{"id":"https://openalex.org/W2060794276","doi":"https://doi.org/10.1109/icde.2014.6816644","title":"Incremental discovery of prominent situational facts","display_name":"Incremental discovery of prominent situational facts","publication_year":2014,"publication_date":"2014-03-01","ids":{"openalex":"https://openalex.org/W2060794276","doi":"https://doi.org/10.1109/icde.2014.6816644","mag":"2060794276"},"language":"en","primary_location":{"id":"doi:10.1109/icde.2014.6816644","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2014.6816644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE 30th International Conference on Data Engineering","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/A5113528630","display_name":"Afroza Sultana","orcid":"https://orcid.org/0009-0006-4136-5753"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Afroza Sultana","raw_affiliation_strings":["University of Texas at Arlington","University of Texas at Arlington,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Arlington","institution_ids":["https://openalex.org/I189196454"]},{"raw_affiliation_string":"University of Texas at Arlington,USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090641899","display_name":"Naeemul Hassan","orcid":"https://orcid.org/0000-0002-3951-6886"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Naeemul Hassan","raw_affiliation_strings":["University of Texas at Arlington","University of Texas at Arlington,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Arlington","institution_ids":["https://openalex.org/I189196454"]},{"raw_affiliation_string":"University of Texas at Arlington,USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084878734","display_name":"Chengkai Li","orcid":"https://orcid.org/0000-0002-1724-8278"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chengkai Li","raw_affiliation_strings":["University of Texas at Arlington","University of Texas at Arlington,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Arlington","institution_ids":["https://openalex.org/I189196454"]},{"raw_affiliation_string":"University of Texas at Arlington,USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101801967","display_name":"Jun Yang","orcid":"https://orcid.org/0000-0001-7888-2187"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Yang","raw_affiliation_strings":["Duke University","[Duke University, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]},{"raw_affiliation_string":"[Duke University, USA]","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043275971","display_name":"Cong Yu","orcid":"https://orcid.org/0000-0001-7331-2345"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cong Yu","raw_affiliation_strings":["Google Research","Google Research USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Research","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Research USA","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T11106","display_name":"Data Management and Algorithms","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9898999929428101,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9850999712944031,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/tuple","display_name":"Tuple","score":0.8612164855003357},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7344712018966675},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5784330368041992},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5401533842086792},{"id":"https://openalex.org/keywords/skyline","display_name":"Skyline","score":0.5361862778663635},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.47358447313308716},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4685087502002716},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4628447890281677},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4549221396446228},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4272604286670685},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4259895086288452},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3562668561935425},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.340938925743103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32670721411705017},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13777276873588562},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.09500452876091003}],"concepts":[{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.8612164855003357},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7344712018966675},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5784330368041992},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5401533842086792},{"id":"https://openalex.org/C2780757406","wikidata":"https://www.wikidata.org/wiki/Q465837","display_name":"Skyline","level":2,"score":0.5361862778663635},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.47358447313308716},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4685087502002716},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4628447890281677},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4549221396446228},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4272604286670685},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4259895086288452},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3562668561935425},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.340938925743103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32670721411705017},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13777276873588562},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.09500452876091003},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icde.2014.6816644","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2014.6816644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE 30th International Conference on Data Engineering","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.492.4935","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.492.4935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://ranger.uta.edu/~cli/pubs/2014/factmonitoring-icde14-cameraready-shlyy-nov13.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.764.6988","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.764.6988","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1311.4529.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1771934333","https://openalex.org/W2020319597","https://openalex.org/W2025155398","https://openalex.org/W2060794276","https://openalex.org/W2067021215","https://openalex.org/W2098247219","https://openalex.org/W2110012091","https://openalex.org/W2130696419","https://openalex.org/W2161597434","https://openalex.org/W2170188482","https://openalex.org/W2171717425","https://openalex.org/W2339296435","https://openalex.org/W2730629813","https://openalex.org/W2949573459","https://openalex.org/W4285719527","https://openalex.org/W6638110927"],"related_works":["https://openalex.org/W2904625095","https://openalex.org/W2030931773","https://openalex.org/W259531752","https://openalex.org/W2967037545","https://openalex.org/W2098338547","https://openalex.org/W2214117870","https://openalex.org/W2017776027","https://openalex.org/W2163693507","https://openalex.org/W1566588000","https://openalex.org/W2004796840"],"abstract_inverted_index":{"We":[0,165,186],"study":[1],"the":[2,54,112,118,143,195,208],"novel":[3],"problem":[4],"of":[5,45,53,57,66,101,212],"finding":[6],"new,":[7],"prominent":[8],"situational":[9,74],"facts,":[10],"which":[11],"are":[12,26,104,108,200],"emerging":[13],"statements":[14],"about":[15],"objects":[16,67],"that":[17,81,128],"stand":[18],"out":[19,83],"within":[20],"certain":[21],"contexts.":[22],"Many":[23],"such":[24],"facts":[25,47,197],"newsworthy-e.g.,":[27],"an":[28,63],"athlete's":[29],"outstanding":[30],"performance":[31],"in":[32,50,87,117,161,168],"a":[33,36,77,88,92,99,130,134,189],"game,":[34],"or":[35],"viral":[37],"video's":[38],"impressive":[39],"popularity.":[40],"Effective":[41],"and":[42,70,138,160,180,210],"efficient":[43],"identification":[44],"these":[46,171],"assists":[48],"journalists":[49],"reporting,":[51],"one":[52],"main":[55],"goals":[56],"computational":[58],"journalism.":[59],"Technically,":[60],"we":[61],"consider":[62],"ever-growing":[64],"table":[65],"with":[68,154],"dimension":[69,96],"measure":[71,102,163,184,192],"attributes.":[72],"A":[73,148],"fact":[75],"is":[76,123],"\u201ccontextual\u201d":[78],"skyline":[79,136],"tuple":[80,132],"stands":[82],"against":[84],"historical":[85],"tuples":[86,107],"context,":[89],"specified":[90],"by":[91],"conjunctive":[93],"constraint":[94,178],"involving":[95],"attributes,":[97],"when":[98,198],"set":[100],"attributes":[103],"compared.":[105],"New":[106],"constantly":[109],"added":[110],"to":[111,124,170,193],"table,":[113],"reflecting":[114],"events":[115],"happening":[116],"real":[119,205],"world.":[120],"Our":[121],"goal":[122],"discover":[125,139],"constraint-measure":[126],"pairs":[127],"qualify":[129],"new":[131],"as":[133],"contextual":[135],"tuple,":[137,156],"them":[140],"quickly":[141],"before":[142],"event":[144],"becomes":[145],"yesterday's":[146],"news.":[147],"brute-force":[149],"approach":[150],"requires":[151],"exhaustive":[152],"comparison":[153],"every":[155,158,162],"under":[157],"constraint,":[159],"subspace.":[164],"design":[166],"algorithms":[167],"response":[169],"challenges":[172],"using":[173],"three":[174],"corresponding":[175],"ideas-tuple":[176],"reduction,":[177],"pruning,":[179],"sharing":[181],"computation":[182],"across":[183],"subspaces.":[185],"also":[187],"adopt":[188],"simple":[190],"prominence":[191],"rank":[194],"discovered":[196],"they":[199],"numerous.":[201],"Experiments":[202],"over":[203],"two":[204],"datasets":[206],"validate":[207],"effectiveness":[209],"efficiency":[211],"our":[213],"techniques.":[214]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}