{"id":"https://openalex.org/W3008796435","doi":"https://doi.org/10.1109/bigdata47090.2019.9006146","title":"Adaptive Deep Learning based Time-Varying Volume Compression","display_name":"Adaptive Deep Learning based Time-Varying Volume Compression","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008796435","doi":"https://doi.org/10.1109/bigdata47090.2019.9006146","mag":"3008796435"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 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/A5101459185","display_name":"Yu Pan","orcid":"https://orcid.org/0000-0001-7515-8492"},"institutions":[{"id":"https://openalex.org/I114395901","display_name":"University of Nebraska\u2013Lincoln","ror":"https://ror.org/043mer456","country_code":"US","type":"education","lineage":["https://openalex.org/I114395901"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yu Pan","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA","institution_ids":["https://openalex.org/I114395901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005251827","display_name":"Feiyu Zhu","orcid":"https://orcid.org/0000-0001-6863-7137"},"institutions":[{"id":"https://openalex.org/I114395901","display_name":"University of Nebraska\u2013Lincoln","ror":"https://ror.org/043mer456","country_code":"US","type":"education","lineage":["https://openalex.org/I114395901"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feiyu Zhu","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA","institution_ids":["https://openalex.org/I114395901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028109081","display_name":"Tian Gao","orcid":"https://orcid.org/0000-0003-4075-4125"},"institutions":[{"id":"https://openalex.org/I114395901","display_name":"University of Nebraska\u2013Lincoln","ror":"https://ror.org/043mer456","country_code":"US","type":"education","lineage":["https://openalex.org/I114395901"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tian Gao","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA","institution_ids":["https://openalex.org/I114395901"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020745360","display_name":"Hongfeng Yu","orcid":"https://orcid.org/0000-0002-0596-8227"},"institutions":[{"id":"https://openalex.org/I114395901","display_name":"University of Nebraska\u2013Lincoln","ror":"https://ror.org/043mer456","country_code":"US","type":"education","lineage":["https://openalex.org/I114395901"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongfeng Yu","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA","institution_ids":["https://openalex.org/I114395901"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101459185"],"corresponding_institution_ids":["https://openalex.org/I114395901"],"apc_list":null,"apc_paid":null,"fwci":0.5829,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77112827,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"16","issue":null,"first_page":"1187","last_page":"1194"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9955000281333923,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9950000047683716,"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":0.8596509695053101},{"id":"https://openalex.org/keywords/terabyte","display_name":"Terabyte","score":0.7698209285736084},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6120685338973999},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.5653873085975647},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5519419312477112},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47432228922843933},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4529196619987488},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4449724555015564},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.4401776194572449},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40880680084228516}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8596509695053101},{"id":"https://openalex.org/C199683683","wikidata":"https://www.wikidata.org/wiki/Q8799","display_name":"Terabyte","level":2,"score":0.7698209285736084},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6120685338973999},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.5653873085975647},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5519419312477112},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47432228922843933},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4529196619987488},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4449724555015564},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.4401776194572449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40880680084228516},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 