{"id":"https://openalex.org/W4282565958","doi":"https://doi.org/10.1145/3514221.3526178","title":"TxtAlign: Efficient Near-Duplicate Text Alignment Search via Bottom-k Sketches for Plagiarism Detection","display_name":"TxtAlign: Efficient Near-Duplicate Text Alignment Search via Bottom-k Sketches for Plagiarism Detection","publication_year":2022,"publication_date":"2022-06-10","ids":{"openalex":"https://openalex.org/W4282565958","doi":"https://doi.org/10.1145/3514221.3526178"},"language":"en","primary_location":{"id":"doi:10.1145/3514221.3526178","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3514221.3526178","pdf_url":null,"source":{"id":"https://openalex.org/S4363608845","display_name":"Proceedings of the 2022 International Conference on Management of Data","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 International Conference on Management of Data","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/A5101850444","display_name":"Zhizhi Wang","orcid":"https://orcid.org/0000-0003-2223-9621"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhizhi Wang","raw_affiliation_strings":["Rutgers University, New Brunswick, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038546373","display_name":"Chaoji Zuo","orcid":"https://orcid.org/0000-0001-9869-5602"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chaoji Zuo","raw_affiliation_strings":["Rutgers University, New Brunswick, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103237059","display_name":"Dong Deng","orcid":"https://orcid.org/0000-0002-4596-3850"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Deng","raw_affiliation_strings":["Rutgers University, New Brunswick, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101850444"],"corresponding_institution_ids":["https://openalex.org/I102322142"],"apc_list":null,"apc_paid":null,"fwci":1.0394,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.77930403,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1146","last_page":"1159"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9959999918937683,"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/T10028","display_name":"Topic Modeling","score":0.9959999918937683,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9854999780654907,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9830999970436096,"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":0.7904683947563171},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.7169458866119385},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5781416296958923},{"id":"https://openalex.org/keywords/sketch","display_name":"Sketch","score":0.5703920722007751},{"id":"https://openalex.org/keywords/plagiarism-detection","display_name":"Plagiarism detection","score":0.524718165397644},{"id":"https://openalex.org/keywords/full-text-search","display_name":"Full text search","score":0.506363034248352},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.49079498648643494},{"id":"https://openalex.org/keywords/source-document","display_name":"Source document","score":0.47569239139556885},{"id":"https://openalex.org/keywords/document-retrieval","display_name":"Document retrieval","score":0.43803805112838745},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.43467336893081665},{"id":"https://openalex.org/keywords/document-clustering","display_name":"Document clustering","score":0.41251397132873535},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.25311678647994995},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22506830096244812},{"id":"https://openalex.org/keywords/search-engine","display_name":"Search engine","score":0.20474332571029663},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.15019655227661133},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.125975102186203}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7904683947563171},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.7169458866119385},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5781416296958923},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.5703920722007751},{"id":"https://openalex.org/C2780907237","wikidata":"https://www.wikidata.org/wiki/Q2986238","display_name":"Plagiarism detection","level":2,"score":0.524718165397644},{"id":"https://openalex.org/C20228898","wikidata":"https://www.wikidata.org/wiki/Q83540","display_name":"Full text search","level":3,"score":0.506363034248352},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.49079498648643494},{"id":"https://openalex.org/C105888452","wikidata":"https://www.wikidata.org/wiki/Q7565148","display_name":"Source document","level":2,"score":0.47569239139556885},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.43803805112838745},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.43467336893081665},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.41251397132873535},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.25311678647994995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22506830096244812},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.20474332571029663},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15019655227661133},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.125975102186203}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3514221.3526178","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3514221.3526178","pdf_url":null,"source":{"id":"https://openalex.org/S4363608845","display_name":"Proceedings