{"id":"https://openalex.org/W2198159313","doi":"https://doi.org/10.1109/bigdata.2015.7363787","title":"TKSimGPU: A parallel top-K trajectory similarity query processing algorithm for GPGPUs","display_name":"TKSimGPU: A parallel top-K trajectory similarity query processing algorithm for GPGPUs","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2198159313","doi":"https://doi.org/10.1109/bigdata.2015.7363787","mag":"2198159313"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2015.7363787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-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/A5041886747","display_name":"Eleazar Leal","orcid":"https://orcid.org/0000-0002-3055-1845"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Eleazar Leal","raw_affiliation_strings":["School of Computer Science, University of Oklahoma, Norman, OK, USA"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Oklahoma, Norman, OK, USA","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071062190","display_name":"Le Gruenwald","orcid":"https://orcid.org/0000-0002-5245-4747"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Le Gruenwald","raw_affiliation_strings":["University of Oklahoma, Norman, OK, US"],"affiliations":[{"raw_affiliation_string":"University of Oklahoma, Norman, OK, US","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101482691","display_name":"Jianting Zhang","orcid":"https://orcid.org/0000-0002-0161-9716"},"institutions":[{"id":"https://openalex.org/I125687163","display_name":"City College of New York","ror":"https://ror.org/00wmhkr98","country_code":"US","type":"education","lineage":["https://openalex.org/I125687163"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianting Zhang","raw_affiliation_strings":["Dept. of Computer, Science City College of New York, New York City, NY, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer, Science City College of New York, New York City, NY, USA","institution_ids":["https://openalex.org/I125687163"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113803154","display_name":"Simin You","orcid":null},"institutions":[{"id":"https://openalex.org/I121847817","display_name":"The Graduate Center, CUNY","ror":"https://ror.org/00awd9g61","country_code":"US","type":"education","lineage":["https://openalex.org/I121847817"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Simin You","raw_affiliation_strings":["Dept. of Computer Science, CUNY Graduate Center, New York City, NY, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, CUNY Graduate Center, New York City, NY, USA","institution_ids":["https://openalex.org/I121847817"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041886747"],"corresponding_institution_ids":["https://openalex.org/I8692664"],"apc_list":null,"apc_paid":null,"fwci":2.0103,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.87569963,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"33","issue":null,"first_page":"461","last_page":"469"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9998999834060669,"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":0.9998999834060669,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9753000140190125,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9613999724388123,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.8812413215637207},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8379640579223633},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7107574939727783},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.692592442035675},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5469357371330261},{"id":"https://openalex.org/keywords/multi-core-processor","display_name":"Multi-core processor","score":0.5464563965797424},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.5406993627548218},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45859068632125854},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4178021550178528},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3287869989871979},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32021647691726685},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.22804787755012512},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1554298996925354}],"concepts":[{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.8812413215637207},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8379640579223633},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7107574939727783},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.692592442035675},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5469357371330261},{"id":"https://openalex.org/C78766204","wikidata":"https://www.wikidata.org/wiki/Q555032","display_name":"Multi-core