849dab7e91be2c96a3ada0462ef33e5a098ccf08d0270d4ee9a1b3c644e","last_reissued_at":"2026-05-18T00:00:08.743187Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:08.743187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.PF"],"primary_cat":"cs.DC","authors_text":"Chen Zheng, Chunjie Luo, Daoyi Zheng, Hainan Ye, Haoning Tang, Jiahui Dai, Jianfeng Zhan, Lei Wang, Rui Ren, Shujie Zhang, Wanling Gao, Xiwen He, Xu Wen, Zheng Cao","submitted_at":"2018-02-23T01:28:44Z","abstract_excerpt":"Several fundamental changes in technology indicate domain-specific hardware and software co-design is the only path left. In this context, architecture, system, data management, and machine learning communities pay greater attention to innovative big data and AI algorithms, architecture, and systems. Unfortunately, complexity, diversity, frequently-changed workloads, and rapid evolution of big data and AI systems raise great challenges. First, the traditional benchmarking methodology that creates a new benchmark or proxy for every possible workload is not scalable, or even impossible for Big D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08254","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1802.08254","created_at":"2026-05-18T00:00:08.743253+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.08254v2","created_at":"2026-05-18T00:00:08.743253+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08254","created_at":"2026-05-18T00:00:08.743253+00:00"},{"alias_kind":"pith_short_12","alias_value":"FTLAQSO2W7UR","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"FTLAQSO2W7URXYWJ","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"FTLAQSO2","created_at":"2026-05-18T12:32:25.280505+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.04565","citing_title":"From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems","ref_index":46,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FTLAQSO2W7URXYWJNI5NUBDC54","json":"https://pith.science/pith/FTLAQSO2W7URXYWJNI5NUBDC54.json","graph_json":"https://pith.science/api/pith-number/FTLAQSO2W7URXYWJNI5NUBDC54/graph.json","events_json":"https://pith.science/api/pith-number/FTLAQSO2W7URXYWJNI5NUBDC54/events.json","paper":"https://pith.science/paper/FTLAQSO2"},"agent_actions":{"view_html":"https://pith.science/pith/FTLAQSO2W7URXYWJNI5NUBDC54","download_json":"https://pith.science/pith/FTLAQSO2W7URXYWJNI5NUBDC54.json","view_paper":"https://pith.science/paper/FTLAQSO2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.08254&json=true","fetch_graph":"https://pith.science/api/pith-number/FTLAQSO2W7URXYWJNI5NUBDC54/graph.json","fetch_events":"https://pith.science/api/pith-number/FTLAQSO2W7URXYWJNI5NUBDC54/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FTLAQSO2W7URXYWJNI5NUBDC54/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FTLAQSO2W7URXYWJNI5NUBDC54/action/storage_attestation","attest_author":"https://pith.science/pith/FTLAQSO2W7URXYWJNI5NUBDC54/action/author_attestation","sign_citation":"https://pith.science/pith/FTLAQSO2W7URXYWJNI5NUBDC54/action/citation_signature","submit_replication":"https://pith.science/pith/FTLAQSO2W7URXYWJNI5NUBDC54/action/replication_record"}},"created_at":"2026-05-18T00:00:08.743253+00:00","updated_at":"2026-05-18T00:00:08.743253+00:00"}