of the 2022 International Conference on Management of Data","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 International Conference on Management of Data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1606292664","https://openalex.org/W1790180460","https://openalex.org/W1965996575","https://openalex.org/W1973001156","https://openalex.org/W1974336599","https://openalex.org/W1991175610","https://openalex.org/W1998840390","https://openalex.org/W2007842132","https://openalex.org/W2025051251","https://openalex.org/W2081193615","https://openalex.org/W2092236286","https://openalex.org/W2097184821","https://openalex.org/W2097776316","https://openalex.org/W2097865464","https://openalex.org/W2104671598","https://openalex.org/W2105436061","https://openalex.org/W2111295912","https://openalex.org/W2112036337","https://openalex.org/W2121269638","https://openalex.org/W2134212491","https://openalex.org/W2139660688","https://openalex.org/W2140431670","https://openalex.org/W2151930506","https://openalex.org/W2167302605","https://openalex.org/W2167847032","https://openalex.org/W2294331997","https://openalex.org/W2343777367","https://openalex.org/W2400796284","https://openalex.org/W2464357947","https://openalex.org/W2506088122","https://openalex.org/W2619410666","https://openalex.org/W2798412430","https://openalex.org/W2905907858","https://openalex.org/W2980420460","https://openalex.org/W3041537557","https://openalex.org/W3177445587","https://openalex.org/W6632555176","https://openalex.org/W6636127415","https://openalex.org/W6636190696","https://openalex.org/W6674576723","https://openalex.org/W6677629841","https://openalex.org/W6679663036","https://openalex.org/W6681706716","https://openalex.org/W6682042839","https://openalex.org/W6682678078","https://openalex.org/W6683450081","https://openalex.org/W6717704407","https://openalex.org/W6986298011"],"related_works":["https://openalex.org/W95389613","https://openalex.org/W2345466255","https://openalex.org/W2783636883","https://openalex.org/W2000031603","https://openalex.org/W4293768631","https://openalex.org/W2097605975","https://openalex.org/W2807454818","https://openalex.org/W178189999","https://openalex.org/W2102270039","https://openalex.org/W2295176745"],"abstract_inverted_index":{"In":[0,190],"this":[1,191],"paper,":[2,192],"we":[3,193,227,277],"study":[4],"the":[5,26,31,45,71,79,84,126,137,150,167,172,185,197,206,222,232,244,273],"near-duplicate":[6,27],"text":[7,56,75,173],"alignment":[8,76,174],"search":[9],"problem,":[10,276],"which,":[11],"given":[12],"a":[13,20,65,109,117,133,162,218,235,254],"collection":[14],"of":[15,129,208,283],"source":[16,36,53,62,90,111,130,186,274],"(data)":[17],"documents":[18,63,131,182],"and":[19,34,55,87,116,153,183,259],"suspicious":[21,32,72,85,118],"(query)":[22],"document,":[23],"finds":[24,39,60,77],"all":[25,78,231,243],"passage":[28,81,106,290],"pairs":[29,82,107],"between":[30,83,108],"document":[33,73,86,112,119,219,236,255],"every":[35,88],"document.":[37,91],"It":[38],"applications":[40],"in":[41,49,64,132,217,234,253,267,292],"plagiarism":[42,50],"detection.":[43],"Specifically,":[44],"first":[46],"two":[47,181,209,293],"steps":[48],"detection":[51],"are":[52,104,298,309],"retrieval":[54,59,187,275],"alignment.":[57],"Source":[58],"candidate":[61,89],"corpus":[66],"that":[67,213,242,306],"share":[68,221],"content":[69],"with":[70,113,120,256,285,295],"while":[74],"similar":[80],"This":[92,100],"problem":[93,188],"is":[94,101],"computation-intensive,":[95],"especially":[96],"for":[97,171,180],"long":[98],"documents.":[99],"because":[102],"there":[103],"O(n2m2)":[105],"single":[110],"n":[114,257],"words":[115,258],"m":[121],"words,":[122],"not":[123],"to":[124,136,195,204,229,263,271,280],"mention":[125],"large":[127],"number":[128],"corpus.":[134],"Due":[135],"high":[138],"computation":[139],"cost,":[140],"existing":[141],"solutions":[142],"primarily":[143],"rely":[144],"on":[145,302],"heuristic":[146],"rules,":[147],"such":[148],"as":[149],"\"seeding-extension-filtering\"":[151],"pipeline,":[152],"involve":[154],"many":[155,214],"hard-to-tune":[156],"hyper-parameters.":[157],"To":[158],"address":[159,272],"these":[160,265],"issues,":[161],"recent":[163],"work":[164],"ALLIGN":[165,177],"leverages":[166],"min-wise":[168],"hash":[169],"sketch":[170,199],"problem.":[175],"However,":[176],"only":[178,278],"works":[179],"leaves":[184],"unattended.":[189],"propose":[194,228],"leverage":[196],"bottom-k":[198,224,287],"(a.k.a.":[200],"conditional":[201],"random":[202],"sampling)":[203],"estimate":[205],"similarity":[207],"passages.":[210],"We":[211,240],"observe":[212],"nearby":[215],"passages":[216,233,246,284],"would":[220],"same":[223],"sketch.":[225],"Thus":[226],"group":[230],"by":[237],"their":[238],"sketches.":[239,288],"prove":[241],"O(n2)":[245],"can":[247],"be":[248],"partitioned":[249],"into":[250],"O(nk)":[251],"groups":[252,266,282,294],"develop":[260],"an":[261],"algorithm":[262],"generate":[264],"O(nlogn+nk)":[268],"time.":[269],"Then,":[270],"need":[279],"find":[281],"\"similar\"":[286,296],"Every":[289],"pair":[291],"sketches":[297],"near-duplicates.":[299],"Experimental":[300],"results":[301],"real-world":[303],"datasets":[304],"show":[305],"our":[307],"techniques":[308],"highly":[310],"efficient.":[311]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
