{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:B6HQ4U2Y4346Z3HFDCQUJSMQBK","short_pith_number":"pith:B6HQ4U2Y","schema_version":"1.0","canonical_sha256":"0f8f0e5358e6f9ecece518a144c9900a9a89f0a641f1c6be4c6a783516769fe8","source":{"kind":"arxiv","id":"1207.7055","version":1},"attestation_state":"computed","paper":{"title":"Optimizing MapReduce for Highly Distributed Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Abhishek Chandra, Benjamin Heintz, Ramesh K. Sitaraman","submitted_at":"2012-07-30T19:42:31Z","abstract_excerpt":"MapReduce, the popular programming paradigm for large-scale data processing, has traditionally been deployed over tightly-coupled clusters where the data is already locally available. The assumption that the data and compute resources are available in a single central location, however, no longer holds for many emerging applications in commercial, scientific and social networking domains, where the data is generated in a geographically distributed manner. Further, the computational resources needed for carrying out the data analysis may be distributed across multiple data centers or community "},"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":"1207.7055","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2012-07-30T19:42:31Z","cross_cats_sorted":[],"title_canon_sha256":"d41b0fd22c8e80e43f8ce938a270e204e00678c71532da75df599da3af561749","abstract_canon_sha256":"7688bf03024c9ab299ace8a00dbd3bff9ea8fdb0d329532db058dd944089437c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:49:47.872548Z","signature_b64":"IJeyhDjnK7RNmqiuOWPOeNhpnH1Sfpm/4euZY5qmAjR8MXBffpdmSFmDPSNUmbdLEqaz+S+BVeuDSdt1ZuJxDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f8f0e5358e6f9ecece518a144c9900a9a89f0a641f1c6be4c6a783516769fe8","last_reissued_at":"2026-05-18T03:49:47.871879Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:49:47.871879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing MapReduce for Highly Distributed Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Abhishek Chandra, Benjamin Heintz, Ramesh K. Sitaraman","submitted_at":"2012-07-30T19:42:31Z","abstract_excerpt":"MapReduce, the popular programming paradigm for large-scale data processing, has traditionally been deployed over tightly-coupled clusters where the data is already locally available. The assumption that the data and compute resources are available in a single central location, however, no longer holds for many emerging applications in commercial, scientific and social networking domains, where the data is generated in a geographically distributed manner. Further, the computational resources needed for carrying out the data analysis may be distributed across multiple data centers or community "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1207.7055","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":"1207.7055","created_at":"2026-05-18T03:49:47.871967+00:00"},{"alias_kind":"arxiv_version","alias_value":"1207.7055v1","created_at":"2026-05-18T03:49:47.871967+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1207.7055","created_at":"2026-05-18T03:49:47.871967+00:00"},{"alias_kind":"pith_short_12","alias_value":"B6HQ4U2Y4346","created_at":"2026-05-18T12:26:58.693483+00:00"},{"alias_kind":"pith_short_16","alias_value":"B6HQ4U2Y4346Z3HF","created_at":"2026-05-18T12:26:58.693483+00:00"},{"alias_kind":"pith_short_8","alias_value":"B6HQ4U2Y","created_at":"2026-05-18T12:26:58.693483+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/B6HQ4U2Y4346Z3HFDCQUJSMQBK","json":"https://pith.science/pith/B6HQ4U2Y4346Z3HFDCQUJSMQBK.json","graph_json":"https://pith.science/api/pith-number/B6HQ4U2Y4346Z3HFDCQUJSMQBK/graph.json","events_json":"https://pith.science/api/pith-number/B6HQ4U2Y4346Z3HFDCQUJSMQBK/events.json","paper":"https://pith.science/paper/B6HQ4U2Y"},"agent_actions":{"view_html":"https://pith.science/pith/B6HQ4U2Y4346Z3HFDCQUJSMQBK","download_json":"https://pith.science/pith/B6HQ4U2Y4346Z3HFDCQUJSMQBK.json","view_paper":"https://pith.science/paper/B6HQ4U2Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1207.7055&json=true","fetch_graph":"https://pith.science/api/pith-number/B6HQ4U2Y4346Z3HFDCQUJSMQBK/graph.json","fetch_events":"https://pith.science/api/pith-number/B6HQ4U2Y4346Z3HFDCQUJSMQBK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B6HQ4U2Y4346Z3HFDCQUJSMQBK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B6HQ4U2Y4346Z3HFDCQUJSMQBK/action/storage_attestation","attest_author":"https://pith.science/pith/B6HQ4U2Y4346Z3HFDCQUJSMQBK/action/author_attestation","sign_citation":"https://pith.science/pith/B6HQ4U2Y4346Z3HFDCQUJSMQBK/action/citation_signature","submit_replication":"https://pith.science/pith/B6HQ4U2Y4346Z3HFDCQUJSMQBK/action/replication_record"}},"created_at":"2026-05-18T03:49:47.871967+00:00","updated_at":"2026-05-18T03:49:47.871967+00:00"}