{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:FGI4V63IZ6CBBEK7VFEVO46HFF","short_pith_number":"pith:FGI4V63I","canonical_record":{"source":{"id":"1303.0447","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2013-03-03T01:45:37Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"513a2233abec7a30b7881d8505ed5792d2e4797e20e49c423969b8272ecb9979","abstract_canon_sha256":"19ccc892ae355b71460b3a6ab7c6dc7a52e4d89a9c7c664baa3d935453094d8b"},"schema_version":"1.0"},"canonical_sha256":"2991cafb68cf8410915fa9495773c729701e2dcfd972bf058d350dc366f37e77","source":{"kind":"arxiv","id":"1303.0447","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1303.0447","created_at":"2026-05-18T03:31:56Z"},{"alias_kind":"arxiv_version","alias_value":"1303.0447v1","created_at":"2026-05-18T03:31:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.0447","created_at":"2026-05-18T03:31:56Z"},{"alias_kind":"pith_short_12","alias_value":"FGI4V63IZ6CB","created_at":"2026-05-18T12:27:45Z"},{"alias_kind":"pith_short_16","alias_value":"FGI4V63IZ6CBBEK7","created_at":"2026-05-18T12:27:45Z"},{"alias_kind":"pith_short_8","alias_value":"FGI4V63I","created_at":"2026-05-18T12:27:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:FGI4V63IZ6CBBEK7VFEVO46HFF","target":"record","payload":{"canonical_record":{"source":{"id":"1303.0447","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2013-03-03T01:45:37Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"513a2233abec7a30b7881d8505ed5792d2e4797e20e49c423969b8272ecb9979","abstract_canon_sha256":"19ccc892ae355b71460b3a6ab7c6dc7a52e4d89a9c7c664baa3d935453094d8b"},"schema_version":"1.0"},"canonical_sha256":"2991cafb68cf8410915fa9495773c729701e2dcfd972bf058d350dc366f37e77","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:31:56.022356Z","signature_b64":"LZzeGgjKy2nATRsxbraoVh/hCqr7alupBAtdhWshgzY0hsNzVtTZUknIhg2qVe8WlaXhzfEPHcZpRsVew4x5AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2991cafb68cf8410915fa9495773c729701e2dcfd972bf058d350dc366f37e77","last_reissued_at":"2026-05-18T03:31:56.021646Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:31:56.021646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1303.0447","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":"2026-05-18T03:31:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jFdolWMbr0mgVdCg8He7ERlgjG732XWt5BXQ/O6OZnEHq3dJGbr3uh4mnMd6VZwE+Rr9qVlo7DBxhZE6MS9/CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T15:56:32.503958Z"},"content_sha256":"0484f38f77049409011e7a4391169f349757d8009d9959aff475d8f26e427814","schema_version":"1.0","event_id":"sha256:0484f38f77049409011e7a4391169f349757d8009d9959aff475d8f26e427814"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:FGI4V63IZ6CBBEK7VFEVO46HFF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Study on Application of Spatial Data Mining Techniques for Rural Progress","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.DB","authors_text":"K. Raja, V. R. Kanagavalli","submitted_at":"2013-03-03T01:45:37Z","abstract_excerpt":"This paper focuses on the application of Spatial Data mining Techniques to efficiently manage the challenges faced by peripheral rural areas in analyzing and predicting market scenario and better manage their economy. Spatial data mining is the task of unfolding the implicit knowledge hidden in the spatial databases. The spatial Databases contain both spatial and non-spatial attributes of the areas under study. Finding implicit regularities, rules or patterns hidden in spatial databases is an important task, e.g. for geo-marketing, traffic control or environmental studies. In this paper the fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.0447","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":"2026-05-18T03:31:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6JeeYEzUZfnbf4HTPAmkIwYdpJG71uiHCfPKi5a2Oo0YUKjGYVgOaR1sjPSr27gnPn9zKumgwJzOj9ilC0MrDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T15:56:32.504305Z"},"content_sha256":"e36d121129899af35be6e61e8a4353bf3cf115052362745deb656ac1067fe3ff","schema_version":"1.0","event_id":"sha256:e36d121129899af35be6e61e8a4353bf3cf115052362745deb656ac1067fe3ff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FGI4V63IZ6CBBEK7VFEVO46HFF/bundle.json","state_url":"https://pith.science