{"id":"https://openalex.org/W4413360897","doi":"https://doi.org/10.1109/icde65448.2025.00199","title":"Towards Online Spatio-Temporal Prediction: A Knowledge Distillation Driven Continual Learning Approach","display_name":"Towards Online Spatio-Temporal Prediction: A Knowledge Distillation Driven Continual Learning Approach","publication_year":2025,"publication_date":"2025-05-19","ids":{"openalex":"https://openalex.org/W4413360897","doi":"https://doi.org/10.1109/icde65448.2025.00199"},"language":"en","primary_location":{"id":"doi:10.1109/icde65448.2025.00199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde65448.2025.00199","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 41st International Conference on Data Engineering (ICDE)","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":null,"display_name":"Tinghui Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tinghui Luo","raw_affiliation_strings":["Zhejiang University,China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University,China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042394655","display_name":"Ziquan Fang","orcid":"https://orcid.org/0009-0009-2034-5501"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziquan Fang","raw_affiliation_strings":["Zhejiang University,China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University,China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kaixuan Duan","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaixuan Duan","raw_affiliation_strings":["Zhejiang University,China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University,China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100605099","display_name":"Chen L\u00fc","orcid":"https://orcid.org/0000-0003-1927-2391"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lu Chen","raw_affiliation_strings":["Zhejiang University,China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University,China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078652556","display_name":"Panpan Feng","orcid":"https://orcid.org/0000-0002-7820-4439"},"institutions":[{"id":"https://openalex.org/I4210115040","display_name":"iQIYI (China)","ror":"https://ror.org/026xvz038","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210115040"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Panpan Feng","raw_affiliation_strings":["iQIYI Inc,China"],"affiliations":[{"raw_affiliation_string":"iQIYI Inc,China","institution_ids":["https://openalex.org/I4210115040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102387343","display_name":"Mingfan Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210115040","display_name":"iQIYI (China)","ror":"https://ror.org/026xvz038","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210115040"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingfan Lu","raw_affiliation_strings":["iQIYI Inc,China"],"affiliations":[{"raw_affiliation_string":"iQIYI Inc,China","institution_ids":["https://openalex.org/I4210115040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":2.7431,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.91128413,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2642","last_page":"2655"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9465000033378601,"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":0.9465000033378601,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.927299976348877,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9150999784469604,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7385412454605103},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.6276465654373169},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.539014995098114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5316354036331177}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7385412454605103},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.6276465654373169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.539014995098114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5316354036331177},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icde65448.2025.00199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde65448.2025.00199","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 41st