{"id":"https://openalex.org/W7138297401","doi":"https://doi.org/10.48550/arxiv.2603.14190","title":"Sublime: Sublinear Error &amp; Space for Unbounded Skewed Streams","display_name":"Sublime: Sublinear Error &amp; Space for Unbounded Skewed Streams","publication_year":2026,"publication_date":"2026-03-15","ids":{"openalex":"https://openalex.org/W7138297401","doi":"https://doi.org/10.48550/arxiv.2603.14190"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14190","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14190","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.14190","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129685545","display_name":"Navid Eslami","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eslami, Navid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016433369","display_name":"Ioana O. Bercea","orcid":"https://orcid.org/0000-0001-8430-2441"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bercea, Ioana O.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014293815","display_name":"Rasmus Pagh","orcid":"https://orcid.org/0000-0002-1516-9306"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pagh, Rasmus","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5003707725","display_name":"Niv Dayan","orcid":"https://orcid.org/0000-0003-0314-0167"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dayan, Niv","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.8317999839782715,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.8317999839782715,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.04230000078678131,"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"}},{"id":"https://openalex.org/T10101","display_name":"Cloud Computing and Resource Management","score":0.01769999973475933,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/sketch","display_name":"Sketch","score":0.682699978351593},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.6396999955177307},{"id":"https://openalex.org/keywords/sublinear-function","display_name":"Sublinear function","score":0.5436999797821045},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4925999939441681},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.4300999939441681},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.3727000057697296},{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.36469998955726624},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.3560999929904938},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.3546999990940094},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.3544999957084656}],"concepts":[{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.682699978351593},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.6396999955177307},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6331999897956848},{"id":"https://openalex.org/C117160843","wikidata":"https://www.wikidata.org/wiki/Q338652","display_name":"Sublinear function","level":2,"score":0.5436999797821045},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5286999940872192},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.4300999939441681},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36309999227523804},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.3546999990940094},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C79516417","wikidata":"https://www.wikidata.org/wiki/Q1376168","display_name":"Memoization","level":4,"score":0.3181999921798706},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.29420000314712524},{"id":"https://openalex.org/C2781018962","wikidata":"https://www.wikidata.org/wiki/Q5164884","display_name":"Container (type theory)","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C2780388253","wikidata":"https://www.wikidata.org/wiki/Q5421508","display_name":"Exponent","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.271699994802475},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C65165936","wikidata":"https://www.wikidata.org/wiki/Q575784","display_name":"Baseband","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.25679999589920044},{"id":"https://openalex.org/C176649486","wikidata":"https://www.wikidata.org/wiki/Q2308807","display_name":"Memory management","level":3,"score":0.25609999895095825},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14190","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14190","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.14190","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14190","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"stream":[1,16,169],"processing":[2],"systems":[3],"often":[4],"need":[5],"to":[6,43,69,120,156,193,199],"track":[7],"the":[8,71,78,94,97,146,168,222,225],"frequency":[9,40,48,117],"of":[10,28,81,96,108,176,186,224],"distinct":[11],"keys":[12],"in":[13,17,53],"a":[14,25,105,113,161,179,183],"data":[15,36],"real-time.":[18],"Since":[19],"maintaining":[20,228],"exact":[21],"counts":[22],"can":[23,191],"require":[24],"prohibitive":[26],"amount":[27],"memory,":[29],"many":[30,82],"applications":[31,190],"rely":[32,103],"on":[33,76,104],"compact,":[34],"probabilistic":[35],"structures":[37],"known":[38],"as":[39,139,167],"estimation":[41,49,87,118],"sketches":[42,50,119],"approximate":[44],"them.":[45],"However,":[46],"mainstream":[47],"fall":[51],"short":[52,133],"two":[54],"critical":[55],"aspects.":[56],"First,":[57],"they":[58,65,102,140],"are":[59],"memory-inefficient":[60],"under":[61,128],"skewed":[62],"workloads":[63],"because":[64],"use":[66],"uniformly-sized":[67],"counters":[68,134,177],"count":[70],"keys,":[72],"thus":[73],"wasting":[74],"memory":[75,126,220],"storing":[77,142],"leading":[79],"zeros":[80],"small":[83],"counts.":[84],"Second,":[85],"their":[86,143,194],"error":[88],"deteriorates":[89],"at":[90,178],"least":[91],"linearly":[92],"with":[93,132],"length":[95],"stream--which":[98],"may":[99],"grow":[100],"indefinitely--because":[101],"fixed":[106],"number":[107,175],"counters.":[109],"We":[110,196],"present":[111],"Sublime,":[112],"framework":[114],"that":[115,189,214],"generalizes":[116],"address":[121],"these":[122],"challenges.":[123],"To":[124,164],"reduce":[125],"footprint":[127],"skew,":[129],"Sublime":[130,171,198,215],"begins":[131],"and":[135,159,203,209,219],"dynamically":[136],"elongates":[137],"them":[138],"overflow,":[141],"extensions":[144],"within":[145],"same":[147],"cache":[148],"line.":[149],"It":[150],"employs":[151],"efficient":[152],"bit":[153],"manipulation":[154],"routines":[155],"quickly":[157],"locate":[158],"access":[160],"counter's":[162],"extensions.":[163],"maintain":[165],"accuracy":[166,218],"grows,":[170],"also":[172],"expands":[173],"its":[174],"configurable":[180],"rate,":[181],"exposing":[182],"new":[184],"spectrum":[185],"accuracy-memory":[187],"tradeoffs":[188],"tune":[192],"needs.":[195],"apply":[197],"both":[200],"Count-Min":[201],"Sketch":[202],"Count":[204],"Sketch.":[205],"Through":[206],"theoretical":[207],"analysis":[208],"empirical":[210],"evaluation,":[211],"we":[212],"show":[213],"significantly":[216],"improves":[217],"over":[221],"state":[223],"art":[226],"while":[227],"competitive":[229],"or":[230],"superior":[231],"performance.":[232]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-18T00:00:00"}