/pith/FGI4V63IZ6CBBEK7VFEVO46HFF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FGI4V63IZ6CBBEK7VFEVO46HFF/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":"2026-06-24T15:56:32Z","links":{"resolver":"https://pith.science/pith/FGI4V63IZ6CBBEK7VFEVO46HFF","bundle":"https://pith.science/pith/FGI4V63IZ6CBBEK7VFEVO46HFF/bundle.json","state":"https://pith.science/pith/FGI4V63IZ6CBBEK7VFEVO46HFF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FGI4V63IZ6CBBEK7VFEVO46HFF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:FGI4V63IZ6CBBEK7VFEVO46HFF","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":"19ccc892ae355b71460b3a6ab7c6dc7a52e4d89a9c7c664baa3d935453094d8b","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2013-03-03T01:45:37Z","title_canon_sha256":"513a2233abec7a30b7881d8505ed5792d2e4797e20e49c423969b8272ecb9979"},"schema_version":"1.0","source":{"id":"1303.0447","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1303.0447","created_at":"2026-05-18T03:31:56Z"},{"alias_kind":"arxiv_version","alias_value":"1303.0447v1","created_at":"2026-05-18T03:31:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.0447","created_at":"2026-05-18T03:31:56Z"},{"alias_kind":"pith_short_12","alias_value":"FGI4V63IZ6CB","created_at":"2026-05-18T12:27:45Z"},{"alias_kind":"pith_short_16","alias_value":"FGI4V63IZ6CBBEK7","created_at":"2026-05-18T12:27:45Z"},{"alias_kind":"pith_short_8","alias_value":"FGI4V63I","created_at":"2026-05-18T12:27:45Z"}],"graph_snapshots":[{"event_id":"sha256:e36d121129899af35be6e61e8a4353bf3cf115052362745deb656ac1067fe3ff","target":"graph","created_at":"2026-05-18T03:31:56Z","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":"This paper focuses on the application of Spatial Data mining Techniques to efficiently manage the challenges faced by peripheral rural areas in analyzing and predicting market scenario and better manage their economy. Spatial data mining is the task of unfolding the implicit knowledge hidden in the spatial databases. The spatial Databases contain both spatial and non-spatial attributes of the areas under study. Finding implicit regularities, rules or patterns hidden in spatial databases is an important task, e.g. for geo-marketing, traffic control or environmental studies. In this paper the fo","authors_text":"K. Raja, V. R. Kanagavalli","cross_cats":["cs.CY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2013-03-03T01:45:37Z","title":"A Study on Application of Spatial Data Mining Techniques for Rural Progress"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.0447","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":"sha256:0484f38f77049409011e7a4391169f349757d8009d9959aff475d8f26e427814","target":"record","created_at":"2026-05-18T03:31:56Z","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":"19ccc892ae355b71460b3a6ab7c6dc7a52e4d89a9c7c664baa3d935453094d8b","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2013-03-03T01:45:37Z","title_canon_sha256":"513a2233abec7a30b7881d8505ed5792d2e4797e20e49c423969b8272ecb9979"},"schema_version":"1.0","source":{"id":"1303.0447","kind":"arxiv","version":1}},"canonical_sha256":"2991cafb68cf8410915fa9495773c729701e2dcfd972bf058d350dc366f37e77","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2991cafb68cf8410915fa9495773c729701e2dcfd972bf058d350dc366f37e77","first_computed_at":"2026-05-18T03:31:56.021646Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:31:56.021646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LZzeGgjKy2nATRsxbraoVh/hCqr7alupBAtdhWshgzY0hsNzVtTZUknIhg2qVe8WlaXhzfEPHcZpRsVew4x5AA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:31:56.022356Z","signed_message":"canonical_sha256_bytes"},"source_id":"1303.0447","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0484f38f77049409011e7a4391169f349757d8009d9959aff475d8f26e427814","sha256:e36d121129899af35be6e61e8a4353bf3cf115052362745deb656ac1067fe3ff"],"state_sha256":"17f4922e7acad5b25f3396a417d9468d1c8659e42ea2363c0f7b88b5ecc02834"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SKr/t4lx52sCLFxowYeLeFv0MWOdUZMlZ38Zbd7vXfNlB0F3fZXWc1gN3NgHMcuPvNV4TlzL2swdcQDWfERPBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T15:56:32.506232Z","bundle_sha256":"6626fb250064f229187c1a528b3735af09683e1d6c600b565db0eeb60c6b2a1b"}}