{"id":"https://openalex.org/W4391486360","doi":"https://doi.org/10.3233/ida-230638","title":"Floating-point histograms for exploratory analysis of large scale real-world data sets","display_name":"Floating-point histograms for exploratory analysis of large scale real-world data sets","publication_year":2024,"publication_date":"2024-02-02","ids":{"openalex":"https://openalex.org/W4391486360","doi":"https://doi.org/10.3233/ida-230638"},"language":"en","primary_location":{"id":"doi:10.3233/ida-230638","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-230638","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","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/A5111936967","display_name":"Marc Boull\u00e9","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Marc Boull\u00e9","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5111936967"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00947236,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":"5","first_page":"1347","last_page":"1394"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9987000226974487,"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/T11106","display_name":"Data Management and Algorithms","score":0.9965999722480774,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9916999936103821,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/real-world-data","display_name":"Real world data","score":0.5742173194885254},{"id":"https://openalex.org/keywords/exploratory-data-analysis","display_name":"Exploratory data analysis","score":0.555933952331543},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5498752593994141},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5385884046554565},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4568106234073639},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.44109079241752625},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.37787312269210815},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2855699956417084},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.26079079508781433},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2288171947002411},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.22687193751335144},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.223075270652771},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1971236765384674},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.07176190614700317}],"concepts":[{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.5742173194885254},{"id":"https://openalex.org/C120894424","wikidata":"https://www.wikidata.org/wiki/Q1322871","display_name":"Exploratory data analysis","level":2,"score":0.555933952331543},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5498752593994141},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5385884046554565},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4568106234073639},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.44109079241752625},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.37787312269210815},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2855699956417084},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.26079079508781433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2288171947002411},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.22687193751335144},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.223075270652771},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1971236765384674},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.07176190614700317},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/ida-230638","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-230638","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W18458893","https://openalex.org/W194523672","https://openalex.org/W1965575573","https://openalex.org/W1994727615","https://openalex.org/W1997597503","https://openalex.org/W2001592424","https://openalex.org/W2048092465","https://openalex.org/W2054658115","https://openalex.org/W2068782468","https://openalex.org/W2082773934","https://openalex.org/W2092443557","https://openalex.org/W2106596127","https://openalex.org/W2137130182","https://openalex.org/W2138790649","https://openalex.org/W2149620753","https://openalex.org/W2153957131","https://openalex.org/W2157564217","https://openalex.org/W2168114384","https://openalex.org/W2204310803","https://openalex.org/W2508307802","https://openalex.org/W2903185461","https://openalex.org/W2950627632","https://openalex.org/W3035965352","https://openalex.org/W3099878876","https://openalex.org/W3105120162","https://openalex.org/W3119955138","https://openalex.org/W4293212596","https://openalex.org/W4311989047","https://openalex.org/W6600730831","https://openalex.org/W6607949688","https://openalex.org/W6676455310"],"related_works":["https://openalex.org/W2107628111","https://openalex.org/W2394004323","https://openalex.org/W2398764543","https://openalex.org/W2027335291","https://openalex.org/W4210328553","https://openalex.org/W1980417906","https://openalex.org/W2007994675","https://openalex.org/W2071206959","https://openalex.org/W1892675750","https://openalex.org/W2139422440"],"abstract_inverted_index":{"Histograms":[0],"are":[1,19,55,154],"among":[2],"the":[3,29,45,63,84,90,121,142,164,167,174,180,188,191],"most":[4,51],"popular":[5],"methods":[6,54],"used":[7],"in":[8,44,62,179],"exploratory":[9,60,143,194],"analysis":[10,61,144],"to":[11,47,57,96,106,127,156],"summarize":[12],"univariate":[13],"distributions.":[14,77,131],"In":[15,78],"particular,":[16],"irregular":[17],"histograms":[18,98,125],"good":[20],"non-parametric":[21],"density":[22,177],"estimators":[23],"that":[24],"require":[25],"very":[26],"few":[27],"parameters:":[28],"number":[30],"of":[31,65,123,145,166,176,182,190],"bins":[32],"with":[33,69,120,170],"their":[34],"lengths":[35],"and":[36,137,187],"frequencies.":[37],"Although":[38],"many":[39],"approaches":[40],"have":[41],"been":[42],"proposed":[43],"literature":[46],"infer":[48],"these":[49],"parameters,":[50],"existing":[52],"histogram":[53,86],"difficult":[56,155],"exploit":[58],"for":[59,141,158,193],"case":[64,181],"real-world":[66,148],"data":[67,149,195],"sets,":[68,150],"scalability":[70],"issues,":[71],"truncated":[72],"data,":[73],"outliers":[74,128,183],"or":[75,129,184],"heavy-tailed":[76,130,185],"this":[79,108],"paper,":[80],"we":[81],"focus":[82],"on":[83,117],"G-Enum":[85],"method,":[87],"which":[88],"exploits":[89],"Minimum":[91],"Description":[92],"Length":[93],"(MDL)":[94],"principle":[95],"build":[97],"without":[99],"any":[100],"user":[101],"parameter.":[102],"We":[103,132],"then":[104],"propose":[105],"extend":[107],"method":[109],"by":[110],"exploiting":[111],"a":[112,138,171],"new":[113],"modeling":[114],"space":[115],"based":[116],"floating-point":[118],"representation,":[119],"objective":[122],"building":[124],"resistant":[126],"also":[133],"suggest":[134],"several":[135],"heuristics":[136],"methodology":[139],"suitable":[140],"large":[146],"scale":[147],"whose":[151],"underlying":[152],"patterns":[153],"recover":[157],"digitization":[159],"reasons.":[160],"Extensive":[161],"experiments":[162],"show":[163],"benefits":[165],"approach,":[168],"evaluated":[169],"dual":[172],"objective:":[173],"accuracy":[175],"estimation":[178],"distributions,":[186],"effectiveness":[189],"approach":[192],"analysis.":[196]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}