{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DRXQWMWDFMCPZYT2SMQ7CE4G3O","short_pith_number":"pith:DRXQWMWD","schema_version":"1.0","canonical_sha256":"1c6f0b32c32b04fce27a9321f11386dba7a236981b688edf1d6da5a53ea8ab19","source":{"kind":"arxiv","id":"1808.02793","version":1},"attestation_state":"computed","paper":{"title":"Efficient Continuous Top-$k$ Geo-Image Search on Road Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.MM","authors_text":"Chengyuan Zhang, Fang Huang, Kesheng Cheng, Lei Zhu, Ruipeng Chen, Zuping Zhang","submitted_at":"2018-08-08T14:25:16Z","abstract_excerpt":"With the rapid development of mobile Internet and cloud computing technology, large-scale multimedia data, e.g., texts, images, audio and videos have been generated, collected, stored and shared. In this paper, we propose a novel query problem named continuous top-$k$ geo-image query on road network which aims to search out a set of geo-visual objects based on road network distance proximity and visual content similarity. Existing approaches for spatial textual query and geo-image query cannot address this problem effectively because they do not consider both of visual content similarity and r"},"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":"1808.02793","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2018-08-08T14:25:16Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"1a0bed1002fa6142ed03b38edab40463f334189712e3e8e76e1c8d2681214d66","abstract_canon_sha256":"b81821ac107453c0119b810eaa80f532f10d7e0c69b4c77118f0d947bbe897c2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:32.162897Z","signature_b64":"f7iRFO0jXPv8+0c15roRGipp5Zi4D2/DbPDWJj0w4njkSPEwNBolvTgGJ2dQWPSlslr+s4nc9eBQhto0OD8eDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c6f0b32c32b04fce27a9321f11386dba7a236981b688edf1d6da5a53ea8ab19","last_reissued_at":"2026-05-18T00:08:32.162227Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:32.162227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Continuous Top-$k$ Geo-Image Search on Road Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.MM","authors_text":"Chengyuan Zhang, Fang Huang, Kesheng Cheng, Lei Zhu, Ruipeng Chen, Zuping Zhang","submitted_at":"2018-08-08T14:25:16Z","abstract_excerpt":"With the rapid development of mobile Internet and cloud computing technology, large-scale multimedia data, e.g., texts, images, audio and videos have been generated, collected, stored and shared. In this paper, we propose a novel query problem named continuous top-$k$ geo-image query on road network which aims to search out a set of geo-visual objects based on road network distance proximity and visual content similarity. Existing approaches for spatial textual query and geo-image query cannot address this problem effectively because they do not consider both of visual content similarity and r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.02793","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":"1808.02793","created_at":"2026-05-18T00:08:32.162313+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.02793v1","created_at":"2026-05-18T00:08:32.162313+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.02793","created_at":"2026-05-18T00:08:32.162313+00:00"},{"alias_kind":"pith_short_12","alias_value":"DRXQWMWDFMCP","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DRXQWMWDFMCPZYT2","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DRXQWMWD","created_at":"2026-05-18T12:32:19.392346+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/DRXQWMWDFMCPZYT2SMQ7CE4G3O","json":"https://pith.science/pith/DRXQWMWDFMCPZYT2SMQ7CE4G3O.json","graph_json":"https://pith.science/api/pith-number/DRXQWMWDFMCPZYT2SMQ7CE4G3O/graph.json","events_json":"https://pith.science/api/pith-number/DRXQWMWDFMCPZYT2SMQ7CE4G3O/events.json","paper":"https://pith.science/paper/DRXQWMWD"},"agent_actions":{"view_html":"https://pith.science/pith/DRXQWMWDFMCPZYT2SMQ7CE4G3O","download_json":"https://pith.science/pith/DRXQWMWDFMCPZYT2SMQ7CE4G3O.json","view_paper":"https://pith.science/paper/DRXQWMWD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.02793&json=true","fetch_graph":"https://pith.science/api/pith-number/DRXQWMWDFMCPZYT2SMQ7CE4G3O/graph.json","fetch_events":"https://pith.science/api/pith-number/DRXQWMWDFMCPZYT2SMQ7CE4G3O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DRXQWMWDFMCPZYT2SMQ7CE4G3O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DRXQWMWDFMCPZYT2SMQ7CE4G3O/action/storage_attestation","attest_author":"https://pith.science/pith/DRXQWMWDFMCPZYT2SMQ7CE4G3O/action/author_attestation","sign_citation":"https://pith.science/pith/DRXQWMWDFMCPZYT2SMQ7CE4G3O/action/citation_signature","submit_replication":"https://pith.science/pith/DRXQWMWDFMCPZYT2SMQ7CE4G3O/action/replication_record"}},"created_at":"2026-05-18T00:08:32.162313+00:00","updated_at":"2026-05-18T00:08:32.162313+00:00"}