International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4806798762","display_name":null,"funder_award_id":"62402422","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1631295847","https://openalex.org/W2004353783","https://openalex.org/W2085866051","https://openalex.org/W2152226152","https://openalex.org/W2533328922","https://openalex.org/W2579495707","https://openalex.org/W2605307020","https://openalex.org/W2756203131","https://openalex.org/W2802508687","https://openalex.org/W2885533908","https://openalex.org/W2988514316","https://openalex.org/W2996847713","https://openalex.org/W2998436408","https://openalex.org/W3019433526","https://openalex.org/W3022629704","https://openalex.org/W3030364939","https://openalex.org/W3034368386","https://openalex.org/W3034944009","https://openalex.org/W3034997257","https://openalex.org/W3080253043","https://openalex.org/W3092822179","https://openalex.org/W3094290081","https://openalex.org/W3112170794","https://openalex.org/W3117755275","https://openalex.org/W3174499835","https://openalex.org/W3178914588","https://openalex.org/W3193056147","https://openalex.org/W3214945533","https://openalex.org/W4220851938","https://openalex.org/W4220918847","https://openalex.org/W4224324119","https://openalex.org/W4224926669","https://openalex.org/W4285602708","https://openalex.org/W4289533938","https://openalex.org/W4312703862","https://openalex.org/W4312744085","https://openalex.org/W4312801469","https://openalex.org/W4319069175","https://openalex.org/W4319586415","https://openalex.org/W4385245566","https://openalex.org/W4385284115","https://openalex.org/W4385568020","https://openalex.org/W4385763767","https://openalex.org/W4387846860","https://openalex.org/W4390190870","https://openalex.org/W4391054888","https://openalex.org/W4392173735","https://openalex.org/W4400909833","https://openalex.org/W4400910490","https://openalex.org/W4401863538"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Spatio-temporal":[0],"data":[1,56,183],"prediction":[2,224],"is":[3,275],"a":[4,11,117,146,197,235],"fundamental":[5],"task":[6],"in":[7],"urban":[8],"computing,":[9],"benefiting":[10],"variety":[12],"of":[13,30,54,85,93,191,229,260],"real-life":[14],"applications":[15],"such":[16],"as":[17],"traffic":[18],"forecasting":[19],"and":[20,27,77,103,158,222,245,263],"environmental":[21],"monitoring.":[22],"Due":[23],"to":[24,57,135,187,203,252,267],"the":[25,82,90,97,154,159,171,188,227,261,268],"dynamic":[26,189],"time-involving":[28],"nature":[29,84,190],"spatio-temporal":[31,108,192,243],"data,":[32,87,179],"researchers":[33],"have":[34],"increasingly":[35],"emphasized":[36],"online":[37,86,94,107,167,178,209,237],"prediction.":[38,109],"However,":[39],"existing":[40],"approaches":[41],"(e.g.,":[42],"URCL)":[43],"typically":[44],"rely":[45],"on":[46,214],"data-replay":[47],"strategies,":[48],"which":[49,150],"require":[50],"storing":[51],"large":[52],"volumes":[53],"historical":[55],"frequently":[58],"update":[59],"their":[60],"models":[61],"with":[62,89],"new":[63],"inputs.":[64],"These":[65],"methods":[66,185],"impose":[67],"substantial":[68],"costs,":[69],"including":[70],"frequent":[71],"buffer":[72],"construction,":[73],"high":[74],"storage":[75],"requirements,":[76],"increased":[78],"training":[79,156,238,262],"complexity.":[80],"Furthermore,":[81],"single-pass":[83,177],"combined":[88],"constrained":[91],"resources":[92],"environments,":[95],"highlights":[96],"urgent":[98],"need":[99],"for":[100,106,240],"more":[101],"efficient":[102],"lightweight":[104],"solutions":[105],"To":[110,141,174],"address":[111],"these":[112],"challenges,":[113],"we":[114],"propose":[115],"Storm,":[116],"knowledge":[118],"distillation":[119],"driven":[120],"continual":[121],"learning":[122,168],"framework.":[123,271],"Storm":[124,144,180,195],"introduces":[125],"Dynamic":[126],"Knowledge":[127],"Distillation":[128],"(DKD),":[129],"leveraging":[130],"an":[131,137],"ever-evolving":[132],"teacher":[133],"model":[134],"train":[136],"effective":[138,182],"student":[139],"model.":[140],"optimize":[142],"efficiency,":[143],"employs":[145],"Mixture-of-Experts":[147],"(MoE)":[148],"mechanism,":[149],"dynamically":[151],"switches":[152],"between":[153],"original":[155],"mode":[157],"DKD":[160],"mode.":[161],"This":[162],"hybrid":[163],"design":[164],"enables":[165],"low-cost":[166],"while":[169,256],"addressing":[170],"stabilityplasticity":[172],"dilemma.":[173],"fully":[175],"leverage":[176],"integrates":[181],"augmentation":[184],"tailored":[186],"data.":[193],"Moreover,":[194],"incorporates":[196],"Gradual":[198],"Parameter":[199],"Freezing":[200],"(GPF)":[201],"module":[202],"progressively":[204],"reduce":[205],"computational":[206],"costs":[207],"during":[208],"training.":[210],"Extensive":[211],"experiments":[212],"conducted":[213],"four":[215],"real-world":[216],"datasets,":[217],"evaluated":[218],"across":[219],"short-term,":[220],"medium-term,":[221],"long-term":[223],"horizons,":[225],"demonstrate":[226],"superiority":[228],"Storm.":[230],"Specifically,":[231],"Storm:":[232],"(i)":[233],"provides":[234],"general":[236],"extension":[239],"various":[241],"offline":[242],"models,":[244],"(ii)":[246],"achieves":[247],"remarkable":[248],"improvements,":[249],"e.g.,":[250],"up":[251],"14.24%":[253],"accuracy":[254],"gains":[255],"requiring":[257],"only":[258],"0.3%":[259],"inference":[264],"time":[265],"compared":[266],"state-of-the-art":[269],"URCL":[270],"The":[272],"source":[273],"code":[274],"publicly":[276],"available":[277],"at":[278],"https://github.com/ZJU-DAILY/Storm.":[279]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}