{"id":"https://openalex.org/W2474897245","doi":"https://doi.org/10.1145/2911451.2914685","title":"Sampling Strategies and Active Learning for Volume Estimation","display_name":"Sampling Strategies and Active Learning for Volume Estimation","publication_year":2016,"publication_date":"2016-07-07","ids":{"openalex":"https://openalex.org/W2474897245","doi":"https://doi.org/10.1145/2911451.2914685","mag":"2474897245"},"language":"en","primary_location":{"id":"doi:10.1145/2911451.2914685","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2911451.2914685","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=2914685&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://dl.acm.org/ft_gateway.cfm?id=2914685&type=pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100392954","display_name":"Haotian Zhang","orcid":"https://orcid.org/0000-0001-7786-389X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Haotian Zhang","raw_affiliation_strings":["University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082997975","display_name":"Jimmy Lin","orcid":"https://orcid.org/0000-0002-0661-7189"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jimmy Lin","raw_affiliation_strings":["University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084091807","display_name":"Gordon V. Cormack","orcid":"https://orcid.org/0000-0002-5890-0293"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Gordon V. Cormack","raw_affiliation_strings":["University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049222552","display_name":"Mark D. Smucker","orcid":"https://orcid.org/0000-0003-4968-6405"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mark D. Smucker","raw_affiliation_strings":["University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"981","last_page":"984"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9994999766349792,"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/T11269","display_name":"Algorithms and Data Compression","score":0.9984999895095825,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9972000122070312,"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/computer-science","display_name":"Computer science","score":0.7834603786468506},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6667330861091614},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.6613118052482605},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5043843984603882},{"id":"https://openalex.org/keywords/switchover","display_name":"Switchover","score":0.5040794610977173},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4961901307106018},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.4931873679161072},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.4451752305030823},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.4348278045654297},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.43115121126174927},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4211616516113281},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.42025452852249146},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4166318476200104},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4120335578918457},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38481032848358154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3760519027709961},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.24935084581375122}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7834603786468506},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6667330861091614},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.6613118052482605},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5043843984603882},{"id":"https://openalex.org/C124200919","wikidata":"https://www.wikidata.org/wiki/Q2373346","display_name":"Switchover","level":2,"score":0.5040794610977173},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4961901307106018},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.4931873679161072},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.4451752305030823},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.4348278045654297},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.43115121126174927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4211616516113281},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.42025452852249146},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4166318476200104},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4120335578918457},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38481032848358154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3760519027709961},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.24935084581375122},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2911451.2914685","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2911451.2914685","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=2914685&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/2911451.2914685","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2911451.2914685","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=2914685&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1961012327","display_name":null,"funder_award_id":"1405688","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"},{"id":"https://openalex.org/G2830599200","display_name":"II-EN: Hadoop NextGen Infrastructure for Heterogeneous Approaches to Data-Intensive Computing","funder_award_id":"1405688","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4394039926","display_name":"III: Small: Providing Relevant and Timely Results: Real-Time Search Architectures and Relevance Algorithms","funder_award_id":"1218043","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6609948673","display_name":null,"funder_award_id":"IIS-1218043","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6894796833","display_name":null,"funder_award_id":"IIS-1218043 and CNS-1405688","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6945253973","display_name":null,"funder_award_id":"CNS-1405688","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309327","display_name":"Google","ror":"https://ror.org/00njsd438"},{"id":"https://openalex.org/F4320322676","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2474897245.pdf","grobid_xml":"https://content.openalex.org/works/W2474897245.grobid-xml"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W1700671489","https://openalex.org/W2040066835","https://openalex.org/W2155961949","https://openalex.org/W2341384651","https://openalex.org/W2399991609","https://openalex.org/W2490176128","https://openalex.org/W2604321711","https://openalex.org/W6776056717"],"related_works":["https://openalex.org/W2385368614","https://openalex.org/W2391729463","https://openalex.org/W2369951470","https://openalex.org/W2368126447","https://openalex.org/W2379248043","https://openalex.org/W2370187656","https://openalex.org/W2367027673","https://openalex.org/W2097736318","https://openalex.org/W2358294942","https://openalex.org/W4367460280"],"abstract_inverted_index":{"This":[0],"paper":[1],"tackles":[2],"the":[3,10,27,44,50,68,79,85,106,122,144],"challenge":[4],"of":[5,12,29,46,53,81,133],"accurately":[6],"and":[7,48,71,130],"efficiently":[8],"estimating":[9,26],"number":[11,80],"relevant":[13,82],"documents":[14,83],"in":[15,84,108],"a":[16,19,37,54,90,131],"collection":[17,132],"for":[18,95],"particular":[20],"topic.":[21],"One":[22],"real-world":[23],"application":[24],"is":[25,40,59],"volume":[28],"social":[30],"media":[31],"posts":[32],"(e.g.,":[33],"tweets)":[34],"pertaining":[35],"to":[36,42,60,65,73,77],"topic,":[38],"which":[39,100],"fundamental":[41],"tracking":[43],"popularity":[45],"politicians":[47],"brands,":[49],"potential":[51],"sales":[52],"product,":[55],"etc.":[56],"Our":[57],"insight":[58],"leverage":[61],"active":[62],"learning":[63],"techniques":[64,76],"find":[66],"all":[67],"\"easy\"":[69],"documents,":[70],"then":[72],"use":[74],"sampling":[75,119],"infer":[78],"residual":[86],"collection.":[87],"We":[88,124],"propose":[89],"simple":[91],"yet":[92],"effective":[93],"technique":[94,138],"determining":[96],"this":[97],"\"switchover\"":[98],"point,":[99],"intuitively":[101],"can":[102],"be":[103],"understood":[104],"as":[105,115,117],"\"knee\"":[107],"an":[109],"effort":[110],"vs.":[111],"recall":[112],"gain":[113],"curve,":[114],"well":[116],"alternative":[118],"strategies":[120],"beyond":[121],"knee.":[123],"show":[125],"on":[126],"several":[127,148],"TREC":[128],"datasets":[129],"tweets":[134],"that":[135],"our":[136],"best":[137],"yields":[139],"more":[140],"accurate":[141],"estimates":[142],"(with":[143],"same":[145],"effort)":[146],"than":[147],"alternatives.":[149]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}