{"id":"https://openalex.org/W2776558668","doi":"https://doi.org/10.1109/ldav.2017.8231848","title":"Sampling techniques to improve big data exploration","display_name":"Sampling techniques to improve big data exploration","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2776558668","doi":"https://doi.org/10.1109/ldav.2017.8231848","mag":"2776558668"},"language":"en","primary_location":{"id":"doi:10.1109/ldav.2017.8231848","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ldav.2017.8231848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 7th Symposium on Large Data Analysis and Visualization (LDAV)","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/A5110538885","display_name":"Julian A. Ramos Rojas","orcid":null},"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":true,"raw_author_name":"Julian A. Ramos Rojas","raw_affiliation_strings":["School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, US"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, US","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068961297","display_name":"Mary Beth Kery","orcid":"https://orcid.org/0000-0002-1771-0565"},"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":"Mary Beth Kery","raw_affiliation_strings":["School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, US"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, US","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086237721","display_name":"Stephanie Rosenthal","orcid":"https://orcid.org/0000-0002-7583-4590"},"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":"Stephanie Rosenthal","raw_affiliation_strings":["School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, US"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, US","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032134965","display_name":"Anind K. Dey","orcid":"https://orcid.org/0000-0002-3004-0770"},"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":"Anind Dey","raw_affiliation_strings":["School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, US"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, US","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5110538885"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":4.3941,"has_fulltext":false,"cited_by_count":43,"citation_normalized_percentile":{"value":0.95585364,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"26","last_page":"35"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9994999766349792,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9994999766349792,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9961000084877014,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.8054529428482056},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.7653000354766846},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.7401070594787598},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.6650494933128357},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5970777869224548},{"id":"https://openalex.org/keywords/experience-sampling-method","display_name":"Experience sampling method","score":0.5308564305305481},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.524594247341156},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.5215468406677246},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49081191420555115},{"id":"https://openalex.org/keywords/simple-random-sample","display_name":"Simple random sample","score":0.46749913692474365},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4203077256679535},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39879485964775085}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8054529428482056},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.7653000354766846},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.7401070594787598},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.6650494933128357},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5970777869224548},{"id":"https://openalex.org/C65499552","wikidata":"https://www.wikidata.org/wiki/Q5421061","display_name":"Experience sampling method","level":2,"score":0.5308564305305481},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.524594247341156},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.5215468406677246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49081191420555115},{"id":"https://openalex.org/C20353970","wikidata":"https://www.wikidata.org/wiki/Q1056998","display_name":"Simple random sample","level":3,"score":0.46749913692474365},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4203077256679535},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39879485964775085},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","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/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ldav.2017.8231848","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ldav.2017.8231848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 7th Symposium on Large Data Analysis and Visualization (LDAV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4000000059604645,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W144670803","https://openalex.org/W1494137514","https://openalex.org/W1513874326","https://openalex.org/W1549658602","https://openalex.org/W1553262910","https://openalex.org/W1736726159","https://openalex.org/W1978633512","https://openalex.org/W1987135115","https://openalex.org/W1996070121","https://openalex.org/W2015528223","https://openalex.org/W2044102377","https://openalex.org/W2070600700","https://openalex.org/W2099550922","https://openalex.org/W2101234009","https://openalex.org/W2110114082","https://openalex.org/W2130774862","https://openalex.org/W2133990480","https://openalex.org/W2138722877","https://openalex.org/W2143677795","https://openalex.org/W2151023586","https://openalex.org/W2160828669","https://openalex.org/W2171036274","https://openalex.org/W2319794630","https://openalex.org/W2516874331","https://openalex.org/W2903158431","https://openalex.org/W3016054986","https://openalex.org/W4285719527","https://openalex.org/W4300601563","https://openalex.org/W6675354045","https://openalex.org/W6756615331"],"related_works":["https://openalex.org/W2245479382","https://openalex.org/W2137479650","https://openalex.org/W4210350690","https://openalex.org/W2940392639","https://openalex.org/W3199845601","https://openalex.org/W2791222374","https://openalex.org/W2207685884","https://openalex.org/W2513483708","https://openalex.org/W3198122215","https://openalex.org/W2776558668"],"abstract_inverted_index":{"The":[0,27,199],"success":[1],"of":[2,10,25,29,44,142,154,188,220],"Big":[3,97],"Data":[4,42],"relies":[5],"fundamentally":[6],"on":[7,74,217],"the":[8,72,75,113,135,152,221],"ability":[9],"a":[11,37,45,52,107,126,170,176,227],"person":[12],"(the":[13],"data":[14,34,56,79,119,165,171,180,190,228],"scientist)":[15],"to":[16,68,121,148,174,215],"make":[17],"sense":[18],"and":[19,39,54,63,66,89,131,150],"generate":[20,69,211],"insights":[21,124,212],"from":[22,106,125,134,182,191],"this":[23,100],"wealth":[24],"data.":[26],"process":[28],"generating":[30,51],"actionable":[31],"insights,":[32],"called":[33],"exploration,":[35],"is":[36,91,112],"difficult":[38],"time-consuming":[40],"task.":[41],"exploration":[43,172],"big":[46,127],"dataset":[47,128],"usually":[48],"requires":[49],"first":[50,103],"small":[53],"representative":[55],"sample":[57],"that":[58,109,139,203,213],"can":[59,210],"be":[60],"easily":[61],"plotted":[62],"viewed,":[64],"managed":[65],"interpreted":[67],"insights.":[70],"However,":[71],"literature":[73],"topic":[76],"hints":[77],"at":[78],"scientists":[80,120,168],"only":[81,114],"using":[82,143,179,195],"random":[83,110,208],"sampling":[84,111,145,156,197,204,209],"with":[85,96,163],"regular":[86],"sized":[87],"datasets":[88],"it":[90],"unclear":[92],"what":[93],"they":[94],"do":[95],"Data.":[98],"In":[99],"work,":[101],"we":[102,158],"show":[104],"evidence":[105,133],"survey":[108],"technique":[115],"commonly":[116],"used":[117],"by":[118],"quickly":[122],"gain":[123],"despite":[129],"theoretical":[130],"empirical":[132],"active":[136],"learning":[137],"community":[138],"suggests":[140],"benefits":[141,153],"other":[144,155,206],"techniques.":[146,198],"Second,":[147],"evaluate":[149],"demonstrate":[151,202],"techniques,":[157],"conducted":[159],"an":[160],"online":[161],"study":[162,200],"34":[164],"scientists.":[166],"These":[167],"performed":[169],"task":[173],"support":[175],"classification":[177],"goal":[178],"samples":[181],"more":[183],"than":[184,207],"2":[185],"million":[186],"records":[187],"editing":[189],"Wikipedia":[192],"articles,":[193],"generated":[194],"different":[196,218],"results":[201],"techniques":[205],"help":[214],"focus":[216],"characteristics":[219],"data,":[222],"without":[223],"compromising":[224],"quality":[225],"in":[226],"exploration.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":5}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}