{"id":"https://openalex.org/W2142035328","doi":"https://doi.org/10.14778/1687627.1687701","title":"Coordinated weighted sampling for estimating aggregates over multiple weight assignments","display_name":"Coordinated weighted sampling for estimating aggregates over multiple weight assignments","publication_year":2009,"publication_date":"2009-08-01","ids":{"openalex":"https://openalex.org/W2142035328","doi":"https://doi.org/10.14778/1687627.1687701","mag":"2142035328"},"language":"en","primary_location":{"id":"doi:10.14778/1687627.1687701","is_oa":false,"landing_page_url":"https://doi.org/10.14778/1687627.1687701","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-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/A5026385549","display_name":"Edith Cohen","orcid":"https://orcid.org/0000-0002-3926-8237"},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Edith Cohen","raw_affiliation_strings":["AT&T Labs--Research, Florham Park, NJ"],"affiliations":[{"raw_affiliation_string":"AT&T Labs--Research, Florham Park, NJ","institution_ids":["https://openalex.org/I1283103587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006699796","display_name":"Haim Kaplan","orcid":"https://orcid.org/0000-0001-9586-8002"},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Haim Kaplan","raw_affiliation_strings":["Tel Aviv University, Tel Aviv, Israel","Tel Aviv University, Tel Aviv, Israel,"],"affiliations":[{"raw_affiliation_string":"Tel Aviv University, Tel Aviv, Israel","institution_ids":["https://openalex.org/I16391192"]},{"raw_affiliation_string":"Tel Aviv University, Tel Aviv, Israel,","institution_ids":["https://openalex.org/I16391192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114832181","display_name":"Subhabrata Sen","orcid":"https://orcid.org/0009-0005-5969-1023"},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Subhabrata Sen","raw_affiliation_strings":["AT&T Labs--Research, Florham Park, NJ"],"affiliations":[{"raw_affiliation_string":"AT&T Labs--Research, Florham Park, NJ","institution_ids":["https://openalex.org/I1283103587"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026385549"],"corresponding_institution_ids":["https://openalex.org/I1283103587"],"apc_list":null,"apc_paid":null,"fwci":3.1052,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.93580998,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"2","issue":"1","first_page":"646","last_page":"657"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9984999895095825,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9984999895095825,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9975000023841858,"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/T11719","display_name":"Data Quality and Management","score":0.9968000054359436,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.78973788022995},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7292989492416382},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6222752928733826},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6082848310470581},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5143369436264038},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4954169988632202},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.45064857602119446},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41029518842697144},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3522668480873108},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3347782492637634},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16455069184303284}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.78973788022995},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7292989492416382},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6222752928733826},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6082848310470581},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5143369436264038},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4954169988632202},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.45064857602119446},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41029518842697144},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3522668480873108},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3347782492637634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16455069184303284},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.14778/1687627.1687701","is_oa":false,"landing_page_url":"https://doi.org/10.14778/1687627.1687701","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.249.5145","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.249.5145","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/0906.4560v1.