{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:NSEIBGPOITERDT5YE3PGX36I44","short_pith_number":"pith:NSEIBGPO","canonical_record":{"source":{"id":"1609.06221","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-09-20T15:26:37Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"01015fe1284d12ff5b08efefad324c3b44a9160250d2e0f6a995040cfb455942","abstract_canon_sha256":"588338ee16620bb816166913420e215326adc0c6edb3c7ee25c4b8bc2ccb6eec"},"schema_version":"1.0"},"canonical_sha256":"6c888099ee44c911cfb826de6befc8e714b891d8dddfc6612849fa4ac85dcfa5","source":{"kind":"arxiv","id":"1609.06221","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.06221","created_at":"2026-05-18T01:04:16Z"},{"alias_kind":"arxiv_version","alias_value":"1609.06221v1","created_at":"2026-05-18T01:04:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.06221","created_at":"2026-05-18T01:04:16Z"},{"alias_kind":"pith_short_12","alias_value":"NSEIBGPOITER","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"NSEIBGPOITERDT5Y","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"NSEIBGPO","created_at":"2026-05-18T12:30:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:NSEIBGPOITERDT5YE3PGX36I44","target":"record","payload":{"canonical_record":{"source":{"id":"1609.06221","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-09-20T15:26:37Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"01015fe1284d12ff5b08efefad324c3b44a9160250d2e0f6a995040cfb455942","abstract_canon_sha256":"588338ee16620bb816166913420e215326adc0c6edb3c7ee25c4b8bc2ccb6eec"},"schema_version":"1.0"},"canonical_sha256":"6c888099ee44c911cfb826de6befc8e714b891d8dddfc6612849fa4ac85dcfa5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:04:16.384899Z","signature_b64":"1NG8GBskT8wvmmgUwmVWhfgu9xpO3qbN6bYdVGWjsef5Y0uKNzERJrdLVkEI89euQuVAVEc1zbgmfB1aW9rzDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c888099ee44c911cfb826de6befc8e714b891d8dddfc6612849fa4ac85dcfa5","last_reissued_at":"2026-05-18T01:04:16.384437Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:04:16.384437Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1609.06221","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:04:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZLgx8Ec0RO04hAKI3ozLoVhyAkM1wsqXwAr78xxwCK+Uo5Gk9WPP7jq991XJ/9pHATDB1hUpvUlUtM/vKiBlDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T18:27:47.568557Z"},"content_sha256":"e2c294863a9f2ffb0959136aad263e08f1f92e4709ceb3089313367b40018c9b","schema_version":"1.0","event_id":"sha256:e2c294863a9f2ffb0959136aad263e08f1f92e4709ceb3089313367b40018c9b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:NSEIBGPOITERDT5YE3PGX36I44","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Efficient Method of Partitioning High Volumes of Multidimensional Data for Parallel Clustering Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.AI","authors_text":"Avnish Chandra Suman, Saraswati Mishra","submitted_at":"2016-09-20T15:26:37Z","abstract_excerpt":"An optimal data partitioning in parallel & distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of affected points and better & faster merging. Though partitioning using Kd Tree is being conventionally used in academia, it suffers from performance drenches and bias (non equal distribution) as dimensionality of data increases and hence is not suitable for practical use in industry where dimensionality can be of order of 100s to 1000s. To address these issues we propose two new partitioning techni"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.06221","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:04:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ohjiEglkBkQq3GJr++5Y4C0cXQMhPMUwDITJY5l7NDR/QtoT5Dl0OTc++gyWgaJspHZP3y/53RSv8821qh1oAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T18:27:47.569206Z"},"content_sha256":"31040064d88718cc079660c10591bfff64f227326b9580d5a50e55864fc5a128","schema_version":"1.0","event_id":"sha256:31040064d88718cc079660c10591bfff64f227326b9580d5a50e55864fc5a128"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NSEIBGPOITERDT5YE3PGX36I44/bundle.json","state_url":"https://pith.science/pith/NSEIBGPOITERDT5YE3PGX36I44/