{"id":"https://openalex.org/W2614062760","doi":"https://doi.org/10.1145/3035918.3064026","title":"Beta Probabilistic Databases","display_name":"Beta Probabilistic Databases","publication_year":2017,"publication_date":"2017-05-09","ids":{"openalex":"https://openalex.org/W2614062760","doi":"https://doi.org/10.1145/3035918.3064026","mag":"2614062760"},"language":"en","primary_location":{"id":"doi:10.1145/3035918.3064026","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3035918.3064026","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3064026&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM International Conference on Management of Data","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://dl.acm.org/ft_gateway.cfm?id=3064026&type=pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025993283","display_name":"Niccol\u00f2 Meneghetti","orcid":"https://orcid.org/0000-0002-4107-1759"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Niccolo' Meneghetti","raw_affiliation_strings":["HPE Vertica, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"HPE Vertica, Cambridge, MA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071259962","display_name":"Oliver Kennedy","orcid":"https://orcid.org/0000-0003-0632-1668"},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Oliver Kennedy","raw_affiliation_strings":["University at Buffalo, Buffalo, NY, USA"],"affiliations":[{"raw_affiliation_string":"University at Buffalo, Buffalo, NY, USA","institution_ids":["https://openalex.org/I63190737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086781628","display_name":"Wolfgang Gatterbauer","orcid":"https://orcid.org/0000-0002-9614-0504"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wolfgang Gatterbauer","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5025993283"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4157,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.71102023,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"573","last_page":"586"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9991000294685364,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9991000294685364,"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/T11106","display_name":"Data Management and Algorithms","score":0.9973999857902527,"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":0.9968000054359436,"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/tuple","display_name":"Tuple","score":0.8345925807952881},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.735181450843811},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6822060346603394},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6290507316589355},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5786874294281006},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.5368877053260803},{"id":"https://openalex.org/keywords/probabilistic-database","display_name":"Probabilistic database","score":0.5063282251358032},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.4253968298435211},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.39258331060409546},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3540174961090088},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34386032819747925},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32646411657333374},{"id":"https://openalex.org/keywords/database-theory","display_name":"Database theory","score":0.252490758895874},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1505422592163086}],"concepts":[{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.8345925807952881},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.735181450843811},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6822060346603394},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6290507316589355},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5786874294281006},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.5368877053260803},{"id":"https://openalex.org/C174539288","wikidata":"https://www.wikidata.org/wiki/Q7246853","display_name":"Probabilistic database","level":4,"score":0.5063282251358032},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.4253968298435211},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.39258331060409546},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3540174961090088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34386032819747925},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32646411657333374},{"id":"https://openalex.org/C12439846","wikidata":"https://www.wikidata.org/wiki/Q4809258","display_name":"Database theory","level":3,"score":0.252490758895874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1505422592163086},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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":1,"locations":[{"id":"doi:10.1145/3035918.3064026","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3035918.3064026","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3064026&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM International Conference on Management of Data","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3035918.3064026","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3035918.3064026","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=3064026&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM International Conference on Management of Data","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G133560476","display_name":"TWC: Medium: Collaborative: Data is Social: Exploiting Data Relationships to Detect Insider Attacks","funder_award_id":"1409551","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1691658952","display_name":null,"funder_award_id":"SaTC-1409551 