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":60,"referenced_works":["https://openalex.org/W1519331563","https://openalex.org/W1571664355","https://openalex.org/W1584212476","https://openalex.org/W1609518033","https://openalex.org/W1965996575","https://openalex.org/W1972076792","https://openalex.org/W1979795732","https://openalex.org/W1979819093","https://openalex.org/W1982682305","https://openalex.org/W1984566373","https://openalex.org/W1985895460","https://openalex.org/W1992363839","https://openalex.org/W1993482412","https://openalex.org/W1994945255","https://openalex.org/W2001947543","https://openalex.org/W2012833704","https://openalex.org/W2013809345","https://openalex.org/W2027689065","https://openalex.org/W2028831670","https://openalex.org/W2029685080","https://openalex.org/W2036304306","https://openalex.org/W2045555847","https://openalex.org/W2067432306","https://openalex.org/W2068519756","https://openalex.org/W2068936568","https://openalex.org/W2077599515","https://openalex.org/W2080745194","https://openalex.org/W2085845250","https://openalex.org/W2092236286","https://openalex.org/W2102481563","https://openalex.org/W2111295912","https://openalex.org/W2111806841","https://openalex.org/W2112400233","https://openalex.org/W2115215982","https://openalex.org/W2119026482","https://openalex.org/W2119885577","https://openalex.org/W2123845384","https://openalex.org/W2126356486","https://openalex.org/W2126540423","https://openalex.org/W2127455097","https://openalex.org/W2128891547","https://openalex.org/W2132069633","https://openalex.org/W2134786002","https://openalex.org/W2145349611","https://openalex.org/W2148885851","https://openalex.org/W2150517569","https://openalex.org/W2152617961","https://openalex.org/W2153180170","https://openalex.org/W2156943642","https://openalex.org/W2167450247","https://openalex.org/W2167716306","https://openalex.org/W2341535507","https://openalex.org/W2752853835","https://openalex.org/W4232440580","https://openalex.org/W4233471163","https://openalex.org/W4236629897","https://openalex.org/W4237793107","https://openalex.org/W6636190696","https://openalex.org/W6669417358","https://openalex.org/W6679663036"],"related_works":["https://openalex.org/W2366403280","https://openalex.org/W1495108544","https://openalex.org/W2091301346","https://openalex.org/W3148229873","https://openalex.org/W2150160875","https://openalex.org/W4242223894","https://openalex.org/W1517524280","https://openalex.org/W4389760904","https://openalex.org/W4306886878","https://openalex.org/W2594414941"],"abstract_inverted_index":{"Many":[0],"data":[1,47,83,115,170],"sources":[2],"are":[3,49,86,112,148],"naturally":[4],"modeled":[5],"by":[6],"multiple":[7,21,28,36,41,67,141],"weight":[8,121,142],"assignments":[9,143],"over":[10,27,66],"a":[11,118,129],"set":[12,57],"of":[13,16,58,69,150,160],"keys:":[14],"snapshots":[15],"an":[17,164],"evolving":[18],"database":[19],"at":[20,35],"points":[22],"in":[23,51],"time,":[24],"measurements":[25],"collected":[26],"time":[29],"periods,":[30],"requests":[31],"for":[32,82,114,140],"resources":[33],"served":[34],"locations,":[37],"and":[38,64,144],"records":[39],"with":[40,53,124],"numeric":[42],"attributes.":[43],"Over":[44],"such":[45,60,71],"vector-weighted":[46],"we":[48],"interested":[50],"aggregates":[52,65],"respect":[54],"to":[55,89,176],"one":[56],"weights,":[59],"as":[61,72],"weighted":[62,135],"sums,":[63],"sets":[68,84,116,171],"weights":[70],"the":[73,105,158],"L":[74],"1":[75],"difference.":[76],"Sample-based":[77],"summarization":[78],"is":[79,122,138],"highly":[80],"effective":[81],"that":[85,100,137,147],"too":[87],"large":[88],"be":[90,102],"stored":[91],"or":[92],"manipulated.":[93],"The":[94],"summary":[95,106],"facilitates":[96],"approximate":[97],"processing":[98],"queries":[99],"may":[101],"specified":[103],"after":[104],"was":[107],"generated.":[108],"Current":[109],"designs,":[110],"however,":[111],"geared":[113],"where":[117],"single":[119],"scalar":[120],"associated":[123],"each":[125],"key.":[126],"We":[127,156],"develop":[128],"sampling":[130],"framework":[131],"based":[132],"on":[133,168],"coordinated":[134],"samples":[136],"suited":[139],"obtain":[145],"estimators":[146],"orders":[149],"magnitude":[151],"tighter":[152],"than":[153],"previously":[154],"possible.":[155],"demonstrate":[157],"power":[159],"our":[161],"methods":[162],"through":[163],"extensive":[165],"empirical":[166],"evaluation":[167],"diverse":[169],"ranging":[172],"from":[173],"IP":[174],"network":[175],"stock":[177],"quotes":[178],"data.":[179]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":7},{"year":2012,"cited_by_count":3}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}