{"id":"https://openalex.org/W3013683801","doi":"https://doi.org/10.1186/s40537-020-00292-y","title":"Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter","display_name":"Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter","publication_year":2020,"publication_date":"2020-02-28","ids":{"openalex":"https://openalex.org/W3013683801","doi":"https://doi.org/10.1186/s40537-020-00292-y","mag":"3013683801"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-020-00292-y","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-020-00292-y","pdf_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-020-00292-y","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-020-00292-y","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091772050","display_name":"Sandhya Narayanan","orcid":null},"institutions":[{"id":"https://openalex.org/I20497027","display_name":"Cochin University of Science and Technology","ror":"https://ror.org/00a4kqq17","country_code":"IN","type":"education","lineage":["https://openalex.org/I20497027"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sandhya Narayanan","raw_affiliation_strings":["Information Technology, School of Engineering, Cochin University of Science & Technology, Kochi, 682022, India","Information Technology, School of Engineering, Cochin University of Science & Technology, Kochi, India"],"affiliations":[{"raw_affiliation_string":"Information Technology, School of Engineering, Cochin University of Science & Technology, Kochi, 682022, India","institution_ids":["https://openalex.org/I20497027"]},{"raw_affiliation_string":"Information Technology, School of Engineering, Cochin University of Science & Technology, Kochi, India","institution_ids":["https://openalex.org/I20497027"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032700845","display_name":"Philip Samuel","orcid":"https://orcid.org/0000-0001-6476-1544"},"institutions":[{"id":"https://openalex.org/I20497027","display_name":"Cochin University of Science and Technology","ror":"https://ror.org/00a4kqq17","country_code":"IN","type":"education","lineage":["https://openalex.org/I20497027"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Philip Samuel","raw_affiliation_strings":["Department of Computer Science, Cochin University of Science & Technology, Kochi, 682022, India","Department of Computer Science, Cochin University of Science & Technology, Kochi, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Cochin University of Science & Technology, Kochi, 682022, India","institution_ids":["https://openalex.org/I20497027"]},{"raw_affiliation_string":"Department of Computer Science, Cochin University of Science & Technology, Kochi, India","institution_ids":["https://openalex.org/I20497027"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048577364","display_name":"Mariamma Chacko","orcid":null},"institutions":[{"id":"https://openalex.org/I20497027","display_name":"Cochin University of Science and Technology","ror":"https://ror.org/00a4kqq17","country_code":"IN","type":"education","lineage":["https://openalex.org/I20497027"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mariamma Chacko","raw_affiliation_strings":["Department of Ship Technology, Cochin University of Science & Technology, Kochi, 682022, India","Department of Ship Technology, Cochin University of Science & Technology, Kochi, India"],"affiliations":[{"raw_affiliation_string":"Department of Ship Technology, Cochin University of Science & Technology, Kochi, 682022, India","institution_ids":["https://openalex.org/I20497027"]},{"raw_affiliation_string":"Department of Ship Technology, Cochin University of Science & Technology, Kochi, India","institution_ids":["https://openalex.org/I20497027"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5091772050"],"corresponding_institution_ids":["https://openalex.org/I20497027"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":0.9254,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.77516817,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"7","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9951000213623047,"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"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9951000213623047,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9933000206947327,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9930999875068665,"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/computer-science","display_name":"Computer science","score":0.8356168270111084},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6031844615936279},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5854925513267517},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.5842862129211426},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5540157556533813},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.542641282081604},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4844782054424286},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4502984881401062},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.4480913281440735},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.44604459404945374},{"id":"https://openalex.org/keywords/fault-tolerance","display_name":"Fault