{"id":"https://openalex.org/W4230011955","doi":"https://doi.org/10.1109/trustcom.2015.588","title":"Predicting Object Trajectories from High-Speed Streaming Data","display_name":"Predicting Object Trajectories from High-Speed Streaming Data","publication_year":2015,"publication_date":"2015-08-01","ids":{"openalex":"https://openalex.org/W4230011955","doi":"https://doi.org/10.1109/trustcom.2015.588"},"language":"en","primary_location":{"id":"doi:10.1109/trustcom.2015.588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/trustcom.2015.588","pdf_url":null,"source":{"id":"https://openalex.org/S4363605531","display_name":"2015 IEEE Trustcom/BigDataSE/ISPA","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":"2015 IEEE Trustcom/BigDataSE/ISPA","raw_type":"proceedings-article"},"type":"conference-paper","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/A5024735794","display_name":"Nikolaos Zorbas","orcid":null},"institutions":[{"id":"https://openalex.org/I32762134","display_name":"Harokopio University of Athens","ror":"https://ror.org/02k5gp281","country_code":"GR","type":"education","lineage":["https://openalex.org/I32762134"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Nikolaos Zorbas","raw_affiliation_strings":["Dept. of Informatics and Telematics, Harokopio University of Athens, Tavros, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Informatics and Telematics, Harokopio University of Athens, Tavros, Greece","institution_ids":["https://openalex.org/I32762134"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076536574","display_name":"Dimitris Zissis","orcid":"https://orcid.org/0000-0003-2870-2656"},"institutions":[{"id":"https://openalex.org/I32762134","display_name":"Harokopio University of Athens","ror":"https://ror.org/02k5gp281","country_code":"GR","type":"education","lineage":["https://openalex.org/I32762134"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Dimitrios Zissis","raw_affiliation_strings":["Dept. of Informatics and Telematics, Harokopio University of Athens, Tavros, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Informatics and Telematics, Harokopio University of Athens, Tavros, Greece","institution_ids":["https://openalex.org/I32762134"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021420035","display_name":"Konstantinos Tserpes","orcid":"https://orcid.org/0000-0001-5183-1443"},"institutions":[{"id":"https://openalex.org/I32762134","display_name":"Harokopio University of Athens","ror":"https://ror.org/02k5gp281","country_code":"GR","type":"education","lineage":["https://openalex.org/I32762134"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Konstantinos Tserpes","raw_affiliation_strings":["Dept. of Informatics and Telematics, Harokopio University of Athens, Tavros, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Informatics and Telematics, Harokopio University of Athens, Tavros, Greece","institution_ids":["https://openalex.org/I32762134"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023717678","display_name":"Dimosthenis Anagnostopoulos","orcid":"https://orcid.org/0000-0003-0747-4252"},"institutions":[{"id":"https://openalex.org/I98805295","display_name":"University of the Aegean","ror":"https://ror.org/03zsp3p94","country_code":"GR","type":"education","lineage":["https://openalex.org/I98805295"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Dimosthenis Anagnostopoulos","raw_affiliation_strings":["Dept. of Product and Systems Design Engineering, University of the Aegean Ermoupolis, Syros, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Product and Systems Design Engineering, University of the Aegean Ermoupolis, Syros, Greece","institution_ids":["https://openalex.org/I98805295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":null,"first_page":"229","last_page":"234"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9968000054359436,"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"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9968000054359436,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9940999746322632,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9925000071525574,"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":0.8403959274291992},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6674999594688416},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.6286514401435852},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.6020129323005676},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5971493721008301},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5099982023239136},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5048044919967651},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.4717833697795868},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4371771812438965},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.4361934959888458},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.41586318612098694},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.4139018654823303},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.4102192521095276},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.37552082538604736}