{"id":"https://openalex.org/W1982516027","doi":"https://doi.org/10.1109/hpec.2014.7040944","title":"Big data dimensional analysis","display_name":"Big data dimensional analysis","publication_year":2014,"publication_date":"2014-09-01","ids":{"openalex":"https://openalex.org/W1982516027","doi":"https://doi.org/10.1109/hpec.2014.7040944","mag":"1982516027"},"language":"en","primary_location":{"id":"doi:10.1109/hpec.2014.7040944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec.2014.7040944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1408.0517","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043450560","display_name":"Vijay Gadepally","orcid":"https://orcid.org/0000-0002-4598-2808"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]},{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vijay Gadepally","raw_affiliation_strings":["MIT Lincoln Laboratory, Lexington, MA","[MIT Lincoln Laboratory, Lexington, MA 02420, USA]"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory, Lexington, MA","institution_ids":["https://openalex.org/I4210122954"]},{"raw_affiliation_string":"[MIT Lincoln Laboratory, Lexington, MA 02420, USA]","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072108599","display_name":"Jeremy Kepner","orcid":"https://orcid.org/0000-0001-9668-2613"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]},{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeremy Kepner","raw_affiliation_strings":["MIT Lincoln Laboratory, Lexington, MA","[MIT Lincoln Laboratory, Lexington, MA 02420, USA]"],"affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory, Lexington, MA","institution_ids":["https://openalex.org/I4210122954"]},{"raw_affiliation_string":"[MIT Lincoln Laboratory, Lexington, MA 02420, USA]","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5043450560"],"corresponding_institution_ids":["https://openalex.org/I4210122954","https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":4.4161,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.95021954,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9990000128746033,"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":0.9990000128746033,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.998199999332428,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9950000047683716,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.9102163314819336},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8157575130462646},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.7628123760223389},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7163645625114441},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.6993135809898376},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5540207028388977},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.49708083271980286},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.493255078792572},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.4923213720321655},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47731074690818787},{"id":"https://openalex.org/keywords/data-analysis","display_name":"Data analysis","score":0.45450717210769653},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4289359450340271},{"id":"https://openalex.org/keywords/data-type","display_name":"Data type","score":0.4162244200706482},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.24762091040611267},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.14007297158241272}],"concepts":[{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.9102163314819336},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8157575130462646},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.7628123760223389},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7163645625114441},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.6993135809898376},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5540207028388977},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.49708083271980286},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.493255078792572},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.4923213720321655},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47731074690818787},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.45450717210769653},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4289359450340271},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.4162244200706482},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.24762091040611267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.14007297158241272},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/hpec.2014.7040944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec.2014.7040944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1408.0517","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1408.0517","pdf_url":"https://arxiv.org/pdf/1408.0517","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.760.257","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.760.257","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/1408.0517.pdf","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1408.0517","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1408.0517","pdf_url":"https://arxiv.org/pdf/1408.0517","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W26556108","https://openalex.org/W1964530510","https://openalex.org/W2048006137","https://openalex.org/W2089497633","https://openalex.org/W2125584498","https://openalex.org/W2147717514","https://openalex.org/W2539193928","https://openalex.org/W3102499167"],"related_works":["https://openalex.org/W4226266853","https://openalex.org/W4210252074","https://openalex.org/W3092201768","https://openalex.org/W2796632413","https://openalex.org/W2740083192","https://openalex.org/W2794907032","https://openalex.org/W4255802207","https://openalex.org/W4299701476","https://openalex.org/W2904574413","https://openalex.org/W2462007151"],"abstract_inverted_index":{"The":[0,18,212],"ability":[1],"to":[2,70,76,91,97,120,149,172,202,222],"collect":[3],"and":[4,23,39,57,86,127,180,208,218],"analyze":[5],"large":[6],"amounts":[7],"of":[8,42,59,73,129,155,170,177,205,214],"data":[9,22,36,49,81,99,130,134,147,171,179,193,206,225],"is":[10,51,65,140,216],"a":[11,68,93,141,156,167,203],"growing":[12,19],"problem":[13],"within":[14],"the":[15,31,43,54,60,71,77,104,124,152,175,178],"scientific":[16],"community.":[17],"gap":[20],"between":[21],"users":[24],"calls":[25],"for":[26,123],"innovative":[27],"tools":[28],"that":[29,88,144,164,188],"address":[30],"challenges":[32,45],"faced":[33],"by":[34,132],"big":[35,48,80,133,146,157,192,224],"volume,":[37,125],"velocity":[38,126],"variety.":[40],"One":[41],"main":[44],"associated":[46],"with":[47],"variety":[50,128],"automatically":[52],"understanding":[53,64,98],"underlying":[55],"structures":[56,163],"patterns":[58],"data.":[61,78],"Such":[62],"an":[63],"required":[66],"as":[67],"pre-requisite":[69],"application":[72],"advanced":[74],"analytics":[75],"Further,":[79],"sets":[82],"often":[83,114],"contain":[84],"anomalies":[85],"errors":[87],"are":[89,101,111,189],"difficult":[90],"know":[92],"priori.":[94],"Current":[95],"approaches":[96,110],"structure":[100,154],"drawn":[102],"from":[103],"traditional":[105],"database":[106],"ontology":[107],"design.":[108],"These":[109],"effective,":[112],"but":[113],"require":[115],"too":[116],"much":[117],"human":[118],"involvement":[119],"be":[121,220],"effective":[122],"encountered":[131],"systems.":[135],"Dimensional":[136],"Data":[137],"Analysis":[138],"(DDA)":[139],"proposed":[142],"technique":[143],"allows":[145],"analysts":[148],"quickly":[150,173],"understand":[151],"overall":[153],"dataset,":[158],"determine":[159,174],"anomalies.":[160,183],"DDA":[161,184,215],"exploits":[162],"exist":[165],"in":[166,191],"wide":[168],"class":[169],"nature":[176],"its":[181,210],"statical":[182],"leverages":[185],"existing":[186,223],"schemas":[187],"employed":[190],"databases":[194],"today.":[195],"This":[196],"paper":[197],"presents":[198],"DDA,":[199],"applies":[200],"it":[201],"number":[204],"sets,":[207],"measures":[209],"performance.":[211],"overhead":[213],"low":[217],"can":[219],"applied":[221],"systems":[226],"without":[227],"greatly":[228],"impacting":[229],"their":[230],"computing":[231],"requirements.":[232]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":7}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}