{"id":"https://openalex.org/W2583850415","doi":"https://doi.org/10.1109/bigdata.2016.7840601","title":"Detecting gradual changes from data stream using MDL-change statistics","display_name":"Detecting gradual changes from data stream using MDL-change statistics","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2583850415","doi":"https://doi.org/10.1109/bigdata.2016.7840601","mag":"2583850415"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2016.7840601","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 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/A5021981442","display_name":"Kenji Yamanishi","orcid":"https://orcid.org/0000-0001-7370-9991"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Kenji Yamanishi","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, JAPAN"],"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, JAPAN","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001545412","display_name":"Kohei Miyaguchi","orcid":"https://orcid.org/0000-0002-6702-7780"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kohei Miyaguchi","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, JAPAN"],"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, JAPAN","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5021981442"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":4.7132,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.95504056,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9995999932289124,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9855999946594238,"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.9829000234603882,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/minimum-description-length","display_name":"Minimum description length","score":0.8587237596511841},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.8486332893371582},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6416982412338257},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6271652579307556},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.4245697259902954},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42348411679267883},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42085689306259155},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.419960618019104},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41016435623168945},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38671618700027466},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3546410799026489},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28519678115844727}],"concepts":[{"id":"https://openalex.org/C87465248","wikidata":"https://www.wikidata.org/wiki/Q1417790","display_name":"Minimum description length","level":2,"score":0.8587237596511841},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.8486332893371582},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6416982412338257},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6271652579307556},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.4245697259902954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42348411679267883},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42085689306259155},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.419960618019104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41016435623168945},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38671618700027466},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3546410799026489},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28519678115844727}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2016.7840601","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","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":22,"referenced_works":["https://openalex.org/W204865844","https://openalex.org/W300721812","https://openalex.org/W636097894","https://openalex.org/W1483365869","https://openalex.org/W1537984764","https://openalex.org/W1991485860","https://openalex.org/W2000381708","https://openalex.org/W2044535354","https://openalex.org/W2054658115","https://openalex.org/W2057797785","https://openalex.org/W2058148593","https://openalex.org/W2099419573","https://openalex.org/W2120587290","https://openalex.org/W2137446376","https://openalex.org/W2166064672","https://openalex.org/W2345132057","https://openalex.org/W2639550143","https://openalex.org/W4238201623","https://openalex.org/W6628808406","https://openalex.org/W6675040714","https://openalex.org/W6704944113","https://openalex.org/W6830627584"],"related_works":["https://openalex.org/W3199009818","https://openalex.org/W2583850415","https://openalex.org/W4226248369","https://openalex.org/W3126974661","https://openalex.org/W2991529710","https://openalex.org/W1995014849","https://openalex.org/W2106419767","https://openalex.org/W2964556660","https://openalex.org/W3093275957","https://openalex.org/W1505086392"],"abstract_inverted_index":{"In":[0,51],"this":[1],"paper":[2],"we":[3,53],"propose":[4],"a":[5,39],"novel":[6],"methodology":[7],"of":[8,48,69,110],"sequential":[9,85],"change":[10,32,86],"detection":[11,61,87,138],"using":[12],"the":[13,22,26,29,46,55,59,66,70,80,84,108,111],"minimum":[14],"description":[15],"length":[16],"(MDL)-change":[17],"statistics.":[18],"We":[19,37,77,105,126],"first":[20],"introduce":[21],"MDL-change":[23,60,81],"statistics":[24,82],"as":[25,96,98,135],"difference":[27],"between":[28],"code-lengths":[30],"with":[31],"and":[33,73,139],"that":[34,116],"without":[35],"change.":[36],"give":[38],"theoretical":[40],"justification":[41],"for":[42,58,123],"its":[43,129],"use":[44],"in":[45],"scenario":[47],"hypothesis":[49],"testing.":[50],"it":[52,117],"evaluate":[54],"error":[56],"probabilities":[57],"to":[62,65,92],"relate":[63],"them":[64],"information-theoretic":[67],"complexities":[68],"probabilistic":[71],"models":[72],"their":[74],"discrepancy":[75],"measure.":[76],"then":[78],"convert":[79],"into":[83],"algorithm.":[88],"It":[89],"is":[90],"designed":[91],"detect":[93],"gradual":[94],"changes":[95,100],"well":[97],"abrupt":[99],"from":[101],"big":[102],"stream":[103],"data.":[104,125],"empirically":[106],"demonstrate":[107],"effectiveness":[109],"proposed":[112],"method":[113],"by":[114],"showing":[115],"performs":[118],"better":[119],"than":[120],"existing":[121],"algorithms":[122],"synthetic":[124],"also":[127],"show":[128],"validity":[130],"through":[131],"real":[132],"problems":[133],"such":[134],"SQL":[136],"injection":[137],"failure":[140],"symptom":[141],"detection.":[142]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
