{"id":"https://openalex.org/W2147676257","doi":null,"title":"Anytime exploratory data analysis for massive data sets","display_name":"Anytime exploratory data analysis for massive data sets","publication_year":1997,"publication_date":"1997-08-14","ids":{"openalex":"https://openalex.org/W2147676257","mag":"2147676257"},"language":"en","primary_location":{"id":"mag:2147676257","doi":null,"is_oa":false,"landing_page_url":"https://aaaipress.org/Papers/KDD/1997/KDD97-009.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306420424","display_name":"Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Knowledge Discovery and Data Mining","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5077460655","display_name":"Padhraic Smyth","orcid":"https://orcid.org/0000-0001-9971-8378","observed_orcids":["https://orcid.org/0000-0001-9971-8378"]},"institutions":[{"id":"https://openalex.org/I122411786","display_name":"California Institute of Technology","ror":"https://ror.org/05dxps055","country_code":"US","type":"education","lineage":["https://openalex.org/I122411786"]},{"id":"https://openalex.org/I1334627681","display_name":"Jet Propulsion Laboratory","ror":"https://ror.org/027k65916","country_code":"US","type":"facility","lineage":["https://openalex.org/I122411786","https://openalex.org/I1334627681","https://openalex.org/I4210124779"]},{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Padhraic Smyth","raw_affiliation_strings":["Department of Information and Computer Science, University of California, Irvine, CA and Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA#TAB#"],"raw_orcid":"https://orcid.org/0000-0001-9971-8378","affiliations":[{"raw_affiliation_string":"Department of Information and Computer Science, University of California, Irvine, CA and Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA#TAB#","institution_ids":["https://openalex.org/I122411786","https://openalex.org/I1334627681","https://openalex.org/I204250578"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073514351","display_name":"David H. Wolpert","orcid":"https://orcid.org/0000-0003-3105-2869","observed_orcids":["https://orcid.org/0000-0003-3105-2869","https://orcid.org/0000-0001-5114-8361"]},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David H. Wolpert","raw_affiliation_strings":["IBM Almaden Research. San Jose, CA#TAB#"],"raw_orcid":"https://orcid.org/0000-0003-3105-2869","affiliations":[{"raw_affiliation_string":"IBM Almaden Research. San Jose, CA#TAB#","institution_ids":["https://openalex.org/I4210085935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3787,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.79449087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"54","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13141","display_name":"Statistical Methods and Applications","score":0.305400013923645,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13141","display_name":"Statistical Methods and Applications","score":0.305400013923645,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.1298000067472458,"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/T10136","display_name":"Statistical Methods and Inference","score":0.07829999923706055,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/exploratory-data-analysis","display_name":"exploratory data analysis","score":0.9769999980926514},{"id":"https://openalex.org/keywords/big-data","display_name":"big data","score":0.9860000014305115},{"id":"https://openalex.org/keywords/interactive-data-analysis","display_name":"interactive data analysis","score":0.7519999742507935},{"id":"https://openalex.org/keywords/multivariate-density-estimation","display_name":"multivariate density estimation","score":0.984000027179718},{"id":"https://openalex.org/keywords/stacking","display_name":"stacking","score":0.7580000162124634},{"id":"https://openalex.org/keywords/model-combination","display_name":"model combination","score":0.9620000123977661},{"id":"https://openalex.org/keywords/computational-constraints","display_name":"computational resource constraints","score":0.3869999945163727}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8292468786239624},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.658918559551239},{"id":"https://openalex.org/C120894424","wikidata":"https://www.wikidata.org/wiki/Q1322871","display_name":"Exploratory data analysis","level":2,"score":0.5358119606971741},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5204746723175049},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5132551193237305},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5109404921531677},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.44142067432403564},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.4318869709968567},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41072142124176025},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2639944553375244},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1351136863231659},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2147676257","doi":null,"is_oa":false,"landing_page_url":"https://aaaipress.org/Papers/KDD/1997/KDD97-009.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306420424","display_name":"Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Knowledge Discovery and Data Mining","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W23603061","https://openalex.org/W28412257","https://openalex.org/W1519698894","https://openalex.org/W1525110644","https://openalex.org/W1582937231","https://openalex.org/W1588509063","https://openalex.org/W1976129448","https://openalex.org/W2013702926","https://openalex.org/W2015734757","https://openalex.org/W2028347381","https://openalex.org/W2067304955","https://openalex.org/W2111051773","https://openalex.org/W2115980193","https://openalex.org/W2118948426","https://openalex.org/W2153711228","https://openalex.org/W2912934387","https://openalex.org/W2914369697"],"related_works":["https://openalex.org/W2278740622","https://openalex.org/W2256512886","https://openalex.org/W2574559610","https://openalex.org/W2237203044","https://openalex.org/W2884148178","https://openalex.org/W1692178976","https://openalex.org/W7057542737","https://openalex.org/W1882945533","https://openalex.org/W2104517908","https://openalex.org/W2118733287"],"abstract_inverted_index":{"Exploratory":[0],"data":[1,14,19,37,48,52,55,108,118],"analysis":[38],"is":[3,39,83,112,122],"inherently":[4],"an":[5],"iterative,":[6],"interactive":[7,89],"endeavor.":[8],"In":[9,33],"the":[10,62,65,71,107,137,141],"context":[11],"of":[12,50,64,143],"massive":[13,51],"sets,":[15],"however,":[16],"many":[17],"current":[18],"algorithms":[21],"will":[22],"not":[23,60],"scale":[24],"appropriately":[25],"to":[26,45,77,104,114,129,135],"permit":[27],"interaction":[28],"on":[29],"a":[30,42,144],"human":[31],"time-scale.":[32],"this":[34],"paper":[35],"anytime":[36],"proposed":[40],"as":[41,100],"general":[43],"framework":[44,82],"enable":[46],"exploratory":[47],"sets.":[53],"Anytime":[54],"takes":[57],"into":[58],"account":[59],"only":[61],"quality":[63],"model":[66,96],"being":[67],"fit":[68],"but":[69],"also":[70],"resources":[72],"(time":[73],"and":[74,95],"memory)":[75],"used":[76,103],"achieve":[78],"that":[79,124],"fit.":[80],"The":[81,110],"discussed":[84],"in":[85],"some":[86],"detail":[87],"for":[88],"multivariate":[90],"density":[91],"estimation.":[92],"Out-of-sample":[93],"log-likelihood":[94],"combination":[97],"techniques":[98],"(such":[99],"stacking)":[101],"are":[102],"greedily":[105],"explore":[106],"landscape.":[109],"method":[111],"applied":[113],"two":[115],"significant":[116],"scientific":[117],"sets":[119],"where":[120],"it":[121,125],"shown":[123],"can":[126],"be":[127],"better":[128],"combine":[130],"multiple":[131],"cheap-to-construct":[132],"models":[133],"than":[134],"spend":[136],"same":[138],"time":[139],"optimizing":[140],"parameters":[142],"single":[145],"more":[146],"complex":[147],"model.":[148]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
