{"id":"https://openalex.org/W1991086014","doi":"https://doi.org/10.1145/1081870.1081962","title":"A generalized framework for mining spatio-temporal patterns in scientific data","display_name":"A generalized framework for mining spatio-temporal patterns in scientific data","publication_year":2005,"publication_date":"2005-08-21","ids":{"openalex":"https://openalex.org/W1991086014","doi":"https://doi.org/10.1145/1081870.1081962","mag":"1991086014"},"language":"en","primary_location":{"id":"doi:10.1145/1081870.1081962","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1081870.1081962","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5013881706","display_name":"Hui Yang","orcid":"https://orcid.org/0000-0002-3372-4801"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Yang","raw_affiliation_strings":["Ohio State University, Columbus, OH"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ohio State University, Columbus, OH","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100755351","display_name":"Srinivasan Parthasarathy","orcid":"https://orcid.org/0000-0002-6062-6449"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Srinivasan Parthasarathy","raw_affiliation_strings":["Ohio State University, Columbus, OH"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ohio State University, Columbus, OH","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046646390","display_name":"Sameep Mehta","orcid":"https://orcid.org/0000-0002-9599-1526"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sameep Mehta","raw_affiliation_strings":["Ohio State University, Columbus, OH"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ohio State University, Columbus, OH","institution_ids":["https://openalex.org/I52357470"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I52357470"],"apc_list":null,"apc_paid":null,"fwci":6.1288,"has_fulltext":false,"cited_by_count":61,"citation_normalized_percentile":{"value":0.97326272,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"716","last_page":"721"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.7481248378753662},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.587409257888794},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5864356756210327},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.5433143377304077},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5210287570953369},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.42479923367500305},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.41472384333610535},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33354073762893677},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10129430890083313},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.0866820216178894}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7481248378753662},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.587409257888794},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5864356756210327},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.5433143377304077},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5210287570953369},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.42479923367500305},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.41472384333610535},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33354073762893677},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10129430890083313},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0866820216178894},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1081870.1081962","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1081870.1081962","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.126.5443","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.126.5443","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://dmrl.cse.ohio-state.edu/papers/kdd05.hui.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6000000238418579,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W22907186","https://openalex.org/W76928844","https://openalex.org/W95210126","https://openalex.org/W1483679765","https://openalex.org/W1515526911","https://openalex.org/W1544139368","https://openalex.org/W1560354205","https://openalex.org/W1579430386","https://openalex.org/W1871668702","https://openalex.org/W1964759024","https://openalex.org/W1973749534","https://openalex.org/W1976347887","https://openalex.org/W1977957682","https://openalex.org/W2097604114","https://openalex.org/W2123656392","https://openalex.org/W2136108450","https://openalex.org/W2148653807","https://openalex.org/W2150797568","https://openalex.org/W2170726034","https://openalex.org/W2294935888","https://openalex.org/W2536286998","https://openalex.org/W2602645338","https://openalex.org/W2994359910","https://openalex.org/W3044855295","https://openalex.org/W6795527497"],"related_works":["https://openalex.org/W1585007175","https://openalex.org/W2382521049","https://openalex.org/W2144385241","https://openalex.org/W4300101996","https://openalex.org/W2069592018","https://openalex.org/W2075740387","https://openalex.org/W2165950148","https://openalex.org/W4253593777","https://openalex.org/W2951497643","https://openalex.org/W2358990940"],"abstract_inverted_index":{"In":[0,18],"this":[1,24],"paper,":[2],"we":[3],"present":[4,136],"a":[5,90],"general":[6],"framework":[7,113],"to":[8,20,68,84,93,105,117,165],"discover":[9,69],"spatial":[10,62,74],"associations":[11],"and":[12,46,88,131,145],"spatio-temporal":[13,95],"episodes":[14,101,146],"for":[15],"scientific":[16],"datasets.":[17],"contrast":[19],"previous":[21],"work":[22],"in":[23,51,61],"area,":[25],"features":[26],"are":[27,48],"modeled":[28],"as":[29],"geometric":[30],"objects":[31,60],"rather":[32],"than":[33],"points.":[34],"We":[35,64,79,97,110,135,154],"define":[36],"multiple":[37],"distance":[38],"metrics":[39],"that":[40,99,157],"take":[41],"into":[42],"account":[43],"objects'":[44],"extent":[45],"thus":[47],"more":[49],"robust":[50],"capturing":[52],"the":[53,139,142,151,158,166],"influence":[54],"of":[55,73,141],"an":[56],"object":[57,75],"on":[58,114],"other":[59],"neighborhood.":[63],"have":[65],"developed":[66],"algorithms":[67,160],"four":[70],"different":[71,126],"types":[72],"interaction":[76],"(association)":[77],"patterns.":[78],"also":[80,155],"extend":[81],"our":[82,112],"approach":[83],"accommodate":[85],"temporal":[86],"information":[87],"propose":[89],"simple":[91],"algorithm":[92],"derive":[94],"episodes.":[96],"show":[98,156],"such":[100],"can":[102],"be":[103],"used":[104],"reason":[106],"about":[107],"critical":[108],"events.":[109],"evaluate":[111],"real":[115],"datasets":[116,122],"demonstrate":[118],"its":[119],"efficacy.":[120],"The":[121],"originate":[123],"from":[124,150],"two":[125],"areas:":[127],"Computational":[128,132],"Molecular":[129],"Dynamics":[130],"Fluid":[133],"Flow.":[134],"results":[137],"highlighting":[138],"importance":[140],"identified":[143],"patterns":[144],"by":[147],"using":[148],"knowledge":[149],"underlying":[152],"domains.":[153],"proposed":[159],"scale":[161],"linearly":[162],"with":[163],"respect":[164],"dataset":[167],"size.":[168]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":4},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":7}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}
