{"id":"https://openalex.org/W4414538914","doi":"https://doi.org/10.1109/icc52391.2025.11162086","title":"Highly Efficient Direct Analytics on Semantic-Aware Time Series Data Compression","display_name":"Highly Efficient Direct Analytics on Semantic-Aware Time Series Data Compression","publication_year":2025,"publication_date":"2025-06-08","ids":{"openalex":"https://openalex.org/W4414538914","doi":"https://doi.org/10.1109/icc52391.2025.11162086"},"language":"en","primary_location":{"id":"doi:10.1109/icc52391.2025.11162086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11162086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://curis.ku.dk/ws/files/514460711/Highly_Efficient_Direct_Analytics.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101715065","display_name":"G. Sun","orcid":"https://orcid.org/0000-0003-3162-3350"},"institutions":[{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"Guoyou Sun","raw_affiliation_strings":["Aarhus University,DIGIT and Department of Electrical and Computer Engineering,Denmark"],"affiliations":[{"raw_affiliation_string":"Aarhus University,DIGIT and Department of Electrical and Computer Engineering,Denmark","institution_ids":["https://openalex.org/I204337017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061315439","display_name":"Panagiotis Karras","orcid":"https://orcid.org/0000-0002-7642-0875"},"institutions":[{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Panagiotis Karras","raw_affiliation_strings":["Aarhus University,DIGIT and Department of Computer Science,Denmark"],"affiliations":[{"raw_affiliation_string":"Aarhus University,DIGIT and Department of Computer Science,Denmark","institution_ids":["https://openalex.org/I204337017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082693278","display_name":"Qi Zhang","orcid":"https://orcid.org/0000-0001-7041-6704"},"institutions":[{"id":"https://openalex.org/I204337017","display_name":"Aarhus University","ror":"https://ror.org/01aj84f44","country_code":"DK","type":"education","lineage":["https://openalex.org/I204337017"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Qi Zhang","raw_affiliation_strings":["Aarhus University,DIGIT and Department of Electrical and Computer Engineering,Denmark"],"affiliations":[{"raw_affiliation_string":"Aarhus University,DIGIT and Department of Electrical and Computer Engineering,Denmark","institution_ids":["https://openalex.org/I204337017"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101715065"],"corresponding_institution_ids":["https://openalex.org/I204337017"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33265794,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5945","last_page":"5950"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9955999851226807,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9955999851226807,"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/T10320","display_name":"Neural Networks and Applications","score":0.9674999713897705,"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/data-compression","display_name":"Data compression","score":0.6822999715805054},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5874999761581421},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5817000269889832},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.517799973487854},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.4860999882221222},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.43560001254081726},{"id":"https://openalex.org/keywords/uncompressed-video","display_name":"Uncompressed video","score":0.43149998784065247},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.42250001430511475},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.421099990606308},{"id":"https://openalex.org/keywords/data-analysis","display_name":"Data analysis","score":0.42010000348091125}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8184000253677368},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.6822999715805054},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5874999761581421},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5817000269889832},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5572999715805054},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.517799973487854},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.4860999882221222},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C162478608","wikidata":"https://www.wikidata.org/wiki/Q4011369","display_name":"Uncompressed video","level":4,"score":0.43149998784065247},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.42250001430511475},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.42010000348091125},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4097999930381775},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.38440001010894775},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.3334999978542328},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.3287999927997589},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.32580000162124634},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.3208000063896179},{"id":"https://openalex.org/C110593043","wikidata":"https://www.wikidata.org/wiki/Q7300787","display_name":"Real-time data","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C194739806","wikidata":"https://www.wikidata.org/wiki/Q66221","display_name":"Computer data storage","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.31049999594688416},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.2815999984741211},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C193519340","wikidata":"https://www.wikidata.org/wiki/Q891179","display_name":"Data loss","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C94835093","wikidata":"https://www.wikidata.org/wiki/Q3113333","display_name":"Data compression ratio","level":5,"score":0.25690001249313354},{"id":"https://openalex.org/C1668388","wikidata":"https://www.wikidata.org/wiki/Q1149776","display_name":"Data