{"id":"https://openalex.org/W2012890979","doi":"https://doi.org/10.1145/1857947.1857952","title":"A Model-Agnostic Framework for Fast Spatial Anomaly Detection","display_name":"A Model-Agnostic Framework for Fast Spatial Anomaly Detection","publication_year":2010,"publication_date":"2010-10-01","ids":{"openalex":"https://openalex.org/W2012890979","doi":"https://doi.org/10.1145/1857947.1857952","mag":"2012890979"},"language":"en","primary_location":{"id":"doi:10.1145/1857947.1857952","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1857947.1857952","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103182309","display_name":"Mingxi Wu","orcid":"https://orcid.org/0009-0009-2738-7018"},"institutions":[{"id":"https://openalex.org/I1342911587","display_name":"Oracle (United States)","ror":"https://ror.org/006c77m33","country_code":"US","type":"company","lineage":["https://openalex.org/I1342911587"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mingxi Wu","raw_affiliation_strings":["Oracle Corporation","Oracle Corporation,#TAB#"],"affiliations":[{"raw_affiliation_string":"Oracle Corporation","institution_ids":["https://openalex.org/I1342911587"]},{"raw_affiliation_string":"Oracle Corporation,#TAB#","institution_ids":["https://openalex.org/I1342911587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002518742","display_name":"Chris Jermaine","orcid":null},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chris Jermaine","raw_affiliation_strings":["Rice University","**Rice University;"],"affiliations":[{"raw_affiliation_string":"Rice University","institution_ids":["https://openalex.org/I74775410"]},{"raw_affiliation_string":"**Rice University;","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sanjay Ranka","orcid":null},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sanjay Ranka","raw_affiliation_strings":["University of Florida","[University of Florida]"],"affiliations":[{"raw_affiliation_string":"University of Florida","institution_ids":["https://openalex.org/I33213144"]},{"raw_affiliation_string":"[University of Florida]","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113679176","display_name":"Xiuyao Song","orcid":null},"institutions":[{"id":"https://openalex.org/I1325784139","display_name":"Yahoo (United Kingdom)","ror":"https://ror.org/038p3gq39","country_code":"GB","type":"company","lineage":["https://openalex.org/I1325784139","https://openalex.org/I4210134091"]},{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Xiuyao Song","raw_affiliation_strings":["Yahoo! Inc","Yahoo! Inc.,#TAB#"],"affiliations":[{"raw_affiliation_string":"Yahoo! Inc","institution_ids":["https://openalex.org/I4210134091"]},{"raw_affiliation_string":"Yahoo! Inc.,#TAB#","institution_ids":["https://openalex.org/I1325784139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075968422","display_name":"John G. Gums","orcid":"https://orcid.org/0000-0003-4727-9566"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Gums","raw_affiliation_strings":["University of Florida","[University of Florida]"],"affiliations":[{"raw_affiliation_string":"University of Florida","institution_ids":["https://openalex.org/I33213144"]},{"raw_affiliation_string":"[University of Florida]","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103182309"],"corresponding_institution_ids":["https://openalex.org/I1342911587"],"apc_list":null,"apc_paid":null,"fwci":0.19,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.59877656,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"4","issue":"4","first_page":"1","last_page":"30"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.7026878595352173},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6115931272506714},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.555334210395813},{"id":"https://openalex.org/keywords/likelihood-ratio-test","display_name":"Likelihood-ratio test","score":0.5539683699607849},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5452419519424438},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5194470286369324},{"id":"https://openalex.org/keywords/likelihood-function","display_name":"Likelihood function","score":0.49286484718322754},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4871184825897217},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.48315948247909546},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.43800824880599976},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4317072629928589},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4002648591995239},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.38908401131629944},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38434267044067383},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33396315574645996},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2904147505760193},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.12924563884735107},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.09433826804161072}],"concepts":[{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.7026878595352173},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6115931272506714},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.555334210395813},{"id":"https://openalex.org/C9483764","wikidata":"https://www.wikidata.org/wiki/Q585740","display_name":"Likelihood-ratio test","level":2,"score":0.5539683699607849},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5452419519424438},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5194470286369324},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.49286484718322754},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4871184825897217},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.48315948247909546},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.43800824880599976},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4317072629928589},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4002648591995239},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.38908401131629944},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38434267044067383},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33396315574645996},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2904147505760193},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.12924563884735107},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.09433826804161072},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1857947.1857952","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1857947.1857952","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6330881573","display_name":null,"funder_award_id":"612170","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W80588910","https://openalex.org/W1566114229","https://openalex.org/W1673310716","https://openalex.org/W1824047490","https://openalex.org/W1965138864","https://openalex.org/W1981796063","https://openalex.org/W2002151188","https://openalex.org/W2006145698","https://openalex.org/W2019841176","https://openalex.org/W2049633694","https://openalex.org/W2070781416","https://openalex.org/W2083772019","https://openalex.org/W2100936901","https://openalex.org/W2104809968","https://openalex.org/W2126626732","https://openalex.org/W2147555723","https://openalex.org/W3100806406"],"related_works":["https://openalex.org/W2013921918","https://openalex.org/W1486530265","https://openalex.org/W2473791430","https://openalex.org/W2084412915","https://openalex.org/W2019724159","https://openalex.org/W2152471723","https://openalex.org/W4280508323","https://openalex.org/W2141324124","https://openalex.org/W2149766606","https://openalex.org/W2371672232"],"abstract_inverted_index":{"Given":[0],"a":[1,39],"spatial":[2],"dataset":[3,24],"placed":[4],"on":[5,58],"an":[6],"n":[7,9],"\u00d7":[8],"grid,":[10,51],"our":[11],"goal":[12],"is":[13],"to":[14,78],"find":[15],"the":[16,23,50,55,59,65],"rectangular":[17,47],"regions":[18],"within":[19],"which":[20],"subsets":[21],"of":[22,54],"exhibit":[25],"anomalous":[26],"behavior.":[27],"We":[28],"develop":[29,76],"algorithms":[30],"that,":[31],"given":[32],"any":[33],"user-supplied":[34],"arbitrary":[35],"likelihood":[36,40],"function,":[37],"conduct":[38],"ratio":[41],"hypothesis":[42],"test":[43],"(LRT)":[44],"over":[45],"each":[46],"region":[48],"in":[49],"rank":[52],"all":[53],"rectangles":[56,80],"based":[57],"computed":[60],"LRT":[61,85],"statistics,":[62],"and":[63],"return":[64],"top":[66],"few":[67],"most":[68],"interesting":[69],"rectangles.":[70],"To":[71],"speed":[72],"this":[73],"process,":[74],"we":[75],"methods":[77],"prune":[79],"without":[81],"computing":[82],"their":[83],"associated":[84],"statistics.":[86]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}