{"id":"https://openalex.org/W2076529558","doi":"https://doi.org/10.1109/cnsm.2014.7014137","title":"Mining temporal lag from fluctuating events for correlation and root cause analysis","display_name":"Mining temporal lag from fluctuating events for correlation and root cause analysis","publication_year":2014,"publication_date":"2014-11-01","ids":{"openalex":"https://openalex.org/W2076529558","doi":"https://doi.org/10.1109/cnsm.2014.7014137","mag":"2076529558"},"language":"en","primary_location":{"id":"doi:10.1109/cnsm.2014.7014137","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnsm.2014.7014137","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"10th International Conference on Network and Service Management (CNSM) and Workshop","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/A5011977282","display_name":"Chunqiu Zeng","orcid":"https://orcid.org/0000-0003-3279-5023"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chunqiu Zeng","raw_affiliation_strings":["School of Computer Science, Florida International University Miami, FL, USA","School of Computer Science, Florida International University, Miami, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Florida International University Miami, FL, USA","institution_ids":["https://openalex.org/I19700959"]},{"raw_affiliation_string":"School of Computer Science, Florida International University, Miami, USA#TAB#","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100723091","display_name":"Liang Tang","orcid":"https://orcid.org/0000-0002-6731-4544"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liang Tang","raw_affiliation_strings":["School of Computer Science, Florida International University Miami, FL, USA","School of Computer Science, Florida International University, Miami, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Florida International University Miami, FL, USA","institution_ids":["https://openalex.org/I19700959"]},{"raw_affiliation_string":"School of Computer Science, Florida International University, Miami, USA#TAB#","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455259","display_name":"Tao Li","orcid":"https://orcid.org/0000-0001-9277-1539"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tao Li","raw_affiliation_strings":["School of Computer Science, Florida International University Miami, FL, USA","School of Computer Science, Florida International University, Miami, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Florida International University Miami, FL, USA","institution_ids":["https://openalex.org/I19700959"]},{"raw_affiliation_string":"School of Computer Science, Florida International University, Miami, USA#TAB#","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037060213","display_name":"Larisa Shwartz","orcid":"https://orcid.org/0000-0001-5878-0765"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Larisa Shwartz","raw_affiliation_strings":["Operational Innovations IBM T.J., Watson Research Center Yorktown Heights, NY, USA","Operational Innovations, IBM T.J. Watson Res. Center, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"Operational Innovations IBM T.J., Watson Research Center Yorktown Heights, NY, USA","institution_ids":[]},{"raw_affiliation_string":"Operational Innovations, IBM T.J. Watson Res. Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031125475","display_name":"Genady Ya. Grabarnik","orcid":"https://orcid.org/0000-0001-8068-0920"},"institutions":[{"id":"https://openalex.org/I142823887","display_name":"St. John's University","ror":"https://ror.org/00bgtad15","country_code":"US","type":"education","lineage":["https://openalex.org/I142823887"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Genady Ya. Grabarnik","raw_affiliation_strings":["Dept. of Math & Computer Science, St. John's University Queens, NY, USA","Department of Math & Computer Science, St. John's University, Queens, NY, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Math & Computer Science, St. John's University Queens, NY, USA","institution_ids":["https://openalex.org/I142823887"]},{"raw_affiliation_string":"Department of Math & Computer Science, St. John's University, Queens, NY, USA","institution_ids":["https://openalex.org/I142823887"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5011977282"],"corresponding_institution_ids":["https://openalex.org/I19700959"],"apc_list":null,"apc_paid":null,"fwci":8.1804,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.97168559,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"19","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9993000030517578,"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":0.9991999864578247,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9842000007629395,"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/computer-science","display_name":"Computer science","score":0.7715795636177063},{"id":"https://openalex.org/keywords/lag","display_name":"Lag","score":0.7123631238937378},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6591971516609192},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.5726933479309082},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5336312651634216},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4807598292827606},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.45741671323776245},{"id":"https://openalex.org/keywords/stock-market-prediction","display_name":"Stock