{"id":"https://openalex.org/W7164822860","doi":"https://doi.org/10.4230/lipics.sea.2026.11","title":"Wavelet Forests Revisited","display_name":"Wavelet Forests Revisited","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7164822860","doi":"https://doi.org/10.4230/lipics.sea.2026.11"},"language":"en","primary_location":{"id":"pmh:oai:drops-oai.dagstuhl.de:26015","is_oa":true,"landing_page_url":"https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2026.11","pdf_url":"https://drops.dagstuhl.de/storage/00lipics/lipics-vol371-sea2026/LIPIcs.SEA.2026.11/LIPIcs.SEA.2026.11.pdf","source":{"id":"https://openalex.org/S4377196569","display_name":"DROPS (Schloss Dagstuhl \u2013 Leibniz Center for Informatics)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2799853480","host_organization_name":"Schloss Dagstuhl \u2013 Leibniz Center for Informatics","host_organization_lineage":["https://openalex.org/I2799853480"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"publishedVersion"},"type":"conference-paper","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://drops.dagstuhl.de/storage/00lipics/lipics-vol371-sea2026/LIPIcs.SEA.2026.11/LIPIcs.SEA.2026.11.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138645601","display_name":"Eric Chiu","orcid":"https://orcid.org/0009-0003-4962-3436"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chiu, Eric","raw_affiliation_strings":["Department of Computer Science, Stony Brook University, NY, USA"],"raw_orcid":"https://orcid.org/0009-0003-4962-3436","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Stony Brook University, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007292222","display_name":"Dominik Kempa","orcid":"https://orcid.org/0000-0003-2286-7417"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kempa, Dominik","raw_affiliation_strings":["Department of Computer Science, Stony Brook University, NY, USA"],"raw_orcid":"https://orcid.org/0000-0003-2286-7417","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Stony Brook University, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I59553526"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T11269","display_name":"Algorithms and Data Compression","score":0.3930000066757202,"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/T11269","display_name":"Algorithms and Data Compression","score":0.3930000066757202,"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":0.30300000309944153,"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"}},{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.1728000044822693,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.8417999744415283},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5637999773025513},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5041999816894531},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4821999967098236},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.4569999873638153},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.4433000087738037},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.44209998846054077},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4255000054836273},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.42239999771118164}],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.8417999744415283},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5637999773025513},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5386999845504761},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5041999816894531},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.490200012922287},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4821999967098236},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.4569999873638153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4512999951839447},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.4433000087738037},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.44209998846054077},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C73339587","wikidata":"https://www.wikidata.org/wiki/Q1375942","display_name":"Stationary wavelet transform","level":5,"score":0.3912000060081482},{"id":"https://openalex.org/C199550912","wikidata":"https://www.wikidata.org/wiki/Q3238415","display_name":"Lifting scheme","level":5,"score":0.37059998512268066},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.35190001130104065},{"id":"https://openalex.org/C88829872","wikidata":"https://www.wikidata.org/wiki/Q5048176","display_name":"Cascade algorithm","level":5,"score":0.349700003862381},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.3328000009059906},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C111350171","wikidata":"https://www.wikidata.org/wiki/Q7443700","display_name":"Second-generation