{"id":"https://openalex.org/W2804269561","doi":"https://doi.org/10.1145/3183713.3197387","title":"Data Integration and Machine Learning","display_name":"Data Integration and Machine Learning","publication_year":2018,"publication_date":"2018-05-25","ids":{"openalex":"https://openalex.org/W2804269561","doi":"https://doi.org/10.1145/3183713.3197387","mag":"2804269561"},"language":"en","primary_location":{"id":"doi:10.1145/3183713.3197387","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3183713.3197387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 International Conference on Management of 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/A5001402526","display_name":"Xin Luna Dong","orcid":"https://orcid.org/0009-0000-8667-322X"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xin Luna Dong","raw_affiliation_strings":["Amazon, Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon, Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002060759","display_name":"Theodoros Rekatsinas","orcid":"https://orcid.org/0000-0001-6148-1854"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Theodoros Rekatsinas","raw_affiliation_strings":["University of Wisconsin-Madison, Madison, WI, USA"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5001402526"],"corresponding_institution_ids":["https://openalex.org/I1311688040"],"apc_list":null,"apc_paid":null,"fwci":10.1484,"has_fulltext":false,"cited_by_count":89,"citation_normalized_percentile":{"value":0.98268123,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1645","last_page":"1650"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.996399998664856,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.993399977684021,"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/machine-learning","display_name":"Machine learning","score":0.7974808812141418},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7580004930496216},{"id":"https://openalex.org/keywords/data-integration","display_name":"Data integration","score":0.7142722010612488},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.6374236345291138},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6336467266082764},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.45951688289642334},{"id":"https://openalex.org/keywords/online-machine-learning","display_name":"Online machine learning","score":0.4409998059272766},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4373803734779358},{"id":"https://openalex.org/keywords/data-analysis","display_name":"Data analysis","score":0.4302123486995697},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3631429970264435},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.3038467764854431},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.25760215520858765}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7974808812141418},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7580004930496216},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.7142722010612488},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.6374236345291138},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6336467266082764},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.45951688289642334},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.4409998059272766},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4373803734779358},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.4302123486995697},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3631429970264435},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3038467764854431},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25760215520858765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3183713.3197387","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3183713.3197387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 International Conference on Management of Data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1485156179","https://openalex.org/W1497523460","https://openalex.org/W1521736627","https://openalex.org/W1565102206","https://openalex.org/W1667830255","https://openalex.org/W1852412531","https://openalex.org/W1981202432","https://openalex.org/W1981590391","https://openalex.org/W2000809552","https://openalex.org/W2016753842","https://openalex.org/W2041439319","https://openalex.org/W2066806792","https://openalex.org/W2067566391","https://openalex.org/W2073471108","https://openalex.org/W2101654853","https://openalex.org/W2101848544","https://openalex.org/W2103018059","https://openalex.org/W2107598941","https://openalex.org/W2107966677","https://openalex.org/W2110411158","https://openalex.org/W2132679783","https://openalex.org/W2134305421","https://openalex.org/W2137479650","https://openalex.org/W2148524305","https://openalex.org/W2163072729","https://openalex.org/W2184188583","https://openalex.org/W2288244345","https://openalex.org/W2323506184","https://openalex.org/W2404161646","https://openalex.org/W2404544029","https://openalex.org/W2437617937","https://openalex.org/W2438792749","https://openalex.org/W2460082991","https://openalex.org/W2516405782","https://openalex.org/W2548122763","https://openalex.org/W2559655401","https://openalex.org/W2591700809","https://openalex.org/W2604259521","https://openalex.org/W2608353553","https://openalex.org/W2612139288","https://openalex.org/W2612526608","https://openalex.org/W2612964472","https://openalex.org/W2613597870","https://openalex.org/W2613751718","https://openalex.org/W2741252866","https://openalex.org/W2769041395","https://openalex.org/W2805639717","https://openalex.org/W2883780523","https://openalex.org/W2959716049","https://openalex.org/W2962902328","https://openalex.org/W2964152081","https://openalex.org/W3126976873","https://openalex.org/W4249682906","https://openalex.org/W6639274278","https://openalex.org/W6696516842"],"related_works":["https://openalex.org/W4226266853","https://openalex.org/W3092201768","https://openalex.org/W2794907032","https://openalex.org/W2796632413","https://openalex.org/W4210252074","https://openalex.org/W4382315681","https://openalex.org/W4255802207","https://openalex.org/W3123352720","https://openalex.org/W2462007151","https://openalex.org/W2565527172"],"abstract_inverted_index":{"There":[0],"is":[1,48,62],"now":[2],"more":[3],"data":[4,11,25,38,50,66,89,99,124,132,149],"to":[5,32,126],"analyze":[6],"than":[7],"ever":[8],"before.":[9],"As":[10],"volume":[12],"and":[13,24,46,75,91,110,130,137,144,151],"variety":[14,43],"have":[15,18],"increased,":[16],"so":[17],"the":[19,40,57,85],"ties":[20],"between":[21,88],"machine":[22,30,60,92,104,119,152],"learning":[23,31,61,120],"integration":[26,51,73,90,100,125,150],"become":[27],"stronger.":[28],"For":[29],"be":[33],"effective,":[34],"one":[35],"must":[36],"utilize":[37],"from":[39],"greatest":[41],"possible":[42],"of":[44,72,84],"sources;":[45],"this":[47],"why":[49],"plays":[52],"a":[53],"key":[54],"role.":[55],"At":[56],"same":[58],"time":[59],"driving":[63],"automation":[64],"in":[65,69],"integration,":[67],"resulting":[68],"overall":[70],"reduction":[71],"costs":[74],"improved":[76],"accuracy.":[77],"This":[78],"tutorial":[79],"focuses":[80],"on":[81,103,123],"three":[82],"aspects":[83],"synergistic":[86],"relationship":[87],"learning:":[93],"(1)":[94],"we":[95,115,139],"survey":[96],"how":[97,117],"state-of-the-art":[98],"solutions":[101],"rely":[102,122],"learning-based":[105],"approaches":[106],"for":[107,133],"accurate":[108],"results":[109],"effective":[111],"human-in-the-loop":[112],"pipelines,":[113],"(2)":[114],"review":[116],"end-to-end":[118],"applications":[121],"identify":[127],"accurate,":[128],"clean,":[129],"relevant":[131],"their":[134],"analytics":[135],"exercises,":[136],"(3)":[138],"discuss":[140],"open":[141],"research":[142],"challenges":[143],"opportunities":[145],"that":[146],"span":[147],"across":[148],"learning.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":21},{"year":2020,"cited_by_count":16},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}