{"id":"https://openalex.org/W2776854168","doi":"https://doi.org/10.1109/tkde.2017.2785803","title":"Sampling and Reconstruction Using Bloom Filters","display_name":"Sampling and Reconstruction Using Bloom Filters","publication_year":2017,"publication_date":"2017-12-21","ids":{"openalex":"https://openalex.org/W2776854168","doi":"https://doi.org/10.1109/tkde.2017.2785803","mag":"2776854168"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2017.2785803","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2017.2785803","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1701.03308","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Neha Sengupta","orcid":"https://orcid.org/0000-0002-0162-374X"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Neha Sengupta","raw_affiliation_strings":["Indian Institute of Technology, Delhi, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology, Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Amitabha Bagchi","orcid":null},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amitabha Bagchi","raw_affiliation_strings":["Indian Institute of Technology, Delhi, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology, Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Srikanta Bedathur","orcid":"https://orcid.org/0000-0002-3949-2175"},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Srikanta Bedathur","raw_affiliation_strings":["IBM Research Labs India, New Delhi, Delhi, India"],"affiliations":[{"raw_affiliation_string":"IBM Research Labs India, New Delhi, Delhi, India","institution_ids":["https://openalex.org/I4210103279"]}]},{"author_position":"last","author":{"id":null,"display_name":"Maya Ramanath","orcid":null},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Maya Ramanath","raw_affiliation_strings":["Indian Institute of Technology, Delhi, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology, Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I68891433"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.18942236,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"30","issue":"7","first_page":"1324","last_page":"1337"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.09920000284910202,"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"}},"topics":[{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.09920000284910202,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.07259999960660934,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.06360000371932983,"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/bloom-filter","display_name":"Bloom filter","score":0.9607999920845032},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.842199981212616},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.7073000073432922},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6507999897003174},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.574999988079071},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5162000060081482}],"concepts":[{"id":"https://openalex.org/C147224247","wikidata":"https://www.wikidata.org/wiki/Q885373","display_name":"Bloom filter","level":2,"score":0.9607999920845032},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.842199981212616},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7753000259399414},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.7073000073432922},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6507999897003174},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.574999988079071},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5325000286102295},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5162000060081482},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5059000253677368},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4408999979496002},{"id":"https://openalex.org/C67388219","wikidata":"https://www.wikidata.org/wiki/Q207440","display_name":"Hash table","level":3,"score":0.3822000026702881},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26840001344680786},{"id":"https://openalex.org/C2780757406","wikidata":"https://www.wikidata.org/wiki/Q465837","display_name":"Skyline","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tkde.2017.2785803","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2017.2785803","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1701.03308","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1701.03308","pdf_url":"https://arxiv.org/pdf/1701.03308","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1701.03308","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1701.03308","pdf_url":"https://arxiv.org/pdf/1701.03308","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2776854168.pdf","grobid_xml":"https://content.openalex.org/works/W2776854168.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W153413138","https://openalex.org/W1975253563","https://openalex.org/W1993284846","https://openalex.org/W1996263819","https://openalex.org/W2005228765","https://openalex.org/W2012251118","https://openalex.org/W2022858489","https://openalex.org/W2032293079","https://openalex.org/W2063550827","https://openalex.org/W2074633331","https://openalex.org/W2088518924","https://openalex.org/W2102814211","https://openalex.org/W2111451400","https://openalex.org/W2119885577","https://openalex.org/W2123845384","https://openalex.org/W2144146806","https://openalex.org/W2145446394","https://openalex.org/W2150999591","https://openalex.org/W2166310193","https://openalex.org/W2478210969","https://openalex.org/W4244638572","https://openalex.org/W6608063655","https://openalex.org/W6632885495","https://openalex.org/W6679951598"],"related_works":[],"abstract_inverted_index":{"In":[0,74],"this":[1,34],"paper,":[2],"we":[3,110],"address":[4,33],"the":[5,22,30,58,71,75,78,83,91,99,120,124,149],"problem":[6],"of":[7,24,101,126],"sampling":[8],"from":[9,57],"a":[10,14,18,38,62,106,112],"set":[11,15,59,72,100],"and":[12,65,145,153,165],"reconstructing":[13],"stored":[16,60],"as":[17,132,134],"Bloom":[19,63,84],"filter.":[20],"To":[21],"best":[23],"our":[25,27,161],"knowledge":[26],"work":[28],"is":[29,95],"first":[31],"to":[32,51,69,97,105],"question.":[35],"We":[36,122,136],"introduce":[37],"novel":[39],"hierarchical":[40],"data":[41],"structure":[42],"called":[43,117],"BloomSampleTree":[44,150],"that":[45,93,103,160],"helps":[46],"us":[47,68],"design":[48],"efficient":[49,164],"algorithms":[50],"extract":[52],"an":[53,156],"almost":[54],"uniform":[55],"sample":[56,146],"in":[61,82,90],"filter":[64,85],"also":[66],"allows":[67],"reconstruct":[70],"efficiently.":[73],"case":[76],"where":[77],"hash":[79,108],"functions":[80],"used":[81],"implementation":[86],"are":[87,163],"partially":[88],"invertible,":[89],"sense":[92],"it":[94],"easy":[96],"calculate":[98],"elements":[102],"map":[104],"particular":[107],"value,":[109],"propose":[111],"second,":[113],"more":[114],"space-efficient":[115],"method":[116],"HashInvert":[118],"for":[119,142,148],"reconstruction.":[121],"study":[123],"properties":[125],"these":[127],"two":[128],"methods":[129,144,162],"both":[130,143],"analytically":[131],"well":[133],"experimentally.":[135],"provide":[137],"bounds":[138],"on":[139],"run":[140],"times":[141],"quality":[147],"based":[151],"algorithm,":[152],"show":[154],"through":[155],"extensive":[157],"experimental":[158],"evaluation":[159],"effective.":[166]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2018-01-05T00:00:00"}