market prediction","score":0.42002564668655396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4181942343711853},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39218658208847046},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.31150585412979126},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15270769596099854},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11112093925476074}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7715795636177063},{"id":"https://openalex.org/C75778745","wikidata":"https://www.wikidata.org/wiki/Q342626","display_name":"Lag","level":2,"score":0.7123631238937378},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6591971516609192},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.5726933479309082},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5336312651634216},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4807598292827606},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.45741671323776245},{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.42002564668655396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4181942343711853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39218658208847046},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.31150585412979126},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15270769596099854},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11112093925476074},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cnsm.2014.7014137","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnsm.2014.7014137","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"10th International Conference on Network and Service Management (CNSM) and Workshop","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.724.1492","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.724.1492","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://users.cis.fiu.edu/%7Eczeng001/download/paper/event-mining.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.8299999833106995,"display_name":"Climate action"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W20722260","https://openalex.org/W1499478889","https://openalex.org/W1573592400","https://openalex.org/W1584003909","https://openalex.org/W1601795611","https://openalex.org/W1641039719","https://openalex.org/W1663973292","https://openalex.org/W1965555277","https://openalex.org/W1990800901","https://openalex.org/W2014080776","https://openalex.org/W2047106425","https://openalex.org/W2068383400","https://openalex.org/W2100169722","https://openalex.org/W2119370347","https://openalex.org/W2148356029","https://openalex.org/W2164405010","https://openalex.org/W2168505553","https://openalex.org/W2952753488","https://openalex.org/W6629940001","https://openalex.org/W6635016272","https://openalex.org/W6636681434","https://openalex.org/W6677888985","https://openalex.org/W6681934041","https://openalex.org/W6684719391"],"related_works":["https://openalex.org/W2167436245","https://openalex.org/W2897734105","https://openalex.org/W1995239016","https://openalex.org/W96694040","https://openalex.org/W2313140552","https://openalex.org/W1983035395","https://openalex.org/W4403677605","https://openalex.org/W4322494972","https://openalex.org/W1610502790","https://openalex.org/W4399092662"],"abstract_inverted_index":{"The":[0],"importance":[1],"of":[2,6,31,86,153,172],"mining":[3],"time":[4,29,53,84,94,116,129,143],"lags":[5,30,95],"hidden":[7],"temporal":[8,32,67,87],"dependencies":[9,33,68,88],"from":[10,69],"sequential":[11,40,58,71],"data":[12,41,162],"is":[13,75,124],"highlighted":[14],"in":[15,89],"many":[16],"domains":[17],"including":[18],"system":[19],"management,":[20],"stock":[21],"market":[22],"analysis,":[23],"climate":[24],"monitoring,":[25],"and":[26,42,99,160,170],"more.":[27],"Mining":[28],"provides":[34],"useful":[35],"insights":[36],"into":[37],"understanding":[38],"the":[39,51,57,66,70,90,128,147,168],"predicting":[43],"its":[44],"evolving":[45],"trend.":[46],"Traditional":[47],"methods":[48,81],"mainly":[49],"utilize":[50],"predefined":[52],"window":[54],"to":[55,64,82,101,113,126,145,166],"analyze":[56],"items":[59],"or":[60],"employ":[61],"statistic":[62],"techniques":[63],"identify":[65],"data.":[72],"However,":[73],"it":[74],"a":[76,110],"challenging":[77],"task":[78],"for":[79,141],"existing":[80],"find":[83,127],"lag":[85,130,144],"real":[91,161],"world,":[92],"where":[93],"are":[96,164],"fluctuating,":[97],"noisy,":[98],"tend":[100],"be":[102],"interleaved":[103],"with":[104,131],"each":[105],"other.":[106],"This":[107,134],"paper":[108,135],"introduces":[109],"parametric":[111],"model":[112],"describe":[114],"noisy":[115],"lags.":[117],"Then":[118],"an":[119,138],"efficient":[120],"expectation":[121],"maximization":[122],"approach":[123],"proposed":[125,173],"maximum":[132],"likelihood.":[133],"also":[136],"contributes":[137],"approximation":[139],"method":[140],"learning":[142],"improve":[146],"scalability":[148],"without":[149],"incurring":[150],"significant":[151],"loss":[152],"accuracy.":[154],"Extensive":[155],"experiments":[156],"on":[157],"both":[158],"synthetic":[159],"sets":[163],"conducted":[165],"demonstrate":[167],"effectiveness":[169],"efficiency":[171],"methods.":[174]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}