{"id":"https://openalex.org/W4205902150","doi":"https://doi.org/10.1109/bigdata52589.2021.9671556","title":"Discovering Maximal Partial Periodic Patterns in Very Large Temporal Databases","display_name":"Discovering Maximal Partial Periodic Patterns in Very Large Temporal Databases","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205902150","doi":"https://doi.org/10.1109/bigdata52589.2021.9671556"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671556","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671556","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5113864626","display_name":"P. Likitha","orcid":null},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"P. Likitha","raw_affiliation_strings":["The University of Aizu, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Aizu, Japan","institution_ids":["https://openalex.org/I141591182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084771214","display_name":"Pamalla Veena","orcid":"https://orcid.org/0000-0002-3611-0143"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"P. Veena","raw_affiliation_strings":["SBRT College Ananthapur, India"],"affiliations":[{"raw_affiliation_string":"SBRT College Ananthapur, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064495970","display_name":"R. Uday Kiran","orcid":"https://orcid.org/0000-0002-5417-0289"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"R. Uday Kiran","raw_affiliation_strings":["The University of Aizu, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Aizu, Japan","institution_ids":["https://openalex.org/I141591182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062253221","display_name":"Yukata Watanobe","orcid":null},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yukata Watanobe","raw_affiliation_strings":["The University of Aizu, Fukushima, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Aizu, Fukushima, Japan","institution_ids":["https://openalex.org/I141591182"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048072689","display_name":"Koji Zettsu","orcid":"https://orcid.org/0000-0003-4062-2376"},"institutions":[{"id":"https://openalex.org/I4210151344","display_name":"National Institute on Consumer Education","ror":"https://ror.org/03y6p7b93","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210151344"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koji Zettsu","raw_affiliation_strings":["NICT, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"NICT, Tokyo, Japan","institution_ids":["https://openalex.org/I4210151344"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5113864626"],"corresponding_institution_ids":["https://openalex.org/I141591182"],"apc_list":null,"apc_paid":null,"fwci":0.8509,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.72958703,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1460","last_page":"1469"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9991999864578247,"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":0.9991999864578247,"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/T11106","display_name":"Data Management and Algorithms","score":0.9991999864578247,"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"}},{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.9972000122070312,"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":0.7010865211486816},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.5846850872039795},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.5059520602226257},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3631588816642761}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7010865211486816},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.5846850872039795},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.5059520602226257},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3631588816642761}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671556","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671556","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","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":16,"referenced_works":["https://openalex.org/W128175867","https://openalex.org/W1585646276","https://openalex.org/W2138660495","https://openalex.org/W2403388512","https://openalex.org/W2605744361","https://openalex.org/W2607208202","https://openalex.org/W2624114119","https://openalex.org/W2928881543","https://openalex.org/W2955497185","https://openalex.org/W2961234535","https://openalex.org/W2998574808","https://openalex.org/W3082637824","https://openalex.org/W3088781576","https://openalex.org/W4241634004","https://openalex.org/W6635226022","https://openalex.org/W6713815392"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W1596616643"],"abstract_inverted_index":{"Partial":[0,103],"periodic":[1,82],"pattern":[2,83],"mining":[3,10,64],"is":[4,127],"an":[5],"important":[6],"model":[7,78,119,138],"in":[8,87],"data":[9],"with":[11,139],"many":[12,35,121],"real-world":[13],"applications.":[14],"However,":[15],"this":[16,71],"model\u2019s":[17],"successful":[18],"industrial":[19],"application":[20],"was":[21],"hindered":[22],"by":[23,74],"the":[24,46,49,55,59,117,125,134],"problem":[25,50,73],"of":[26,29,38,51,62,79,136],"combinatorial":[27,52],"explosion":[28,53],"patterns,":[30,36,123],"which":[31,39],"involves":[32],"generating":[33],"too":[34],"most":[37],"might":[40],"be":[41],"redundant":[42,122],"or":[43],"uninteresting":[44],"to":[45,69,107],"user.":[47],"Furthermore,":[48],"increases":[54],"memory,":[56],"runtime,":[57],"and":[58,97,124,129],"energy":[60],"requirements":[61],"a":[63,76,88,93,98,140],"algorithm.":[65],"This":[66],"paper":[67],"aims":[68],"tackle":[70],"challenging":[72],"proposing":[75],"novel":[77],"maximal":[80],"partial":[81],"that":[84,116],"may":[85],"exist":[86],"database.":[89],"We":[90],"also":[91],"present":[92],"new":[94],"tree":[95],"structure":[96],"pattern-growth":[99],"algorithm,":[100],"called":[101],"Maximal":[102],"Periodic":[104],"Pattern-growth":[105],"(max3P-growth),":[106],"find":[108],"all":[109],"desired":[110],"patterns":[111],"effectively.":[112],"Experimental":[113],"results":[114],"demonstrate":[115],"proposed":[118],"prunes":[120],"max3P-growth":[126],"efficient":[128],"scalable.":[130],"Finally,":[131],"we":[132],"show":[133],"usefulness":[135],"our":[137],"case":[141],"study":[142],"on":[143],"traffic":[144],"congestion":[145],"analytics.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}