{"id":"https://openalex.org/W7135028989","doi":"https://doi.org/10.1109/clei67442.2025.11420611","title":"Recommendation For Tabular Data Visualization Utilizing Machine Learning Applied To The Sales Domain","display_name":"Recommendation For Tabular Data Visualization Utilizing Machine Learning Applied To The Sales Domain","publication_year":2025,"publication_date":"2025-10-27","ids":{"openalex":"https://openalex.org/W7135028989","doi":"https://doi.org/10.1109/clei67442.2025.11420611"},"language":null,"primary_location":{"id":"doi:10.1109/clei67442.2025.11420611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/clei67442.2025.11420611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 LI Latin American Computer Conference (CLEI)","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/A5128901619","display_name":"Jenis Arnold Alvarado Gonzales","orcid":null},"institutions":[{"id":"https://openalex.org/I192703252","display_name":"Universidad de Lima","ror":"https://ror.org/01751w114","country_code":"PE","type":"education","lineage":["https://openalex.org/I192703252"]}],"countries":["PE"],"is_corresponding":false,"raw_author_name":"Jenis Arnold Alvarado Gonzales","raw_affiliation_strings":["Universidad de Lima,Carrera de Ingenier&#x00ED;a de Sistemas,Lima,Per&#x00FA;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad de Lima,Carrera de Ingenier&#x00ED;a de Sistemas,Lima,Per&#x00FA;","institution_ids":["https://openalex.org/I192703252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113393481","display_name":"Edwin Jonathan Escobedo C\u00e1rdenas","orcid":null},"institutions":[{"id":"https://openalex.org/I192703252","display_name":"Universidad de Lima","ror":"https://ror.org/01751w114","country_code":"PE","type":"education","lineage":["https://openalex.org/I192703252"]}],"countries":["PE"],"is_corresponding":false,"raw_author_name":"Edwin Jonathan Escobedo C\u00e1rdenas","raw_affiliation_strings":["Universidad de Lima,Carrera de Ingenier&#x00ED;a de Sistemas,Lima,Per&#x00FA;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad de Lima,Carrera de Ingenier&#x00ED;a de Sistemas,Lima,Per&#x00FA;","institution_ids":["https://openalex.org/I192703252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I192703252"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.66228005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.8238999843597412,"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/T10799","display_name":"Data Visualization and Analytics","score":0.8238999843597412,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.05620000138878822,"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/T11891","display_name":"Big Data and Business Intelligence","score":0.01600000075995922,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.887499988079071},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5559999942779541},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5285000205039978},{"id":"https://openalex.org/keywords/data-visualization","display_name":"Data visualization","score":0.5248000025749207},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.501800000667572},{"id":"https://openalex.org/keywords/column","display_name":"Column (typography)","score":0.48260000348091125},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4352000057697296},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.41670000553131104}],"concepts":[{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.887499988079071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8349999785423279},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5559999942779541},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5303000211715698},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5285000205039978},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.5248000025749207},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5102999806404114},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.501800000667572},{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.48260000348091125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43560001254081726},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4352000057697296},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.41670000553131104},{"id":"https://openalex.org/C185578843","wikidata":"https://www.wikidata.org/wiki/Q10609775","display_name":"Information visualization","level":3,"score":0.3878999948501587},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3693999946117401},{"id":"https://openalex.org/C14669888","wikidata":"https://www.wikidata.org/wiki/Q4014850","display_name":"Creative visualization","level":3,"score":0.362199991941452},{"id":"https://openalex.org/C59732488","wikidata":"https://www.wikidata.org/wiki/Q2528440","display_name":"Visual analytics","level":3,"score":0.3522000014781952},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.29350000619888306},{"id":"https://openalex.org/C195487862","wikidata":"https://www.wikidata.org/wiki/Q850210","display_name":"Revenue","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.26100000739097595}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/clei67442.2025.11420611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/clei67442.2025.11420611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 LI Latin American Computer Conference (CLEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2595457065","https://openalex.org/W2886887279","https://openalex.org/W2963348221","https://openalex.org/W2963427688","https://openalex.org/W2964101465","https://openalex.org/W3080485811","https://openalex.org/W3088290493","https://openalex.org/W3092234597","https://openalex.org/W3092587559","https://openalex.org/W3143309531","https://openalex.org/W3172214016","https://openalex.org/W3174906424","https://openalex.org/W3177093812","https://openalex.org/W3203626883","https://openalex.org/W3213578841","https://openalex.org/W4220657226","https://openalex.org/W4224308856","https://openalex.org/W4296143854","https://openalex.org/W4306317371","https://openalex.org/W4313188278","https://openalex.org/W4382498938","https://openalex.org/W4386825362","https://openalex.org/W4400444500","https://openalex.org/W4402742693","https://openalex.org/W4404031527"],"related_works":[],"abstract_inverted_index":{"Effective":[0],"data":[1,37,83],"presentation":[2],"and":[3,13,21,84,129,177,187],"interpretation":[4],"are":[5],"essential":[6],"for":[7,35,49,150],"optimizing":[8],"business":[9],"operations,":[10],"reducing":[11],"costs,":[12],"enabling":[14],"informed":[15],"decision-making,":[16],"ultimately":[17],"fueling":[18],"revenue":[19],"growth":[20],"establishing":[22],"a":[23,58,75,95,138],"competitive":[24],"advantage.":[25],"This":[26],"research":[27],"addresses":[28],"the":[29,70,81,85,103,113,133,151,175,183],"challenge":[30],"of":[31,124,142,179],"recommending":[32],"appropriate":[33],"visualizations":[34],"tabular":[36,82],"based":[38,61],"on":[39,62],"user":[40],"intentions":[41],"expressed":[42],"in":[43,147,165],"natural":[44,87],"language":[45,88,97],"using":[46],"Machine":[47],"Learning":[48],"Visualization":[50],"(ML4VIS).":[51],"To":[52],"tackle":[53],"this":[54],"challenge,":[55],"we":[56],"developed":[57],"comprehensive":[59],"solution":[60],"two":[63,170],"complementary":[64],"approaches.":[65],"The":[66,91,117],"first":[67],"approach":[68,93,161],"utilizes":[69],"BiDA4Sales":[71],"model,":[72],"which":[73,107],"recommends":[74],"suitable":[76],"visualization":[77,104,115,154,166,180],"type":[78,167],"by":[79,105],"analyzing":[80],"user\u2019s":[86],"query":[89],"(intention).":[90],"second":[92],"employs":[94],"large":[96],"model":[98,120,136],"(LLM)":[99],"that":[100],"further":[101],"refines":[102],"suggesting":[106],"columns":[108],"should":[109],"be":[110],"included,":[111],"considering":[112],"recommended":[114,153],"type.":[116,155],"visualization-type":[118],"recommendation":[119,135,140],"achieved":[121,137],"an":[122],"F1-score":[123],"90.24%,":[125],"demonstrating":[126],"high":[127],"accuracy":[128,178],"reliability.":[130],"In":[131],"contrast,":[132],"column":[134,148],"correct":[139],"percentage":[141],"79.08%,":[143],"indicating":[144],"good":[145],"precision":[146],"selection":[149],"previously":[152],"Compared":[156],"to":[157],"general-purpose":[158],"LLMs,":[159],"our":[160],"demonstrated":[162],"superior":[163],"performance":[164],"recommendation.":[168],"These":[169],"approaches,":[171],"when":[172],"combined,":[173],"enhance":[174],"flexibility":[176],"recommendations,":[181],"making":[182],"process":[184],"more":[185],"efficient":[186],"user-friendly.":[188]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-03-13T00:00:00"}
