{"id":"https://openalex.org/W1583500311","doi":"https://doi.org/10.1109/icde.2015.7113347","title":"Hierarchical in-network attribute compression via importance sampling","display_name":"Hierarchical in-network attribute compression via importance sampling","publication_year":2015,"publication_date":"2015-04-01","ids":{"openalex":"https://openalex.org/W1583500311","doi":"https://doi.org/10.1109/icde.2015.7113347","mag":"1583500311"},"language":"en","primary_location":{"id":"doi:10.1109/icde.2015.7113347","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2015.7113347","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 31st International Conference on Data Engineering","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/A5057941268","display_name":"Arlei Silva","orcid":"https://orcid.org/0000-0003-1792-0076"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Arlei Silva","raw_affiliation_strings":["Computer Science Department, University of California, Santa Barbara, CA, USA","Computer Science Dept., University of California, Santa Barbara, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of California, Santa Barbara, CA, USA","institution_ids":["https://openalex.org/I154570441"]},{"raw_affiliation_string":"Computer Science Dept., University of California, Santa Barbara, USA","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001272357","display_name":"Petko Bogdanov","orcid":"https://orcid.org/0000-0001-6310-3224"},"institutions":[{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Petko Bogdanov","raw_affiliation_strings":["Computer Science Department, University at Albany, SUNY, NY, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University at Albany, SUNY, NY, USA","institution_ids":["https://openalex.org/I392282"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036639779","display_name":"Ambuj K. Singh","orcid":"https://orcid.org/0000-0002-1997-7140"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ambuj K. Singh","raw_affiliation_strings":["Computer Science Department, University of California, Santa Barbara, CA, USA","Computer Science Dept., University of California, Santa Barbara, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of California, Santa Barbara, CA, USA","institution_ids":["https://openalex.org/I154570441"]},{"raw_affiliation_string":"Computer Science Dept., University of California, Santa Barbara, USA","institution_ids":["https://openalex.org/I154570441"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057941268"],"corresponding_institution_ids":["https://openalex.org/I154570441"],"apc_list":null,"apc_paid":null,"fwci":1.9043,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.85063442,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"951","last_page":"962"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.994700014591217,"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/T11106","display_name":"Data Management and Algorithms","score":0.9923999905586243,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7030742168426514},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6538315415382385},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.5714541673660278},{"id":"https://openalex.org/keywords/lossy-compression","display_name":"Lossy compression","score":0.5681369304656982},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5441795587539673},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5078678727149963},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5038272738456726},{"id":"https://openalex.org/keywords/vertex","display_name":"Vertex (graph theory)","score":0.4961129128932953},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.49190616607666016},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4619178771972656},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4223299026489258},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.41701966524124146},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4113646447658539},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1665486991405487},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.15522247552871704}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7030742168426514},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6538315415382385},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.5714541673660278},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.5681369304656982},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5441795587539673},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5078678727149963},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5038272738456726},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.4961129128932953},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.49190616607666016},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4619178771972656},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4223299026489258},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41701966524124146},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4113646447658539},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1665486991405487},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.15522247552871704},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icde.2015.7113347","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2015.7113347","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 31st International Conference on Data Engineering","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.726.159","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.726.159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.ucsb.edu/%7Earlei/pubs/icde15.