tolerance","score":0.4436171352863312},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.43960896134376526},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3894859850406647},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.21081754565238953},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1874222457408905},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.12541618943214417}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8356168270111084},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6031844615936279},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5854925513267517},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.5842862129211426},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5540157556533813},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.542641282081604},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4844782054424286},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4502984881401062},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.4480913281440735},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.44604459404945374},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.4436171352863312},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.43960896134376526},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3894859850406647},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.21081754565238953},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1874222457408905},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.12541618943214417},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-020-00292-y","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-020-00292-y","pdf_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-020-00292-y","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3b7a7d59b92641ce9b044d3e25f48e97","is_oa":true,"landing_page_url":"https://doaj.org/article/3b7a7d59b92641ce9b044d3e25f48e97","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 7, Iss 1, Pp 1-15 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-020-00292-y","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-020-00292-y","pdf_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-020-00292-y","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3013683801.pdf","grobid_xml":"https://content.openalex.org/works/W3013683801.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W21207210","https://openalex.org/W97702925","https://openalex.org/W1511814458","https://openalex.org/W1851422430","https://openalex.org/W1902548479","https://openalex.org/W1976526581","https://openalex.org/W1990363367","https://openalex.org/W1999580159","https://openalex.org/W2002725950","https://openalex.org/W2008886893","https://openalex.org/W2016266039","https://openalex.org/W2020488968","https://openalex.org/W2030397439","https://openalex.org/W2054141820","https://openalex.org/W2070308033","https://openalex.org/W2071283884","https://openalex.org/W2078082072","https://openalex.org/W2081375810","https://openalex.org/W2085040216","https://openalex.org/W2099866409","https://openalex.org/W2099934438","https://openalex.org/W2110325612","https://openalex.org/W2115023510","https://openalex.org/W2118585731","https://openalex.org/W2131975293","https://openalex.org/W2135303445","https://openalex.org/W2145955806","https://openalex.org/W2151464835","https://openalex.org/W2168069066","https://openalex.org/W2188458595","https://openalex.org/W2269703563","https://openalex.org/W2272031392","https://openalex.org/W2282821441","https://openalex.org/W2357827496","https://openalex.org/W2471860648","https://openalex.org/W2513210372","https://openalex.org/W2517030840","https://openalex.org/W2520187617","https://openalex.org/W2560761731","https://openalex.org/W2794778778","https://openalex.org/W2802370270","https://openalex.org/W2949854251","https://openalex.org/W3098649723","https://openalex.org/W4230674625","https://openalex.org/W4285719527","https://openalex.org/W4297971002"],"related_works":["https://openalex.org/W2064720525","https://openalex.org/W2102525122","https://openalex.org/W2153096481","https://openalex.org/W2148616436","https://openalex.org/W4306316843","https://openalex.org/W2036953450","https://openalex.org/W2130594209","https://openalex.org/W2527822502","https://openalex.org/W4245282135","https://openalex.org/W2170004886"],"abstract_inverted_index":{"Abstract":[0],"Launching":[1],"new":[2,159],"products":[3],"in":[4,16,65,84,125],"the":[5,14,20,34,68,71,85,109,115,119,135,151,155],"consumer":[6],"electronics":[7],"market":[8,86],"is":[9,45,58,77,99,140],"challenging.":[10],"Developing":[11],"and":[12,51,93,106,137,148],"marketing":[13],"same":[15],"limited":[17],"time":[18],"affect":[19],"sustainability":[21],"of":[22,36,70,81,102,118,134,144,157],"such":[23,89],"companies.":[24],"This":[25],"research":[26],"work":[27],"introduces":[28],"a":[29,37,126,131],"model":[30,123],"that":[31],"can":[32],"predict":[33],"success":[35],"product.":[38,160],"A":[39],"Feature":[40],"Information":[41],"Gain":[42],"(FIG)":[43],"measure":[44],"used":[46,59,78],"for":[47,79],"significant":[48],"feature":[49,146],"identification":[50],"Distributed":[52],"Memory-based":[53],"Resilient":[54],"Dataset":[55],"Filter":[56],"(DMRDF)":[57],"to":[60,129,153],"eliminate":[61],"duplicate":[62],"reviews,":[63],"which":[64],"turn":[66],"improves":[67],"reliability":[69],"product":[72,82],"reviews.":[73],"The":[74,121,142],"pre-processed":[75],"dataset":[76,110,136],"prediction":[80,116,149],"pre-launch":[83],"using":[87],"classifiers":[88],"as":[90],"Logistic":[91],"regression":[92],"Support":[94],"vector":[95],"machine.":[96],"DMRDF":[97],"method":[98],"fault-tolerant":[100],"because":[101],"its":[103],"resilience":[104],"property":[105],"also":[107],"reduces":[108],"redundancy;":[111],"hence,":[112],"it":[113,139],"increases":[114],"accuracy":[117],"model.":[120],"proposed":[122],"works":[124],"distributed":[127],"environment":[128],"handle":[130],"massive":[132],"volume":[133],"therefore,":[138],"scalable.":[141],"output":[143],"this":[145],"modelling":[147],"allows":[150],"manufacturer":[152],"optimize":[154],"design":[156],"his":[158]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-17T09:09:15.849793","created_date":"2025-10-10T00:00:00"}
