{"doi":"10.14778/3665844.3665846","title":"Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations","abstract":"<jats:p>\n            Videos that are shot using commodity hardware such as phones and surveillance cameras record various metadata such as time and location. We encounter such\n            <jats:italic>geospatial videos</jats:italic>\n            on a daily basis and such videos have been growing in volume significantly. Yet, we do not have data management systems that allow users to interact with such data effectively.\n          </jats:p>\n          <jats:p>\n            In this paper, we describe Spatialyze, a new framework for end-to-end querying of geospatial videos. Spatialyze comes with a domain-specific language where users can construct geospatial video analytic workflows using a 3-step, declarative,\n            <jats:italic>build-filter-observe</jats:italic>\n            paradigm. Internally, Spatialyze leverages the declarative nature of such workflows, the temporal-spatial metadata stored with videos, and physical behavior of real-world objects to optimize the execution of workflows. Our results using real-world videos and workflows show that Spatialyze can reduce execution time by up to 5.3×, while maintaining up to 97.1% accuracy compared to unoptimized execution.\n          </jats:p>","journal":"Proceedings of the VLDB Endowment","year":2024,"id":639002,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1659969,"name":"Yongming Ge","orcid":null,"position":1,"is_corresponding":false},{"id":1659970,"name":"Yousef Helal","orcid":null,"position":2,"is_corresponding":false},{"id":1659971,"name":"Alvin Cheung","orcid":null,"position":3,"is_corresponding":false},{"id":1659967,"name":"Chanwut Kittivorawong","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations","abstract":"<jats:p>\n            Videos that are shot using commodity hardware such as phones and surveillance cameras record various metadata such as time and location. We encounter such\n            <jats:italic>geospatial videos</jats:italic>\n            on a daily basis and such videos have been growing in volume significantly. Yet, we do not have data management systems that allow users to interact with such data effectively.\n          </jats:p>\n          <jats:p>\n            In this paper, we describe Spatialyze, a new framework for end-to-end querying of geospatial videos. Spatialyze comes with a domain-specific language where users can construct geospatial video analytic workflows using a 3-step, declarative,\n            <jats:italic>build-filter-observe</jats:italic>\n            paradigm. Internally, Spatialyze leverages the declarative nature of such workflows, the temporal-spatial metadata stored with videos, and physical behavior of real-world objects to optimize the execution of workflows. Our results using real-world videos and workflows show that Spatialyze can reduce execution time by up to 5.3×, while maintaining up to 97.1% accuracy compared to unoptimized execution.\n          </jats:p>","is_dataset_classified":null,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4401352344","authors":[],"funders":[],"total_grants":0,"fwci":0.8175,"citation_percentile":0.7263957,"influential_citations":0,"citation_trend":[{"year":2024,"count":2},{"year":2025,"count":2}],"oa_status":"closed","license":null,"oa_locations":[{"url":"https://dl.acm.org/doi/pdf/10.14778/3665844.3665846","host_type":"publisher"},{"url":"https://doi.org/10.14778/3665844.3665846","host_type":"journal"}],"fields_of_study":["Advanced Image and Video Retrieval Techniques","Video Surveillance and Tracking Methods","Video Analysis and Summarization"],"mesh_terms":[],"keywords":["Geospatial analysis","Analytics","Computer science","Data science","Remote sensing","Geography"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T22:11:19.460302Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}