processor","level":2,"score":0.5464563965797424},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.5406993627548218},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45859068632125854},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4178021550178528},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3287869989871979},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32021647691726685},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.22804787755012512},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1554298996925354},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2015.7363787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8399999737739563,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W149117090","https://openalex.org/W579519726","https://openalex.org/W588924597","https://openalex.org/W1480958225","https://openalex.org/W1486723877","https://openalex.org/W1552383713","https://openalex.org/W1555915743","https://openalex.org/W1561673278","https://openalex.org/W1567097384","https://openalex.org/W1595783387","https://openalex.org/W1597504361","https://openalex.org/W1600619963","https://openalex.org/W1626398438","https://openalex.org/W1827315716","https://openalex.org/W1981398125","https://openalex.org/W1993997820","https://openalex.org/W2031674781","https://openalex.org/W2047519109","https://openalex.org/W2058458206","https://openalex.org/W2065663378","https://openalex.org/W2068690597","https://openalex.org/W2083221501","https://openalex.org/W2091921805","https://openalex.org/W2096903042","https://openalex.org/W2100946521","https://openalex.org/W2106659141","https://openalex.org/W2115344139","https://openalex.org/W2118371392","https://openalex.org/W2134195632","https://openalex.org/W2136083615","https://openalex.org/W2159481344","https://openalex.org/W2161061943","https://openalex.org/W2164529774","https://openalex.org/W2168259560","https://openalex.org/W2725179571","https://openalex.org/W3000120212","https://openalex.org/W6616851794","https://openalex.org/W6633127185","https://openalex.org/W6635487903","https://openalex.org/W6635824521"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W3138386522","https://openalex.org/W2499279132","https://openalex.org/W2264746079","https://openalex.org/W3012895752","https://openalex.org/W2093601330"],"abstract_inverted_index":{"There":[0],"exist":[1],"large":[2,190],"datasets":[3],"containing":[4],"the":[5,35,40,70,105,114,135,175],"sequences":[6,20],"of":[7,21,37,42,66,77,88,107,137,168,177],"points":[8],"that":[9,33,112,158,199],"moving":[10,22],"objects":[11,23,44],"occupy":[12],"in":[13,56,62,103,110,141,162,205,219],"space":[14],"as":[15,26,53,80],"time":[16,208,222],"goes":[17],"by.":[18],"Such":[19],"are":[24,113],"known":[25],"trajectories.":[27],"Being":[28],"able":[29],"to":[30,47,97,117,164],"issue":[31],"queries":[32,68,181],"allow":[34],"extraction":[36],"patterns":[38],"from":[39],"movements":[41],"these":[43,121],"is":[45,69,124],"important":[46],"many":[48],"real":[49,192],"world":[50,193],"applications,":[51],"such":[52,67],"urban":[54],"planning":[55],"transportation":[57],"and":[58,86,90,95,127,186,215],"bird":[59],"migration":[60],"tracking":[61],"ecology.":[63],"One":[64],"example":[65],"top-K":[71,145,178],"trajectory":[72,101,122,146,169,179,194],"similarity":[73,147,180],"query.":[74],"This":[75],"type":[76,167],"query":[78,206,220],"receives":[79],"input":[81],"arguments":[82],"two":[83],"sets":[84],"P":[85,104],"Q":[87,111],"trajectories":[89,109],"a":[91,142,189,202,213,216,224],"positive":[92],"integer":[93],"k,":[94],"seeks":[96],"find":[98],"for":[99,139],"every":[100],"p":[102],"set":[106],"k":[108],"most":[115],"similar":[116],"p.":[118],"However,":[119],"querying":[120],"data":[123],"both":[125],"compute":[126],"I/O":[128],"intensive.":[129],"In":[130],"this":[131,150,166],"paper":[132],"we":[133,152],"explore":[134],"potential":[136],"GPGPUs":[138,187],"supporting,":[140],"scalable":[143],"manner,":[144],"queries.":[148,170],"To":[149],"end,":[151],"propose":[153],"an":[154],"algorithm,":[155],"called":[156],"TKSimGPU,":[157],"incorporates":[159],"parallelization":[160],"strategies":[161],"order":[163],"answer":[165],"We":[171],"conducted":[172],"experiments":[173,197],"comparing":[174],"throughput":[176],"performed":[182],"on":[183,212,223],"multicore":[184],"CPUs":[185],"using":[188],"scale":[191],"dataset.":[195],"The":[196],"show":[198],"TKSimGPU":[200],"achieved":[201],"3.37x":[203],"speedup":[204,218],"processing":[207,221],"over":[209],"exhaustive":[210],"search":[211],"GPU,":[214],"4.9x":[217],"12-core":[225],"CPU":[226],"architecture.":[227]},"counts_by_year":[{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