and IIS-1553547","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2593170332","display_name":null,"funder_award_id":"IIS-1553547","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7896423399","display_name":null,"funder_award_id":"N00244-16-1- 0022","funder_id":"https://openalex.org/F4320307904","funder_display_name":"Oracle"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8672595501","display_name":null,"funder_award_id":"CAREER IIS-1553547","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320307904","display_name":"Oracle","ror":"https://ror.org/006c77m33"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2614062760.pdf","grobid_xml":"https://content.openalex.org/works/W2614062760.grobid-xml"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W806545817","https://openalex.org/W1215345351","https://openalex.org/W1515568087","https://openalex.org/W1518043585","https://openalex.org/W1552694902","https://openalex.org/W1558832481","https://openalex.org/W1608748481","https://openalex.org/W1824971879","https://openalex.org/W1869322803","https://openalex.org/W1963853643","https://openalex.org/W1967708238","https://openalex.org/W1968464072","https://openalex.org/W1977970897","https://openalex.org/W1991567646","https://openalex.org/W1992609556","https://openalex.org/W1994694864","https://openalex.org/W2020774087","https://openalex.org/W2029138824","https://openalex.org/W2049199200","https://openalex.org/W2049633694","https://openalex.org/W2053181911","https://openalex.org/W2058874014","https://openalex.org/W2069897123","https://openalex.org/W2078686663","https://openalex.org/W2079785597","https://openalex.org/W2086605907","https://openalex.org/W2088422262","https://openalex.org/W2094500233","https://openalex.org/W2099655235","https://openalex.org/W2105541078","https://openalex.org/W2106615669","https://openalex.org/W2129299803","https://openalex.org/W2140757415","https://openalex.org/W2143485006","https://openalex.org/W2144562386","https://openalex.org/W2144810465","https://openalex.org/W2150115297","https://openalex.org/W2163032274","https://openalex.org/W2166916904","https://openalex.org/W2169600045","https://openalex.org/W2266577992","https://openalex.org/W2293299776","https://openalex.org/W2322523654","https://openalex.org/W2333269907","https://openalex.org/W2399037375","https://openalex.org/W2471158680","https://openalex.org/W2567948266","https://openalex.org/W3122109288","https://openalex.org/W6672615329","https://openalex.org/W6675882535","https://openalex.org/W6683761602","https://openalex.org/W6684648153","https://openalex.org/W6720537222"],"related_works":["https://openalex.org/W1774414873","https://openalex.org/W2000084727","https://openalex.org/W2744606804","https://openalex.org/W2739264497","https://openalex.org/W2103934874","https://openalex.org/W4238495367","https://openalex.org/W2125434953","https://openalex.org/W2749065928","https://openalex.org/W2128160835","https://openalex.org/W4288558710"],"abstract_inverted_index":{"Tuple-independent":[0],"probabilistic":[1],"databases":[2],"(TI-PDBs)":[3],"handle":[4],"uncertainty":[5],"by":[6],"annotating":[7],"each":[8,27,107],"tuple":[9],"with":[10,216],"a":[11,18,78,94,110,129,187,207],"probability":[12],"parameter;":[13],"when":[14],"the":[15,20,23,49,55,66,146,157,161,169,195,203],"user":[16],"submits":[17],"query,":[19],"database":[21],"derives":[22],"marginal":[24],"probabilities":[25],"of":[26,51,59,69,80,102,160,171,182,194],"output-tuple,":[28],"assuming":[29],"input-tuples":[30],"are":[31],"statistically":[32],"independent.":[33],"While":[34],"query":[35,60,138],"processing":[36],"in":[37,93,128],"TI-PDBs":[38,81],"has":[39,45],"been":[40,46],"studied":[41],"extensively,":[42],"limited":[43],"research":[44],"dedicated":[47],"to":[48,83,105,144,154,201],"problems":[50],"updating":[52,88,124],"or":[53],"deriving":[54],"parameters":[56,162],"from":[57],"observations":[58],"results.":[61,219],"Addressing":[62],"this":[63,70,118,142],"problem":[64],"is":[65,104],"main":[67],"focus":[68],"paper.":[71],"We":[72,115,140],"introduce":[73],"Beta":[74],"Probabilistic":[75],"Databases":[76],"(B-PDBs),":[77],"generalization":[79],"designed":[82],"support":[84,213],"both":[85,122],"(i)":[86,150],"belief":[87,123,174],"and":[89,96,125],"(ii)":[90,166],"parameter":[91,108,126],"learning":[92,127],"principled":[95,130],"scalable":[97],"way.":[98],"The":[99],"key":[100,148],"idea":[101],"B-PDBs":[103],"treat":[106],"as":[109],"latent,":[111],"Beta-distributed":[112],"random":[113],"variable.":[114],"show":[116,152,199],"how":[117,153,200],"simple":[119],"expedient":[120],"enables":[121],"way,":[131],"without":[132],"imposing":[133],"any":[134],"burden":[135],"on":[136],"regular":[137],"processing.":[139],"use":[141],"model":[143],"provide":[145],"following":[147],"contributions:":[149],"we":[151,167,185,198,212],"scalably":[155],"compute":[156],"posterior":[158],"densities":[159],"given":[163],"new":[164],"evidence;":[165],"study":[168],"complexity":[170],"performing":[172],"Bayesian":[173],"updates,":[175],"devising":[176],"efficient":[177],"algorithms":[178,205],"for":[179,190],"tractable":[180],"classes":[181],"queries;":[183],"(iii)":[184],"propose":[186],"soft-EM":[188],"algorithm":[189],"computing":[191],"maximum-likelihood":[192],"estimates":[193],"parameters;":[196],"(iv)":[197],"embed":[202],"proposed":[204],"into":[206],"standard":[208],"relational":[209],"engine;":[210],"(v)":[211],"our":[214],"conclusions":[215],"extensive":[217],"experimental":[218]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}