{"id":"https://openalex.org/W2047318424","doi":"https://doi.org/10.1109/vast.2014.7042568","title":"Making sense of daily life data: From commonalities to anomalies: VAST 2014 Mini Challenge #2","display_name":"Making sense of daily life data: From commonalities to anomalies: VAST 2014 Mini Challenge #2","publication_year":2014,"publication_date":"2014-10-01","ids":{"openalex":"https://openalex.org/W2047318424","doi":"https://doi.org/10.1109/vast.2014.7042568","mag":"2047318424"},"language":"en","primary_location":{"id":"doi:10.1109/vast.2014.7042568","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vast.2014.7042568","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Conference on Visual Analytics Science and Technology (VAST)","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/A5100386464","display_name":"Ji Wang","orcid":"https://orcid.org/0000-0003-3513-5573"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ji Wang","raw_affiliation_strings":["Virginia Tech"],"affiliations":[{"raw_affiliation_string":"Virginia Tech","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089405476","display_name":"Peng Mi","orcid":"https://orcid.org/0000-0003-0123-4311"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Mi","raw_affiliation_strings":["Virginia Tech"],"affiliations":[{"raw_affiliation_string":"Virginia Tech","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037675411","display_name":"Chris North","orcid":"https://orcid.org/0000-0002-8786-7103"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chris North","raw_affiliation_strings":["Virginia Tech"],"affiliations":[{"raw_affiliation_string":"Virginia Tech","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100386464"],"corresponding_institution_ids":["https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11336507,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"365","last_page":"366"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10799","display_name":"Data Visualization and Analytics","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T14280","display_name":"Big Data Technologies and Applications","score":0.9417999982833862,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6818318963050842},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.6054074764251709},{"id":"https://openalex.org/keywords/data-visualization","display_name":"Data visualization","score":0.5653845071792603},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.5481324195861816},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4673942029476166},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.445879191160202},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.418771892786026},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.41747117042541504},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37462759017944336},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.10300511121749878},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.09762150049209595}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6818318963050842},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.6054074764251709},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.5653845071792603},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5481324195861816},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4673942029476166},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.445879191160202},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.418771892786026},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.41747117042541504},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37462759017944336},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.10300511121749878},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.09762150049209595},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vast.2014.7042568","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vast.2014.7042568","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Conference on Visual Analytics Science and Technology (VAST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"We":[0],"report":[1],"the":[2,7,24,32,38,54,58],"approach":[3],"and":[4,14,42,56],"results":[5],"on":[6,23],"VAST":[8],"2014":[9],"Mini-Challenge":[10],"2:":[11],"Analysis":[12],"Movement":[13],"Tracking":[15],"data":[16,44,63],"of":[17,37],"GAStech":[18],"Employees'":[19],"daily":[20],"lives.":[21],"Based":[22],"commercial":[25],"interactive":[26],"visualization":[27],"software":[28],"Tableau[l],":[29],"we":[30,50],"follow":[31],"sense-making":[33],"loop":[34],"for":[35],"analysis":[36],"massive":[39],"multi-dimensional,":[40],"multi-source":[41],"time-varying":[43],"sets.":[45,64],"The":[46],"findings":[47],"show":[48],"that":[49],"can":[51],"effectively":[52],"identify":[53],"patterns":[55],"discovery":[57],"anomaly":[59],"from":[60],"these":[61],"complex":[62]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}