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5199999809265137,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W28625479","https://openalex.org/W67958692","https://openalex.org/W242065599","https://openalex.org/W1480376833","https://openalex.org/W1875112053","https://openalex.org/W1971526329","https://openalex.org/W1991252559","https://openalex.org/W2008620264","https://openalex.org/W2023661891","https://openalex.org/W2042049682","https://openalex.org/W2059793602","https://openalex.org/W2063491776","https://openalex.org/W2080713965","https://openalex.org/W2101196063","https://openalex.org/W2101491865","https://openalex.org/W2103012681","https://openalex.org/W2110557355","https://openalex.org/W2112056172","https://openalex.org/W2112090702","https://openalex.org/W2115755118","https://openalex.org/W2120121938","https://openalex.org/W2125580539","https://openalex.org/W2139103617","https://openalex.org/W2158787690","https://openalex.org/W2161401420","https://openalex.org/W2165515835","https://openalex.org/W2406266674","https://openalex.org/W2787894218","https://openalex.org/W4211042066","https://openalex.org/W4212845555","https://openalex.org/W4231104845","https://openalex.org/W4233413206","https://openalex.org/W4238452917","https://openalex.org/W4285719527","https://openalex.org/W6601193804","https://openalex.org/W6670445733"],"related_works":["https://openalex.org/W2547124190","https://openalex.org/W2385628723","https://openalex.org/W2888954728","https://openalex.org/W2552401318","https://openalex.org/W108076602","https://openalex.org/W3180760233","https://openalex.org/W4384342390","https://openalex.org/W3035703949","https://openalex.org/W4247601675","https://openalex.org/W1033938421"],"abstract_inverted_index":{"Many":[0],"real-world":[1],"complex":[2],"systems":[3],"can":[4],"be":[5],"modeled":[6],"as":[7,94,113,211],"dynamic":[8,32],"networks":[9,20],"with":[10,85,191],"real-valued":[11],"vertex/edge":[12],"attributes.":[13],"Examples":[14],"include":[15],"users'":[16],"opinions":[17],"in":[18,24,135,158,188,207],"social":[19],"and":[21,90,117,165,172,180,215],"average":[22,96],"speeds":[23],"a":[25,38,50,57,69,80,102,114,192],"road":[26],"system.":[27],"When":[28],"managing":[29],"these":[30],"large":[31],"networks,":[33,209],"compressing":[34],"attribute":[35,189],"values":[36,89,147,190],"becomes":[37],"key":[39],"requirement,":[40],"since":[41],"it":[42],"enables":[43],"the":[44,61,95,129,136,141,145,151,186],"answering":[45],"of":[46,60,97,132,156,160,185],"attribute-based":[47],"queries":[48],"regarding":[49],"node/edge":[51,88],"or":[52],"network":[53,71,81,106],"region":[54],"based":[55],"on":[56],"compact":[58,103],"representation":[59,104],"data.":[62],"To":[63],"address":[64],"this":[65],"problem,":[66],"we":[67],"introduce":[68],"lossy":[70],"compression":[72,152,161,163,197],"scheme":[73],"called":[74,108],"Slice":[75],"Tree":[76],"(ST),":[77],"which":[78],"partitions":[79],"into":[82],"smooth":[83],"regions":[84],"respect":[86],"to":[87,126,177,183],"compresses":[91],"each":[92],"value":[93],"its":[98],"region.":[99],"ST":[100,137,157,175,203],"applies":[101],"for":[105],"partitions,":[107],"slices,":[109],"that":[110,148],"are":[111],"defined":[112],"center":[115],"node":[116,146],"radius":[118],"distance.":[119],"We":[120,199],"propose":[121],"an":[122],"importance":[123],"sampling":[124,142],"algorithm":[125],"efficiently":[127],"prune":[128],"search":[130],"space":[131],"candidate":[133],"slices":[134],"construction":[138],"by":[139],"biasing":[140],"process":[143],"towards":[144],"most":[149],"affect":[150],"error.":[153],"The":[154],"effectiveness":[155],"terms":[159],"error,":[162],"rate,":[164],"running":[166],"time":[167],"is":[168],"demonstrated":[169],"using":[170],"synthetic":[171],"real":[173,208],"datasets.":[174],"scales":[176],"million-node":[178],"instances":[179],"removes":[181],"up":[182],"87%":[184],"error":[187],"10":[193],"<sup":[194],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[195],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">3</sup>":[196],"ratio.":[198],"also":[200],"illustrate":[201],"how":[202],"captures":[204],"relevant":[205],"phenomena":[206],"such":[210],"research":[212],"collaboration":[213],"patterns":[214],"traffic":[216],"congestions.":[217]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}