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/8","score":0.5899999737739563,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W116068320","https://openalex.org/W1480146631","https://openalex.org/W1522301498","https://openalex.org/W1852909287","https://openalex.org/W1898439880","https://openalex.org/W1959608418","https://openalex.org/W1994406579","https://openalex.org/W2009886804","https://openalex.org/W2100495367","https://openalex.org/W2109617680","https://openalex.org/W2110455446","https://openalex.org/W2113432248","https://openalex.org/W2114683661","https://openalex.org/W2130293342","https://openalex.org/W2145094598","https://openalex.org/W2147010257","https://openalex.org/W2153112158","https://openalex.org/W2163922914","https://openalex.org/W2164030956","https://openalex.org/W2168072969","https://openalex.org/W2558748708","https://openalex.org/W2584401907","https://openalex.org/W2755764088","https://openalex.org/W2757203462","https://openalex.org/W2806365215","https://openalex.org/W2953384591","https://openalex.org/W2962845550","https://openalex.org/W2963449488","https://openalex.org/W2964121744","https://openalex.org/W2965958211","https://openalex.org/W2997574889","https://openalex.org/W6628853308","https://openalex.org/W6631190155","https://openalex.org/W6638868411","https://openalex.org/W6640963894","https://openalex.org/W6676170635","https://openalex.org/W6677036450","https://openalex.org/W6681096077","https://openalex.org/W6713134421","https://openalex.org/W6738494155"],"related_works":["https://openalex.org/W2066858118","https://openalex.org/W3011699130","https://openalex.org/W2134017072","https://openalex.org/W1976914335","https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W2915208987","https://openalex.org/W3131327266","https://openalex.org/W2734887215"],"abstract_inverted_index":{"Nowadays,":[0],"floating-point":[1],"temporal-spatial":[2,174],"datasets":[3,25,64,176],"are":[4,109],"routinely":[5],"generated":[6,94],"from":[7],"scientific":[8],"observational":[9],"apparatuses":[10],"or":[11,31],"computer":[12],"simulations":[13],"at":[14,204],"an":[15,185],"unprecedented":[16],"pace.":[17],"The":[18],"sheer":[19],"amount":[20],"of":[21,29,38,77,118,123,131,152,173,214,243],"these":[22,62],"large":[23],"volumetric":[24,112,175],"on":[26],"the":[27,45,128,132,135,150,169,212,239],"order":[28],"terabytes":[30],"petabytes":[32],"consume":[33],"massive":[34,63],"resources":[35,68],"in":[36,69,231],"terms":[37],"bandwidth,":[39],"storage":[40],"and":[41,60,81,95,101,140,192,226,229,241],"computational":[42],"power.":[43],"On":[44],"other":[46],"hand,":[47,70],"scientists,":[48],"equipped":[49],"with":[50,65,190,195,222],"low-end":[51],"post-analysis":[52],"machines,":[53],"often":[54],"find":[55],"it":[56],"impossible":[57],"to":[58,72,92,105,126,149],"visualize":[59],"analyze":[61],"such":[66],"limited":[67],"not":[71,144],"mention":[73],"their":[74],"ultimate":[75],"goal":[76],"real":[78,232],"time":[79],"analysis":[80],"visualization.":[82],"To":[83],"solve":[84],"this":[85,156],"discrepancy,":[86],"a":[87,96,160,205],"compact":[88],"data":[89,202,224],"representation":[90,113,203],"has":[91,104],"be":[93,106,145],"trade-off":[97,136],"between":[98,137],"resource":[99,138],"consumption":[100,139],"analytical":[102,141],"precision":[103],"found.":[107],"There":[108],"many":[110],"existing":[111,196,247],"generating":[114],"methods,":[115,197],"almost":[116],"all":[117],"which":[119],"adopts":[120],"some":[121],"kind":[122],"hand-engineered":[124,153],"heuristics":[125],"extract":[127],"effective":[129],"portion":[130],"datasets.":[133,216],"However,":[134],"quality":[142],"could":[143,200,220],"well":[146],"established":[147],"due":[148],"introduction":[151],"heuristics.":[154],"In":[155],"paper,":[157],"we":[158,237],"present":[159],"deep":[161],"learning":[162],"based":[163,187],"method":[164,199,219],"that":[165],"can":[166],"adaptively":[167],"capture":[168],"inherently":[170],"complicated":[171],"dynamics":[172],"without":[177],"introducing":[178],"any":[179],"hand":[180],"engineered":[181],"features.":[182],"We":[183],"train":[184],"autoencoder":[186],"neural":[188],"network":[189],"quantization":[191],"adaptation.":[193],"Compared":[194],"our":[198,218,244],"learn":[201],"much":[206],"lower":[207],"compressed/uncompressed":[208],"rate":[209],"while":[210],"preserving":[211],"details":[213],"original":[215],"Also,":[217],"adapt":[221],"different":[223],"distribution":[225],"conduct":[227],"compression":[228],"decompression":[230],"time.":[233],"Through":[234],"extensive":[235],"experiments,":[236],"show":[238],"effectiveness":[240],"efficiency":[242],"approach":[245],"over":[246],"methods.":[248]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}