management","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icc52391.2025.11162086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11162086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/d2c488d9-639c-4847-9df1-77d85a62b07e","is_oa":true,"landing_page_url":"https://researchprofiles.ku.dk/da/publications/d2c488d9-639c-4847-9df1-77d85a62b07e","pdf_url":"https://curis.ku.dk/ws/files/514460711/Highly_Efficient_Direct_Analytics.pdf","source":{"id":"https://openalex.org/S4306401983","display_name":"Research at the University of Copenhagen (University of Copenhagen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I124055696","host_organization_name":"University of Copenhagen","host_organization_lineage":["https://openalex.org/I124055696"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sun , G , Karras , P & Zhang , Q 2025 , Highly Efficient Direct Analytics on Semantic-Aware Time Series Data Compression . in M Valenti , D Reed & M Torres (eds) , ICC 2025 - IEEE International Conference on Communications . IEEE , IEEE International Conference on Communications , pp. 5945-5950 , 2025 IEEE International Conference on Communications, ICC 2025 , Montreal , Canada , 08/06/2025 . https://doi.org/10.1109/ICC52391.2025.11162086","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:pure.atira.dk:publications/493cb821-fd0b-483a-a207-ddbf89a020d3","is_oa":false,"landing_page_url":"https://pure.au.dk/portal/en/publications/493cb821-fd0b-483a-a207-ddbf89a020d3","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sun, G, Karras, P & Zhang, Q 2025, Highly Efficient Direct Analytics on Semantic-Aware Time Series Data Compression. in M Valenti, D Reed & M Torres (eds), ICC 2025 - IEEE International Conference on Communications. IEEE, IEEE International Conference on Communications, pp. 5945-5950, 2025 IEEE International Conference on Communications, ICC 2025, Montreal, Canada, 08/06/2025. https://doi.org/10.1109/ICC52391.2025.11162086","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/d2c488d9-639c-4847-9df1-77d85a62b07e","is_oa":true,"landing_page_url":"https://researchprofiles.ku.dk/da/publications/d2c488d9-639c-4847-9df1-77d85a62b07e","pdf_url":"https://curis.ku.dk/ws/files/514460711/Highly_Efficient_Direct_Analytics.pdf","source":{"id":"https://openalex.org/S4306401983","display_name":"Research at the University of Copenhagen (University of Copenhagen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I124055696","host_organization_name":"University of Copenhagen","host_organization_lineage":["https://openalex.org/I124055696"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sun , G , Karras , P & Zhang , Q 2025 , Highly Efficient Direct Analytics on Semantic-Aware Time Series Data Compression . in M Valenti , D Reed & M Torres (eds) , ICC 2025 - IEEE International Conference on Communications . IEEE , IEEE International Conference on Communications , pp. 5945-5950 , 2025 IEEE International Conference on Communications, ICC 2025 , Montreal , Canada , 08/06/2025 . https://doi.org/10.1109/ICC52391.2025.11162086","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3791286706","display_name":null,"funder_award_id":"168043","funder_id":"https://openalex.org/F4320322885","funder_display_name":"NordForsk"},{"id":"https://openalex.org/G7767403542","display_name":null,"funder_award_id":"2079-00040B","funder_id":"https://openalex.org/F4320313796","funder_display_name":"Innovationsfonden"}],"funders":[{"id":"https://openalex.org/F4320309928","display_name":"Aarhus Universitet","ror":"https://ror.org/01aj84f44"},{"id":"https://openalex.org/F4320313796","display_name":"Innovationsfonden","ror":"https://ror.org/00daj4111"},{"id":"https://openalex.org/F4320322885","display_name":"NordForsk","ror":"https://ror.org/05bqzfg94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414538914.pdf","grobid_xml":"https://content.openalex.org/works/W4414538914.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Semantic":[0],"communication":[1,89],"has":[2],"emerged":[3],"as":[4,117],"a":[5,68,96,118],"promising":[6],"paradigm":[7],"to":[8,28],"tackle":[9],"the":[10,23,49,85,108,140,157],"challenges":[11],"of":[12,52,84,87,156],"massive":[13],"growing":[14],"data":[15,19,26,55,79,105,131,158,197],"traffic":[16],"and":[17,44,56,74,152,167,195],"sustainable":[18],"communication.":[20],"It":[21],"shifts":[22],"focus":[24],"from":[25,189],"fidelity":[27],"goal-oriented":[29,88],"or":[30],"task-oriented":[31],"semantic":[32,42],"transmission.":[33,198],"While":[34],"deep":[35],"learning-based":[36],"methods":[37,81],"are":[38],"commonly":[39],"used":[40],"for":[41,99,182],"encoding":[43],"decoding,":[45],"they":[46],"struggle":[47],"with":[48,135,164],"sequential":[50],"nature":[51],"time":[53,103],"series":[54,104],"high":[57,193],"computation":[58,168],"cost,":[59],"particularly":[60],"in":[61,71,132,139],"resource-constrained":[62],"IoT":[63,184],"environments.":[64],"Data":[65],"compression":[66,80,110],"plays":[67],"crucial":[69],"role":[70],"reducing":[72,196],"transmission":[73],"storage":[75,166],"costs,":[76],"yet":[77],"traditional":[78],"fall":[82],"short":[83],"demands":[86],"systems.":[90],"In":[91],"this":[92],"paper,":[93],"we":[94,121],"propose":[95],"novel":[97],"method":[98,125],"direct":[100],"analytics":[101,163,181],"on":[102,129,150],"compressed":[106],"by":[107],"Shrink":[109],"algorithm.":[111],"Through":[112],"experimentation":[113],"using":[114],"outlier":[115,179],"detection":[116,180],"case":[119],"study,":[120],"show":[122],"that":[123,173],"our":[124,174],"outperforms":[126],"baselines":[127],"running":[128],"uncompressed":[130],"multiple":[133],"cases,":[134],"merely":[136],"1%":[137],"difference":[138],"worst":[141],"case.":[142],"Additionally,":[143],"it":[144],"achieves":[145],"four":[146],"times":[147],"lower":[148],"runtime":[149],"average":[151],"accesses":[153],"approximately":[154],"10%":[155],"volume,":[159],"which":[160],"enables":[161],"edge":[162],"limited":[165],"power.":[169],"These":[170],"results":[171],"demonstrate":[172],"approach":[175],"offers":[176],"reliable,":[177],"high-speed":[178],"diverse":[183],"applications":[185],"while":[186],"extracting":[187],"semantics":[188],"time-series":[190],"data,":[191],"achieving":[192],"compression,":[194]},"counts_by_year":[],"updated_date":"2026-03-22T08:09:32.410652","created_date":"2025-10-10T00:00:00"}