{"id":"https://openalex.org/W2782968837","doi":"https://doi.org/10.1109/bigdata.2017.8258011","title":"Distributed decision tree v.2.0","display_name":"Distributed decision tree v.2.0","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2782968837","doi":"https://doi.org/10.1109/bigdata.2017.8258011","mag":"2782968837"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258011","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","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/A5012571311","display_name":"Ankit Desai","orcid":"https://orcid.org/0000-0003-3109-9608"},"institutions":[{"id":"https://openalex.org/I52527943","display_name":"Ahmedabad University","ror":"https://ror.org/02swff503","country_code":"IN","type":"education","lineage":["https://openalex.org/I52527943"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Ankit Desai","raw_affiliation_strings":["School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, India"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, India","institution_ids":["https://openalex.org/I52527943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054359032","display_name":"Sanjay Chaudhary","orcid":"https://orcid.org/0000-0001-9127-360X"},"institutions":[{"id":"https://openalex.org/I52527943","display_name":"Ahmedabad University","ror":"https://ror.org/02swff503","country_code":"IN","type":"education","lineage":["https://openalex.org/I52527943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sanjay Chaudhary","raw_affiliation_strings":["School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, India"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, India","institution_ids":["https://openalex.org/I52527943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5012571311"],"corresponding_institution_ids":["https://openalex.org/I52527943"],"apc_list":null,"apc_paid":null,"fwci":0.9673,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.84085839,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"929","last_page":"934"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9986000061035156,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9986000061035156,"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":0.7970080375671387},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.7144508361816406},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.641939640045166},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.631349503993988},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.6196573972702026},{"id":"https://openalex.org/keywords/incremental-decision-tree","display_name":"Incremental decision tree","score":0.5951961874961853},{"id":"https://openalex.org/keywords/tree-traversal","display_name":"Tree traversal","score":0.5489001870155334},{"id":"https://openalex.org/keywords/parallelism","display_name":"Parallelism (grammar)","score":0.5444586873054504},{"id":"https://openalex.org/keywords/decision-tree-learning","display_name":"Decision tree learning","score":0.523400604724884},{"id":"https://openalex.org/keywords/spark","display_name":"SPARK (programming language)","score":0.5103101134300232},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43517330288887024},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4261758327484131},{"id":"https://openalex.org/keywords/interval-tree","display_name":"Interval tree","score":0.41435837745666504},{"id":"https://openalex.org/keywords/binary-tree","display_name":"Binary tree","score":0.3935580253601074},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3675697445869446},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35616517066955566},{"id":"https://openalex.org/keywords/tree-structure","display_name":"Tree structure","score":0.2930513024330139},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.2735059857368469},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15971171855926514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7970080375671387},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.7144508361816406},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.641939640045166},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.631349503993988},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.6196573972702026},{"id":"https://openalex.org/C10229987","wikidata":"https://www.wikidata.org/wiki/Q17083028","display_name":"Incremental decision tree","level":4,"score":0.5951961874961853},{"id":"https://openalex.org/C140745168","wikidata":"https://www.wikidata.org/wiki/Q1210082","display_name":"Tree traversal","level":2,"score":0.5489001870155334},{"id":"https://openalex.org/C2781172179","wikidata":"https://www.wikidata.org/wiki/Q853109","display_name":"Parallelism (grammar)","level":2,"score":0.5444586873054504},{"id":"https://openalex.org/C5481197","wikidata":"https://www.wikidata.org/wiki/Q16766476","display_name":"Decision tree learning","level":3,"score":0.523400604724884},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.5103101134300232},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43517330288887024},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4261758327484131},{"id":"https://openalex.org/C84927040","wikidata":"https://www.wikidata.org/wiki/Q6057306","display_name":"Interval tree","level":4,"score":0.41435837745666504},{"id":"https://openalex.org/C197855036","wikidata":"https://www.wikidata.org/wiki/Q380172","display_name":"Binary tree","level":2,"score":0.3935580253601074},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3675697445869446},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35616517066955566},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.2930513024330139},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.2735059857368469},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15971171855926514},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8258011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258011","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W150939725","https://openalex.org/W1570448133","https://openalex.org/W1598064945","https://openalex.org/W1736726159","https://openalex.org/W1833977909","https://openalex.org/W1955600018","https://openalex.org/W1971774955","https://openalex.org/W1980273653","https://openalex.org/W2008183828","https://openalex.org/W2050547330","https://openalex.org/W2093717447","https://openalex.org/W2118350665","https://openalex.org/W2119565742","https://openalex.org/W2125816831","https://openalex.org/W2128420091","https://openalex.org/W2140190241","https://openalex.org/W2141083048","https://openalex.org/W2149706766","https://openalex.org/W2166685976","https://openalex.org/W2173213060","https://openalex.org/W2548505086","https://openalex.org/W2783521339","https://openalex.org/W2962739538","https://openalex.org/W2995564009","https://openalex.org/W3004732066","https://openalex.org/W4236137412","https://openalex.org/W4239670432","https://openalex.org/W4367175593","https://openalex.org/W6634094483","https://openalex.org/W6677921607","https://openalex.org/W6747486472","https://openalex.org/W6851995537"],"related_works":["https://openalex.org/W3022865202","https://openalex.org/W2355273608","https://openalex.org/W2372574250","https://openalex.org/W2371680642","https://openalex.org/W1510719821","https://openalex.org/W2382364329","https://openalex.org/W2365019162","https://openalex.org/W2795921908","https://openalex.org/W2351792469","https://openalex.org/W2005948580"],"abstract_inverted_index":{"Decision":[0,23,110,124],"Tree":[1,24,111,125],"is":[2,90,126],"a":[3,26,82,92],"state-of-the-art":[4],"classification":[5],"and":[6,39,96,133,142],"prediction":[7,48],"algorithm":[8,112],"in":[9,86,101,119],"machine":[10],"learning":[11,137],"which":[12],"constructs":[13],"tree-structured":[14],"set":[15,145],"of":[16,29,34,36,55,61,70,72,77,84,98,140],"attributes.":[17],"Its":[18],"distributed":[19,130],"implementation,":[20],"i.e.":[21],"Distributed":[22,109,123],"generates":[25],"specified":[27],"number":[28,33,60,71],"trees":[30],"(depending":[31],"upon":[32,59],"partitions":[35],"input":[37],"dataset)":[38],"at":[40],"the":[41,47,52],"end":[42],"collects":[43],"votes":[44],"or":[45,49],"averages":[46],"classification.":[50],"Here,":[51],"overall":[53],"idea":[54],"achieving":[56],"parallelism":[57,116],"depends":[58],"partitions.":[62,73],"Parallelism":[63],"can":[64],"be":[65],"achived":[66],"by":[67],"proper":[68],"tuning":[69],"However,":[74],"this":[75,102],"kind":[76],"setup":[78],"in-turn":[79],"leads":[80],"to":[81,113,144,150],"problem":[83],"compromise":[85],"accuracy,":[87],"because":[88],"there":[89],"always":[91],"tradeoff":[93],"between":[94],"accuracy":[95,143],"size":[97,139],"partition.":[99],"Therefore,":[100],"paper,":[103],"we":[104],"have":[105],"proposed":[106],"an":[107],"improved":[108,122],"achieve":[114],"true":[115],"without":[117],"loss":[118],"accuracy.":[120],"The":[121],"implemented":[127],"using":[128,148],"open-source":[129],"frameworks":[131],"Hadoop":[132],"Spark.":[134],"We":[135],"measure":[136],"time,":[138],"tree":[141],"up":[146],"benchmarking":[147],"medium":[149],"large":[151],"datasets.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}