{"id":"https://openalex.org/W2973549643","doi":"https://doi.org/10.1109/ic3.2019.8844873","title":"Hadoop based Analysis and Visualization of Diabetes Data through Tableau","display_name":"Hadoop based Analysis and Visualization of Diabetes Data through Tableau","publication_year":2019,"publication_date":"2019-08-01","ids":{"openalex":"https://openalex.org/W2973549643","doi":"https://doi.org/10.1109/ic3.2019.8844873","mag":"2973549643"},"language":"en","primary_location":{"id":"doi:10.1109/ic3.2019.8844873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic3.2019.8844873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Twelfth International Conference on Contemporary Computing (IC3)","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/A5067876623","display_name":"Priti Bhardwaj","orcid":"https://orcid.org/0000-0002-0345-8178"},"institutions":[{"id":"https://openalex.org/I4210143260","display_name":"Indira Gandhi Delhi Technical University for Women","ror":"https://ror.org/057c5p638","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210143260"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Priti Bhardwaj","raw_affiliation_strings":["Indira Gandhi Delhi Technical University for Women, C-DAC, Delhi, Noida, India"],"affiliations":[{"raw_affiliation_string":"Indira Gandhi Delhi Technical University for Women, C-DAC, Delhi, Noida, India","institution_ids":["https://openalex.org/I4210143260"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054937724","display_name":"Niyati Baliyan","orcid":"https://orcid.org/0000-0002-2019-5381"},"institutions":[{"id":"https://openalex.org/I4210143260","display_name":"Indira Gandhi Delhi Technical University for Women","ror":"https://ror.org/057c5p638","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210143260"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Niyati Baliyan","raw_affiliation_strings":["Indira Gandhi Delhi Technical University for Women, Delhi, India"],"affiliations":[{"raw_affiliation_string":"Indira Gandhi Delhi Technical University for Women, Delhi, India","institution_ids":["https://openalex.org/I4210143260"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5067876623"],"corresponding_institution_ids":["https://openalex.org/I4210143260"],"apc_list":null,"apc_paid":null,"fwci":0.671,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.79944607,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9969000220298767,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.986299991607666,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7816779613494873},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.731586217880249},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.6526557207107544},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5520433187484741},{"id":"https://openalex.org/keywords/data-visualization","display_name":"Data visualization","score":0.5321017503738403},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.513554573059082},{"id":"https://openalex.org/keywords/map-reduce","display_name":"Map reduce","score":0.4817911982536316},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.46404096484184265},{"id":"https://openalex.org/keywords/commodity","display_name":"Commodity","score":0.45760297775268555},{"id":"https://openalex.org/keywords/data-processing","display_name":"Data processing","score":0.42581623792648315},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.28339508175849915}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7816779613494873},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.731586217880249},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.6526557207107544},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5520433187484741},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.5321017503738403},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.513554573059082},{"id":"https://openalex.org/C3019257732","wikidata":"https://www.wikidata.org/wiki/Q567759","display_name":"Map reduce","level":3,"score":0.4817911982536316},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.46404096484184265},{"id":"https://openalex.org/C2779439359","wikidata":"https://www.wikidata.org/wiki/Q317088","display_name":"Commodity","level":2,"score":0.45760297775268555},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.42581623792648315},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.28339508175849915},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C34447519","wikidata":"https://www.wikidata.org/wiki/Q179522","display_name":"Market economy","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ic3.2019.8844873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic3.2019.8844873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Twelfth International Conference on Contemporary Computing (IC3)","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":24,"referenced_works":["https://openalex.org/W2048287062","https://openalex.org/W2098935637","https://openalex.org/W2110086534","https://openalex.org/W2123487085","https://openalex.org/W2285118877","https://openalex.org/W2510113590","https://openalex.org/W2532521045","https://openalex.org/W2548753368","https://openalex.org/W2555534312","https://openalex.org/W2563832757","https://openalex.org/W2755254450","https://openalex.org/W2785664809","https://openalex.org/W2788990445","https://openalex.org/W2798566193","https://openalex.org/W2810424599","https://openalex.org/W2883394174","https://openalex.org/W2883581694","https://openalex.org/W2886951144","https://openalex.org/W2888120043","https://openalex.org/W2889633626","https://openalex.org/W2899045906","https://openalex.org/W2899235031","https://openalex.org/W2899435877","https://openalex.org/W2966626628"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W4394895745","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2608358066","https://openalex.org/W2763794325","https://openalex.org/W4392563410","https://openalex.org/W2944926722","https://openalex.org/W2942908007"],"abstract_inverted_index":{"Due":[0],"to":[1,21,31,85,125],"rapid":[2],"development":[3],"of":[4,51,57,62,80,113,129],"diverse":[5],"healthcare":[6,16],"practices,":[7],"various":[8],"procedures":[9],"used":[10],"in":[11,36,70,74,131],"healthcare,":[12],"produce":[13],"data.":[14],"This":[15],"data":[17,35,53,64,81],"has":[18,96],"been":[19,97],"scaled":[20,34],"a":[22,28,43,100],"bigger":[23],"size,":[24],"thus,":[25],"there":[26],"is":[27,42,67,83,115],"dire":[29],"need":[30],"analyze":[32],"this":[33,91],"an":[37,87,132],"efficient":[38],"manner.":[39,135],"Apache":[40],"Hadoop":[41,66,93],"framework":[44],"that":[45],"allows":[46,119],"for":[47],"the":[48,127],"distributed":[49],"processing":[50,61],"large":[52],"sets":[54],"across":[55],"clusters":[56],"commodity":[58],"computers.":[59],"Analytical":[60],"big":[63],"with":[65,121],"very":[68],"helpful":[69],"performing":[71],"significant":[72],"actual-point":[73],"time":[75],"analysis":[76,95,130],"on":[77,99],"massive":[78],"amount":[79],"and":[82,109],"capable":[84],"forecast":[86],"emergency":[88],"situation.":[89],"In":[90],"paper":[92],"driven":[94],"performed":[98],"diabetes":[101],"case":[102],"study":[103],"through":[104],"comparison":[105],"among":[106],"Pig,":[107],"Hive,":[108],"Tableau":[110,114],"The":[111],"superiority":[112],"established":[116],"as":[117],"it":[118],"users":[120],"minimal":[122],"statistical":[123],"background":[124],"visualize":[126],"results":[128],"easy":[133],"`on-button-click'":[134]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