wavelet transform","level":5,"score":0.3075000047683716},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28540000319480896},{"id":"https://openalex.org/C136968285","wikidata":"https://www.wikidata.org/wiki/Q3246374","display_name":"Set partitioning in hierarchical trees","level":5,"score":0.2736999988555908},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26739999651908875}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:drops-oai.dagstuhl.de:26015","is_oa":true,"landing_page_url":"https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2026.11","pdf_url":"https://drops.dagstuhl.de/storage/00lipics/lipics-vol371-sea2026/LIPIcs.SEA.2026.11/LIPIcs.SEA.2026.11.pdf","source":{"id":"https://openalex.org/S4377196569","display_name":"DROPS (Schloss Dagstuhl \u2013 Leibniz Center for Informatics)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2799853480","host_organization_name":"Schloss Dagstuhl \u2013 Leibniz Center for Informatics","host_organization_lineage":["https://openalex.org/I2799853480"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"publishedVersion"},{"id":"doi:10.4230/lipics.sea.2026.11","is_oa":true,"landing_page_url":"https://doi.org/10.4230/lipics.sea.2026.11","pdf_url":null,"source":{"id":"https://openalex.org/S7407052059","display_name":"Dagstuhl Research Online Publication Server","issn_l":null,"issn":[],"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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"ConferencePaper"}],"best_oa_location":{"id":"pmh:oai:drops-oai.dagstuhl.de:26015","is_oa":true,"landing_page_url":"https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2026.11","pdf_url":"https://drops.dagstuhl.de/storage/00lipics/lipics-vol371-sea2026/LIPIcs.SEA.2026.11/LIPIcs.SEA.2026.11.pdf","source":{"id":"https://openalex.org/S4377196569","display_name":"DROPS (Schloss Dagstuhl \u2013 Leibniz Center for Informatics)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2799853480","host_organization_name":"Schloss Dagstuhl \u2013 Leibniz Center for Informatics","host_organization_lineage":["https://openalex.org/I2799853480"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"publishedVersion"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.5278798937797546,"display_name":"Life in Land"}],"awards":[{"id":"https://openalex.org/G4450559485","display_name":"CAREER: Scalable and Flexible Indexing of Compressed Sequences","funder_award_id":"2337891","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","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"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7164822860.pdf","grobid_xml":"https://content.openalex.org/works/W7164822860.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Rank":[0],"and":[1,15,60,103,118,126,145],"select":[2,88,93],"queries":[3,28],"are":[4,123],"basic":[5],"operations":[6],"on":[7,45,113,148],"sequences,":[8],"with":[9,98],"applications":[10],"in":[11,127],"compressed":[12],"text":[13],"indexes":[14],"other":[16],"space-efficient":[17],"data":[18,24],"structures.":[19],"One":[20],"of":[21,116,139],"the":[22,30,46,54,105,137],"standard":[23],"structures":[25,43,52,107],"supporting":[26],"these":[27],"is":[29],"wavelet":[31,38,64,84,121],"tree.":[32],"In":[33,111],"this":[34,73],"paper,":[35],"we":[36],"study":[37,136],"forests,":[39],"that":[40,72,92,104],"is,":[41],"wavelet-tree":[42,132],"based":[44],"fixed-block":[47],"compression":[48],"boosting":[49],"technique.":[50],"Such":[51],"partition":[53],"input":[55],"sequence":[56],"into":[57],"fixed-size":[58],"blocks":[59],"build":[61],"a":[62,114],"separate":[63],"tree":[65],"for":[66,79],"each":[67],"block.":[68],"Previous":[69],"work":[70],"showed":[71],"approach":[74],"yields":[75],"strong":[76],"practical":[77],"performance":[78],"rank":[80],"queries.":[81,89],"We":[82,90,134],"extend":[83],"forests":[85,122],"to":[86],"support":[87,94],"show":[91],"can":[95],"be":[96],"added":[97],"little":[99],"additional":[100],"space":[101],"overhead":[102],"resulting":[106],"remain":[108],"practically":[109],"efficient.":[110],"experiments":[112],"range":[115],"non-repetitive":[117],"repetitive":[119],"inputs,":[120],"competitive":[124],"with,":[125],"most":[128],"cases":[129],"outperform,":[130],"standalone":[131],"implementations.":[133],"also":[135],"effect":[138],"internal":[140],"parameters,":[141],"including":[142],"superblock":[143],"size":[144],"navigational":[146],"data,":[147],"select-query":[149],"performance.":[150]},"counts_by_year":[],"updated_date":"2026-07-01T08:55:40.977307","created_date":"2026-06-16T00:00:00"}