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NSEIBGPOITERDT5YE3PGX36I44/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":"2026-06-08T18:27:47Z","links":{"resolver":"https://pith.science/pith/NSEIBGPOITERDT5YE3PGX36I44","bundle":"https://pith.science/pith/NSEIBGPOITERDT5YE3PGX36I44/bundle.json","state":"https://pith.science/pith/NSEIBGPOITERDT5YE3PGX36I44/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NSEIBGPOITERDT5YE3PGX36I44/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:NSEIBGPOITERDT5YE3PGX36I44","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":"588338ee16620bb816166913420e215326adc0c6edb3c7ee25c4b8bc2ccb6eec","cross_cats_sorted":["cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-09-20T15:26:37Z","title_canon_sha256":"01015fe1284d12ff5b08efefad324c3b44a9160250d2e0f6a995040cfb455942"},"schema_version":"1.0","source":{"id":"1609.06221","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.06221","created_at":"2026-05-18T01:04:16Z"},{"alias_kind":"arxiv_version","alias_value":"1609.06221v1","created_at":"2026-05-18T01:04:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.06221","created_at":"2026-05-18T01:04:16Z"},{"alias_kind":"pith_short_12","alias_value":"NSEIBGPOITER","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"NSEIBGPOITERDT5Y","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"NSEIBGPO","created_at":"2026-05-18T12:30:36Z"}],"graph_snapshots":[{"event_id":"sha256:31040064d88718cc079660c10591bfff64f227326b9580d5a50e55864fc5a128","target":"graph","created_at":"2026-05-18T01:04:16Z","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":"An optimal data partitioning in parallel & distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of affected points and better & faster merging. Though partitioning using Kd Tree is being conventionally used in academia, it suffers from performance drenches and bias (non equal distribution) as dimensionality of data increases and hence is not suitable for practical use in industry where dimensionality can be of order of 100s to 1000s. To address these issues we propose two new partitioning techni","authors_text":"Avnish Chandra Suman, Saraswati Mishra","cross_cats":["cs.DC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-09-20T15:26:37Z","title":"An Efficient Method of Partitioning High Volumes of Multidimensional Data for Parallel Clustering Algorithms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.06221","kind":"arxiv","version":1},"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:e2c294863a9f2ffb0959136aad263e08f1f92e4709ceb3089313367b40018c9b","target":"record","created_at":"2026-05-18T01:04:16Z","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":"588338ee16620bb816166913420e215326adc0c6edb3c7ee25c4b8bc2ccb6eec","cross_cats_sorted":["cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-09-20T15:26:37Z","title_canon_sha256":"01015fe1284d12ff5b08efefad324c3b44a9160250d2e0f6a995040cfb455942"},"schema_version":"1.0","source":{"id":"1609.06221","kind":"arxiv","version":1}},"canonical_sha256":"6c888099ee44c911cfb826de6befc8e714b891d8dddfc6612849fa4ac85dcfa5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c888099ee44c911cfb826de6befc8e714b891d8dddfc6612849fa4ac85dcfa5","first_computed_at":"2026-05-18T01:04:16.384437Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:04:16.384437Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1NG8GBskT8wvmmgUwmVWhfgu9xpO3qbN6bYdVGWjsef5Y0uKNzERJrdLVkEI89euQuVAVEc1zbgmfB1aW9rzDg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:04:16.384899Z","signed_message":"canonical_sha256_bytes"},"source_id":"1609.06221","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e2c294863a9f2ffb0959136aad263e08f1f92e4709ceb3089313367b40018c9b","sha256:31040064d88718cc079660c10591bfff64f227326b9580d5a50e55864fc5a128"],"state_sha256":"1504e7012409d8376458e9e25621ac83ee8713dad15f786e752878725889e70d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d1D+QdmkTYjaJL9ZN/3Gy198odxE7iBEzQZcHmAg1nryjLVryiAdExpEbD86NqIxnBmT26BJVAsXq0jFw6ABCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T18:27:47.573268Z","bundle_sha256":"c8b769218631c39998c52a9ad780767ee291b2719b3e56c3d4adb1ce23f0fcc2"}}