{"id":"https://openalex.org/W2204875540","doi":"https://doi.org/10.1109/icde.2016.7498287","title":"Visualization-aware sampling for very large databases","display_name":"Visualization-aware sampling for very large databases","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2204875540","doi":"https://doi.org/10.1109/icde.2016.7498287","mag":"2204875540"},"language":"en","primary_location":{"id":"doi:10.1109/icde.2016.7498287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2016.7498287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","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/A5023168280","display_name":"Yongjoo Park","orcid":"https://orcid.org/0000-0003-3786-6214"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yongjoo Park","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039133265","display_name":"Michael Cafarella","orcid":"https://orcid.org/0000-0001-6122-0590"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Cafarella","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060287971","display_name":"Barzan Mozafari","orcid":"https://orcid.org/0000-0002-3581-3875"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Barzan Mozafari","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5023168280"],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":13.87,"has_fulltext":false,"cited_by_count":116,"citation_normalized_percentile":{"value":0.99184232,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"755","last_page":"766"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9993000030517578,"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":0.9993000030517578,"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/T10799","display_name":"Data Visualization and Analytics","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9858999848365784,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.8645363450050354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8503028750419617},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6530484557151794},{"id":"https://openalex.org/keywords/data-visualization","display_name":"Data visualization","score":0.5753496885299683},{"id":"https://openalex.org/keywords/interactive-visualization","display_name":"Interactive visualization","score":0.5622820854187012},{"id":"https://openalex.org/keywords/tuple","display_name":"Tuple","score":0.5496252775192261},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5474298000335693},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.510167121887207},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4840109050273895},{"id":"https://openalex.org/keywords/visual-analytics","display_name":"Visual analytics","score":0.4415702521800995},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2591591477394104},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.14633804559707642}],"concepts":[{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.8645363450050354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8503028750419617},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6530484557151794},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.5753496885299683},{"id":"https://openalex.org/C64073096","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Interactive visualization","level":3,"score":0.5622820854187012},{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.5496252775192261},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5474298000335693},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.510167121887207},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4840109050273895},{"id":"https://openalex.org/C59732488","wikidata":"https://www.wikidata.org/wiki/Q2528440","display_name":"Visual analytics","level":3,"score":0.4415702521800995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2591591477394104},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.14633804559707642},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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":1,"locations":[{"id":"doi:10.1109/icde.2016.7498287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2016.7498287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1626398438","https://openalex.org/W1656389077","https://openalex.org/W1680189815","https://openalex.org/W1964755716","https://openalex.org/W1971713783","https://openalex.org/W1999075826","https://openalex.org/W2002544066","https://openalex.org/W2003281274","https://openalex.org/W2035801804","https://openalex.org/W2036836182","https://openalex.org/W2037701287","https://openalex.org/W2043097023","https://openalex.org/W2063546264","https://openalex.org/W2065672113","https://openalex.org/W2071989194","https://openalex.org/W2081315591","https://openalex.org/W2089666999","https://openalex.org/W2100005810","https://openalex.org/W2104839312","https://openalex.org/W2106753423","https://openalex.org/W2110363867","https://openalex.org/W2113411758","https://openalex.org/W2113534353","https://openalex.org/W2118087562","https://openalex.org/W2131824593","https://openalex.org/W2136317921","https://openalex.org/W2138199375","https://openalex.org/W2138722877","https://openalex.org/W2139276812","https://openalex.org/W2140251882","https://openalex.org/W2152029707","https://openalex.org/W2161768947","https://openalex.org/W2165558283","https://openalex.org/W2211925278","https://openalex.org/W2242977838","https://openalex.org/W2257379155","https://openalex.org/W2257756289","https://openalex.org/W2293597654","https://openalex.org/W2296677182","https://openalex.org/W2397173492","https://openalex.org/W2401409094","https://openalex.org/W2615303257","https://openalex.org/W3003506411","https://openalex.org/W3118655244","https://openalex.org/W3143536790","https://openalex.org/W6676096338","https://openalex.org/W6679608865","https://openalex.org/W6712744764","https://openalex.org/W6737993660"],"related_works":["https://openalex.org/W2158984754","https://openalex.org/W3149127250","https://openalex.org/W2564956852","https://openalex.org/W4246764483","https://openalex.org/W4378086562","https://openalex.org/W2126824079","https://openalex.org/W3001728174","https://openalex.org/W2143428259","https://openalex.org/W2007038549","https://openalex.org/W2116732611"],"abstract_inverted_index":{"Interactive":[0],"visualizations":[1,20,94],"are":[2],"crucial":[3],"in":[4,21,41,53,132,182,200],"ad":[5],"hoc":[6],"data":[7,39,57],"exploration":[8],"and":[9,63,111,122,204],"analysis.":[10],"However,":[11],"with":[12,95,179],"the":[13,31,34,45,69,72,100,149,162,184],"growing":[14],"number":[15],"of":[16,33,99,136,151,165],"massive":[17],"datasets,":[18],"generating":[19],"interactive":[22],"timescales":[23],"is":[24,37,131],"increasingly":[25],"challenging.":[26],"One":[27],"approach":[28],"for":[29,81,114],"improving":[30],"speed":[32],"visualization":[35,54,80,117,202,211],"tool":[36],"via":[38],"reduction":[40,58],"order":[42],"to":[43,109,126,198,214],"reduce":[44],"computational":[46],"overhead,":[47],"but":[48],"at":[49],"a":[50,79,86,96,134,140,157,209],"potential":[51],"cost":[52],"accuracy.":[55],"Common":[56],"techniques,":[59],"such":[60],"as":[61],"uniform":[62],"stratified":[64],"sampling,":[65],"do":[66],"not":[67],"exploit":[68],"fact":[70],"that":[71,90,138,160,172,190],"sampled":[73],"tuples":[74,137],"will":[75],"be":[76],"transformed":[77],"into":[78],"human":[82],"consumption.":[83],"We":[84,103],"propose":[85],"visualization-aware":[87],"sampling":[88,128,146],"(VAS)":[89],"guarantees":[91],"high":[92],"quality":[93,212],"small":[97],"subset":[98],"entire":[101],"dataset.":[102],"validate":[104],"our":[105,127,173],"method":[106],"when":[107],"applied":[108],"scatter":[110,166],"map":[112],"plots":[113],"three":[115],"common":[116],"goals:":[118],"regression,":[119],"density":[120],"estimation,":[121],"clustering.":[123],"The":[124],"key":[125],"method's":[129],"success":[130,181,195],"choosing":[133],"set":[135],"minimizes":[139],"visualization-inspired":[141],"loss":[142,158,175],"function.":[143],"While":[144],"existing":[145],"approaches":[147],"minimize":[148],"error":[150],"aggregation":[152],"queries,":[153],"we":[154],"focus":[155],"on":[156],"function":[159,176],"maximizes":[161],"visual":[163],"fidelity":[164],"plots.":[167],"Our":[168,187],"user":[169,180],"study":[170],"confirms":[171],"proposed":[174],"correlates":[177],"strongly":[178],"using":[183],"resulting":[185],"visualizations.":[186],"experiments":[188],"show":[189],"(i)":[191],"VAS":[192,206],"improves":[193],"user's":[194],"by":[196],"up":[197,213],"35%":[199],"various":[201],"tasks,":[203],"(ii)":[205],"can":[207],"achieve":[208],"required":[210],"400\u00d7":[215],"faster.":[216]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":31},{"year":2018,"cited_by_count":12},{"year":2017,"cited_by_count":10},{"year":2016,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