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8403959274291992},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6674999594688416},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.6286514401435852},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.6020129323005676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5971493721008301},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5099982023239136},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5048044919967651},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4717833697795868},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4371771812438965},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.4361934959888458},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.41586318612098694},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.4139018654823303},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.4102192521095276},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.37552082538604736},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"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/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/trustcom.2015.588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/trustcom.2015.588","pdf_url":null,"source":{"id":"https://openalex.org/S4363605531","display_name":"2015 IEEE Trustcom/BigDataSE/ISPA","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":"2015 IEEE Trustcom/BigDataSE/ISPA","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1537430301","https://openalex.org/W1981587527","https://openalex.org/W1985157189","https://openalex.org/W1997453817","https://openalex.org/W1999954155","https://openalex.org/W2022603014","https://openalex.org/W2082329414","https://openalex.org/W2084335476","https://openalex.org/W2090677252","https://openalex.org/W2095822580","https://openalex.org/W2102481563","https://openalex.org/W2130494750","https://openalex.org/W2132837277","https://openalex.org/W2135514714","https://openalex.org/W2140190241","https://openalex.org/W2166901389","https://openalex.org/W2167800608","https://openalex.org/W2294195441","https://openalex.org/W2544987956","https://openalex.org/W3003253354","https://openalex.org/W6679329542","https://openalex.org/W6680192438"],"related_works":["https://openalex.org/W2570647323","https://openalex.org/W2206805568","https://openalex.org/W2076942471","https://openalex.org/W2995529047","https://openalex.org/W2863268765","https://openalex.org/W3027285423","https://openalex.org/W2896245927","https://openalex.org/W4205879366","https://openalex.org/W1961101704","https://openalex.org/W4254129905"],"abstract_inverted_index":{"Huge":[0],"amounts":[1],"of":[2,79,91,102,106,123,154,163,175,197,200,212],"loosely":[3],"structured":[4],"and":[5,22,41,50,64,95,127,157,180,204],"high":[6],"velocity":[7],"data":[8,51,69,104],"are":[9,27],"now":[10],"being":[11],"generated":[12],"by":[13],"ubiquitous":[14],"mobile":[15],"sensing":[16],"devices,":[17],"aerial":[18],"sensory":[19],"systems,":[20],"cameras":[21],"radiofrequency":[23],"identification":[24],"readers,":[25],"which":[26],"generating":[28],"key":[29],"knowledge":[30],"into":[31,183],"social":[32],"media":[33],"behaviors,":[34],"intelligent":[35],"transport":[36],"patterns,":[37],"military":[38],"operational":[39],"environments":[40],"space":[42,94],"monitoring,":[43],"safety":[44],"systems":[45],"etc.":[46],"Machine":[47],"learning":[48,82,125,176],"models":[49,83,203],"mining":[52],"techniques":[53],"can":[54],"be":[55],"employed":[56],"to":[57,73,88,113,130,138,142,167,192,208],"produce":[58],"actionable":[59],"intelligence,":[60],"based":[61],"on":[62],"predictive":[63],"prescriptive":[65],"analytics.":[66],"However,":[67],"more":[68],"is":[70,111,166,191],"not":[71],"leading":[72],"better":[74],"predictions":[75,182],"as":[76],"the":[77,80,89,92,100,114,169,194,209],"accuracy":[78,196,211],"implicated":[81],"hugely":[84],"varies":[85],"in":[86,99,135,187],"accordance":[87],"complexity":[90],"given":[93],"related":[96],"data.":[97],"Especially":[98],"case":[101],"open-ended":[103],"streams":[105],"massive":[107],"scale,":[108],"their":[109,140,150],"efficiency":[110],"put":[112],"challenge.":[115],"In":[116],"this":[117,164,207],"work,":[118],"we":[119],"employ":[120],"a":[121,144,177,184,198],"variety":[122],"machine":[124],"methods":[126],"apply":[128],"them":[129],"geospatial":[131],"time-series":[132],"surveillance":[133],"data,":[134],"an":[136],"attempt":[137],"determine":[139,168],"capacity":[141],"learn":[143],"vessels":[145,178],"behavioral":[146],"pattern.":[147],"We":[148],"evaluate":[149],"effectiveness":[151],"against":[152],"metrics":[153],"accuracy,":[155],"time":[156],"resource":[158],"usage.":[159],"The":[160],"main":[161],"concept":[162],"study":[165],"most":[170],"appropriate":[171],"machine-learning":[172],"model":[173],"capable":[174],"behavior":[179],"performing":[181],"future":[185],"point":[186],"time.":[188],"Our":[189],"aim":[190],"document":[193],"prediction":[195,210],"set":[199],"traditional":[201],"forecasting":[202],"then":[205],"compare":[206],"streaming":[213],"algorithms.":[214]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
