{"id":"https://openalex.org/W2197925542","doi":"https://doi.org/10.1109/bigdata.2015.7363789","title":"Quadtree-based lightweight data compression for large-scale geospatial rasters on multi-core CPUs","display_name":"Quadtree-based lightweight data compression for large-scale geospatial rasters on multi-core CPUs","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2197925542","doi":"https://doi.org/10.1109/bigdata.2015.7363789","mag":"2197925542"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2015.7363789","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363789","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big 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/A5101482691","display_name":"Jianting Zhang","orcid":"https://orcid.org/0000-0002-0161-9716"},"institutions":[{"id":"https://openalex.org/I125687163","display_name":"City College of New York","ror":"https://ror.org/00wmhkr98","country_code":"US","type":"education","lineage":["https://openalex.org/I125687163"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jianting Zhang","raw_affiliation_strings":["Dept. of Computer Science, The City College of New York, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, The City College of New York, New York, NY, USA","institution_ids":["https://openalex.org/I125687163"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113803154","display_name":"Simin You","orcid":null},"institutions":[{"id":"https://openalex.org/I121847817","display_name":"The Graduate Center, CUNY","ror":"https://ror.org/00awd9g61","country_code":"US","type":"education","lineage":["https://openalex.org/I121847817"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Simin You","raw_affiliation_strings":["Dept. of Computer Science, CUNY Graduate Center, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, CUNY Graduate Center, New York, NY, USA","institution_ids":["https://openalex.org/I121847817"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071062190","display_name":"Le Gruenwald","orcid":"https://orcid.org/0000-0002-5245-4747"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Le Gruenwald","raw_affiliation_strings":["Dept. of Computer Science, The University of Oklahoma, Norman, OK, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, The University of Oklahoma, Norman, OK, USA","institution_ids":["https://openalex.org/I8692664"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101482691"],"corresponding_institution_ids":["https://openalex.org/I125687163"],"apc_list":null,"apc_paid":null,"fwci":2.5887,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.91617407,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"57","issue":null,"first_page":"478","last_page":"484"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11269","display_name":"Algorithms and Data Compression","score":0.9995999932289124,"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":0.9995999932289124,"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/T11321","display_name":"Error Correcting Code Techniques","score":0.9969000220298767,"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"}},{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9959999918937683,"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/computer-science","display_name":"Computer science","score":0.7715167999267578},{"id":"https://openalex.org/keywords/bitmap","display_name":"Bitmap","score":0.6674662828445435},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.6162078380584717},{"id":"https://openalex.org/keywords/compression-ratio","display_name":"Compression ratio","score":0.6056095361709595},{"id":"https://openalex.org/keywords/raster-graphics","display_name":"Raster graphics","score":0.5550791621208191},{"id":"https://openalex.org/keywords/quadtree","display_name":"Quadtree","score":0.543041467666626},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.5190951824188232},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.4547232687473297},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.32204481959342957},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.24686121940612793},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.2153177261352539},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.18268895149230957},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08194291591644287}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7715167999267578},{"id":"https://openalex.org/C3115412","wikidata":"https://www.wikidata.org/wiki/Q1194708","display_name":"Bitmap","level":2,"score":0.6674662828445435},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.6162078380584717},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.6056095361709595},{"id":"https://openalex.org/C181844469","wikidata":"https://www.wikidata.org/wiki/Q182270","display_name":"Raster graphics","level":2,"score":0.5550791621208191},{"id":"https://openalex.org/C151416825","wikidata":"https://www.wikidata.org/wiki/Q934791","display_name":"Quadtree","level":2,"score":0.543041467666626},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.5190951824188232},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.4547232687473297},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.32204481959342957},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.24686121940612793},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2153177261352539},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18268895149230957},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08194291591644287},{"id":"https://openalex.org/C511840579","wikidata":"https://www.wikidata.org/wiki/Q12757","display_name":"Internal combustion engine","level":2,"score":0.0},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2015.7363789","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363789","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2034796110","https://openalex.org/W2046288017","https://openalex.org/W2047994284","https://openalex.org/W2051266380","https://openalex.org/W2078528030","https://openalex.org/W2155729921","https://openalex.org/W2592447481","https://openalex.org/W2725179571"],"related_works":["https://openalex.org/W2381109224","https://openalex.org/W2595958443","https://openalex.org/W3098714126","https://openalex.org/W2301398378","https://openalex.org/W2183135427","https://openalex.org/W2035497054","https://openalex.org/W3080614128","https://openalex.org/W4381744720","https://openalex.org/W2088378984","https://openalex.org/W4200061735"],"abstract_inverted_index":{"Huge":[0],"amounts":[1],"of":[2,62,103],"geospatial":[3,49],"rasters,":[4],"such":[5],"as":[6,65],"remotely":[7],"sensed":[8],"imagery":[9],"and":[10,23,45,71,81,134,160,195],"environmental":[11],"modeling":[12],"output,":[13],"are":[14,96],"being":[15],"generated":[16],"with":[17,116,174,185,190],"increasingly":[18],"finer":[19],"spatial,":[20],"temporal,":[21],"spectral":[22],"thematic":[24],"resolutions.":[25],"In":[26],"this":[27],"study,":[28],"we":[29],"aim":[30],"at":[31],"developing":[32],"a":[33,142],"lightweight":[34,82],"lossless":[35],"data":[36,115],"compression":[37,44,133,149,159,194],"technique":[38,58,76,126,152,182,189],"that":[39,123],"balances":[40],"the":[41,60,104,170,186],"performance":[42],"between":[43],"decompression":[46,138,164,196],"for":[47,89,132,137,158,163],"large-scale":[48],"rasters.":[50],"Our":[51,151,181],"Bitplane":[52],"bitmap":[53],"Quadtree":[54],"(or":[55],"BQ-Tree)":[56],"based":[57],"encodes":[59],"bitmaps":[61],"raster":[63,119],"bitplanes":[64],"compact":[66],"quadtrees":[67],"which":[68],"can":[69],"compress":[70],"index":[72],"rasters":[73,102],"simultaneously.":[74],"The":[75],"is":[77,127],"simple":[78],"by":[79,83],"design":[80],"implementations.":[84],"Except":[85],"computing":[86],"Z-order":[87],"codes":[88],"cache":[90],"efficiency,":[91],"only":[92],"bit":[93],"level":[94],"operations":[95],"required.":[97],"Extensive":[98],"experiments":[99],"using":[100,141,165],"36":[101],"NASA":[105],"Shuttle":[106],"Range":[107],"Topography":[108],"Mission":[109],"(SRTM)":[110],"30":[111],"meter":[112],"resolution":[113],"elevation":[114],"20":[117],"billion":[118],"cells":[120],"have":[121],"shown":[122],"our":[124],"BQ-Tree":[125],"more":[128],"than":[129,139],"4X":[130,161],"faster":[131,136],"36%":[135],"zlib":[140],"single":[143],"CPU":[144,167],"core":[145],"while":[146],"achieving":[147],"similar":[148],"ratios.":[150],"further":[153],"has":[154],"achieved":[155],"10-13X":[156],"speedups":[157,162],"16":[166],"cores":[168],"on":[169],"experiment":[171],"machine":[172],"equipped":[173],"dual":[175],"Intel":[176],"Xeon":[177],"8-core":[178],"E5-2650V2":[179],"CPUs.":[180],"compares":[183],"favorably":[184],"best":[187],"known":[188],"respect":[191],"to":[192],"both":[193],"throughputs.":[197]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
