{"id":"https://openalex.org/W3165800149","doi":"https://doi.org/10.1177/01655515211013708","title":"Efficient indexing and retrieval of patient information from the big data using MapReduce framework and optimisation","display_name":"Efficient indexing and retrieval of patient information from the big data using MapReduce framework and optimisation","publication_year":2021,"publication_date":"2021-05-24","ids":{"openalex":"https://openalex.org/W3165800149","doi":"https://doi.org/10.1177/01655515211013708","mag":"3165800149"},"language":"en","primary_location":{"id":"doi:10.1177/01655515211013708","is_oa":false,"landing_page_url":"https://doi.org/10.1177/01655515211013708","pdf_url":null,"source":{"id":"https://openalex.org/S68913162","display_name":"Journal of Information Science","issn_l":"0165-5515","issn":["0165-5515","1741-6485"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Information Science","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/A5039417046","display_name":"N.R. Gladiss Merlin","orcid":"https://orcid.org/0000-0002-4652-8420"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"N.R. Gladiss Merlin","raw_affiliation_strings":["Jeppiaar Institute of Technology, India"],"raw_orcid":"https://orcid.org/0000-0002-4652-8420","affiliations":[{"raw_affiliation_string":"Jeppiaar Institute of Technology, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025286844","display_name":"Vigilson Prem. M","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vigilson Prem. M","raw_affiliation_strings":["RMD Engineering College, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMD Engineering College, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5039417046"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0209,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.82864636,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"49","issue":"2","first_page":"500","last_page":"518"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.995199978351593,"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":0.995199978351593,"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/T11719","display_name":"Data Quality and Management","score":0.97079998254776,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9663000106811523,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.8336628675460815},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.7634648084640503},{"id":"https://openalex.org/keywords/search-engine-indexing","display_name":"Search engine indexing","score":0.7616950273513794},{"id":"https://openalex.org/keywords/reducer","display_name":"Reducer","score":0.7217782139778137},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5676167011260986},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5528212189674377},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.5389806032180786},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5271463394165039},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5144708156585693},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5093649625778198},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.3457196056842804},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.24149292707443237},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12138941884040833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8336628675460815},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.7634648084640503},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.7616950273513794},{"id":"https://openalex.org/C2776985865","wikidata":"https://www.wikidata.org/wiki/Q26820931","display_name":"Reducer","level":2,"score":0.7217782139778137},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5676167011260986},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5528212189674377},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.5389806032180786},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5271463394165039},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5144708156585693},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5093649625778198},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.3457196056842804},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.24149292707443237},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12138941884040833},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1177/01655515211013708","is_oa":false,"landing_page_url":"https://doi.org/10.1177/01655515211013708","pdf_url":null,"source":{"id":"https://openalex.org/S68913162","display_name":"Journal of Information Science","issn_l":"0165-5515","issn":["0165-5515","1741-6485"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Information Science","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1602415067","https://openalex.org/W1698059316","https://openalex.org/W1851798646","https://openalex.org/W1957343200","https://openalex.org/W1997555950","https://openalex.org/W2042990472","https://openalex.org/W2072534270","https://openalex.org/W2085901068","https://openalex.org/W2101141209","https://openalex.org/W2196984851","https://openalex.org/W2215860650","https://openalex.org/W2232317135","https://openalex.org/W2261525379","https://openalex.org/W2559959930","https://openalex.org/W2563411137","https://openalex.org/W2587469635","https://openalex.org/W2606068938","https://openalex.org/W2618327808","https://openalex.org/W2756410484","https://openalex.org/W2768718286","https://openalex.org/W2783127227","https://openalex.org/W2783207633","https://openalex.org/W2786798553","https://openalex.org/W2903640742","https://openalex.org/W3007169964","https://openalex.org/W4248831244"],"related_works":["https://openalex.org/W2284045667","https://openalex.org/W57923944","https://openalex.org/W2981287881","https://openalex.org/W1598081081","https://openalex.org/W2056226831","https://openalex.org/W1823511492","https://openalex.org/W4386612228","https://openalex.org/W3200484643","https://openalex.org/W4379207348","https://openalex.org/W2024635814"],"abstract_inverted_index":{"Large":[0],"and":[1,27,45,64,98,124,171,183,199,204],"complex":[2],"data":[3,38,52,83,95,100,109,146],"becomes":[4],"a":[5],"valuable":[6],"resource":[7],"in":[8,41],"biomedical":[9,42],"discovery,":[10],"which":[11,175],"is":[12,39,53,84,96,119,141,160,176],"highly":[13],"facilitated":[14],"to":[15,86,103,129],"increase":[16],"the":[17,22,29,33,47,56,62,71,80,87,104,107,116,122,130,134,145,148,152,155,164,172,177,180,184,187,192,208],"scientific":[18],"resources":[19],"for":[20,132],"retrieving":[21,28,46,133,144],"helpful":[23],"information.":[24],"However,":[25],"indexing":[26,63],"patient":[30,48],"information":[31,49,67],"from":[32,50,147],"disparate":[34],"source":[35],"of":[36,66,92,179,195,201],"big":[37,51,82],"challenging":[40],"research.":[43],"Indexing":[44],"performed":[54,69,142],"using":[55,70,207],"MapReduce":[57,77],"framework.":[58,78],"In":[59],"this":[60],"research,":[61],"retrieval":[65],"are":[68,101,110],"proposed":[72,173,188],"Jaya-Sine":[73],"Cosine":[74],"Algorithm":[75],"(Jaya\u2013SCA)-based":[76],"Initially,":[79],"input":[81,117],"forwarded":[85,102],"mapper":[88,94,131,149],"randomly.":[89],"The":[90,138,157],"average":[91],"each":[93,113],"calculated,":[97],"these":[99],"reducer,":[105,123],"where":[106],"representative":[108],"stored.":[111],"For":[112],"user":[114],"query,":[115],"query":[118],"matched":[120,135],"with":[121],"thereby,":[125],"it":[126],"switches":[127],"over":[128],"best":[136],"result.":[137],"bilevel":[139],"matching":[140],"while":[143],"based":[150,162],"on":[151,163],"distance":[153],"between":[154],"query.":[156],"similarity":[158,166,170],"measure":[159,167],"computed":[161],"parametric-enabled":[165],"(PESM),":[168],"cosine":[169],"Jaya\u2013SCA,":[174],"integration":[178],"Jaya":[181],"algorithm":[182,190],"SCA.":[185],"Moreover,":[186],"Jaya\u2013SCA":[189],"attained":[191],"maximum":[193],"value":[194],"F":[196],"-measure,":[197],"recall":[198],"precision":[200],"0.5323,":[202],"0.4400":[203],"0.6867,":[205],"respectively,":[206],"StatLog":[209],"Heart":[210],"Disease":[211],"dataset.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-05T06:06:40.768181","created_date":"2025-10-10T00:00:00"}
