{"id":"https://openalex.org/W2439044439","doi":"https://doi.org/10.1109/icde.2016.7498372","title":"Efficient similarity join based on Earth Mover's Distance using MapReduce","display_name":"Efficient similarity join based on Earth Mover's Distance using MapReduce","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2439044439","doi":"https://doi.org/10.1109/icde.2016.7498372","mag":"2439044439"},"language":"en","primary_location":{"id":"doi:10.1109/icde.2016.7498372","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2016.7498372","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","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/A5042055991","display_name":"Jia Xu","orcid":"https://orcid.org/0000-0003-4061-8262"},"institutions":[{"id":"https://openalex.org/I150807315","display_name":"Guangxi University","ror":"https://ror.org/02c9qn167","country_code":"CN","type":"education","lineage":["https://openalex.org/I150807315"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Xu","raw_affiliation_strings":["School of Computer, Electronics and Information, Guangxi University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, Electronics and Information, Guangxi University, China","institution_ids":["https://openalex.org/I150807315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056766372","display_name":"Bin Lei","orcid":"https://orcid.org/0000-0001-7625-9236"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Lei","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013145169","display_name":"Yu Gu","orcid":"https://orcid.org/0000-0001-7422-6254"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Gu","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011314280","display_name":"Marianne Winslett","orcid":"https://orcid.org/0000-0002-3935-7168"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marianne Winslett","raw_affiliation_strings":["Department of Computer Science, University of Illinois, Urbana Champaign, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Illinois, Urbana Champaign, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072406974","display_name":"Ge Yu","orcid":"https://orcid.org/0000-0002-3171-8889"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ge Yu","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100633297","display_name":"Zhenjie Zhang","orcid":"https://orcid.org/0009-0000-9080-7489"},"institutions":[{"id":"https://openalex.org/I4210108443","display_name":"Advanced Digital Sciences Center","ror":"https://ror.org/01xaqx887","country_code":"SG","type":"facility","lineage":["https://openalex.org/I4210108443"]},{"id":"https://openalex.org/I4210110242","display_name":"Digital Science (United States)","ror":"https://ror.org/020h4b682","country_code":"US","type":"company","lineage":["https://openalex.org/I4210110242","https://openalex.org/I4210112888","https://openalex.org/I4210118830"]}],"countries":["SG","US"],"is_corresponding":false,"raw_author_name":"Zhenjie Zhang","raw_affiliation_strings":["Advanced Digital Sciences Center, Illinois at Singapore Pte. Ltd., Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Advanced Digital Sciences Center, Illinois at Singapore Pte. Ltd., Singapore","institution_ids":["https://openalex.org/I4210108443","https://openalex.org/I4210110242"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.04122572,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1456","last_page":"1457"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9923999905586243,"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/earth-movers-distance","display_name":"Earth mover's distance","score":0.8693060278892517},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.786483883857727},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.70478355884552},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6741177439689636},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5156001448631287},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.5072457194328308},{"id":"https://openalex.org/keywords/join","display_name":"Join (topology)","score":0.4866076707839966},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.4803696572780609},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4252415895462036},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37351512908935547},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.29880598187446594},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.13283520936965942},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09922680258750916}],"concepts":[{"id":"https://openalex.org/C82668687","wikidata":"https://www.wikidata.org/wiki/Q3046456","display_name":"Earth mover's distance","level":2,"score":0.8693060278892517},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.786483883857727},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.70478355884552},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6741177439689636},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5156001448631287},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.5072457194328308},{"id":"https://openalex.org/C2776124973","wikidata":"https://www.wikidata.org/wiki/Q3183033","display_name":"Join (topology)","level":2,"score":0.4866076707839966},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.4803696572780609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4252415895462036},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37351512908935547},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29880598187446594},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.13283520936965942},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09922680258750916},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icde.2016.7498372","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2016.7498372","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1991909257","https://openalex.org/W2019044299","https://openalex.org/W2055239866","https://openalex.org/W2143668817","https://openalex.org/W2173213060"],"related_works":["https://openalex.org/W2319693127","https://openalex.org/W2072263576","https://openalex.org/W2474567666","https://openalex.org/W1940044583","https://openalex.org/W2056226831","https://openalex.org/W2806903871","https://openalex.org/W2094963775","https://openalex.org/W4320802053","https://openalex.org/W2159804503","https://openalex.org/W3179795469"],"abstract_inverted_index":{"Earth":[0],"Mover's":[1],"Distance":[2],"(EMD)":[3],"evaluates":[4],"the":[5,55,62,69,98,113,127,147,160],"similarity":[6,19,23,26,66],"between":[7],"probability":[8,31],"distributions,":[9],"known":[10],"as":[11,44],"a":[12,36,75,136,155],"robust":[13],"measure":[14],"more":[15],"consistent":[16],"with":[17,33,135,154],"human":[18],"perception":[20],"than":[21,146],"traditional":[22],"functions.":[24],"EMD":[25,34,65,94,122],"join":[27],"retrieves":[28],"pairs":[29],"of":[30,57,64,93,129,139,144],"distributions":[32,90],"below":[35],"specified":[37],"threshold,":[38],"supporting":[39],"many":[40],"important":[41],"applications,":[42],"such":[43],"duplicate":[45],"image":[46,162],"retrieval":[47],"and":[48,118,150],"sensor":[49],"pattern":[50],"recognition.":[51],"This":[52],"paper":[53],"studies":[54],"possibility":[56],"using":[58,86],"MapReduce":[59],"to":[60,81,97,111],"improve":[61],"scalability":[63],"join.":[67],"Utilizing":[68],"dual-program":[70],"mapping":[71],"technique,":[72],"we":[73],"present":[74],"new":[76],"general":[77],"data":[78],"partition":[79],"framework":[80],"facilitate":[82],"effective":[83],"workload":[84],"decomposition":[85],"MapReduce,":[87],"ensuring":[88],"similar":[89],"in":[91,167,173],"terms":[92],"are":[95,108,171],"mapped":[96],"same":[99],"reduce":[100,116],"task":[101],"for":[102],"further":[103],"verification.":[104],"New":[105],"optimization":[106],"strategies":[107],"also":[109],"proposed":[110],"balance":[112],"workloads":[114],"among":[115],"tasks":[117],"eliminate":[119],"large":[120],"unnecessary":[121],"evaluations.":[123],"Our":[124],"experiments":[125],"verify":[126],"superiority":[128],"our":[130],"proposal":[131],"on":[132,151,164],"system":[133,152],"efficiency,":[134],"huge":[137],"advantage":[138],"at":[140],"least":[141],"one":[142],"order":[143],"magnitude":[145],"state-of-the-art":[148],"solution,":[149],"effectiveness,":[153],"real":[156],"case":[157],"study":[158],"towards":[159],"abused":[161],"phenomenon":[163],"C2C":[165],"website":[166],"China.":[168],"Further":[169],"details":[170],"reported":[172],"[4].":[174]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
