{"id":"https://openalex.org/W2982740562","doi":"https://doi.org/10.1145/3347146.3359098","title":"Simple Distances for Trajectories via Landmarks","display_name":"Simple Distances for Trajectories via Landmarks","publication_year":2019,"publication_date":"2019-11-05","ids":{"openalex":"https://openalex.org/W2982740562","doi":"https://doi.org/10.1145/3347146.3359098","mag":"2982740562"},"language":"en","primary_location":{"id":"doi:10.1145/3347146.3359098","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3347146.3359098","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3347146.3359098","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3347146.3359098","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017619650","display_name":"Jeff M. Phillips","orcid":"https://orcid.org/0000-0003-1169-2965"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jeff M. Phillips","raw_affiliation_strings":["School of Computing, University of Utah., Salt Lake City, Utah, USA"],"affiliations":[{"raw_affiliation_string":"School of Computing, University of Utah., Salt Lake City, Utah, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031541199","display_name":"Pingfan Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pingfan Tang","raw_affiliation_strings":["School of Computing, University of Utah., Salt Lake City, Utah, USA"],"affiliations":[{"raw_affiliation_string":"School of Computing, University of Utah., Salt Lake City, Utah, USA","institution_ids":["https://openalex.org/I223532165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017619650"],"corresponding_institution_ids":["https://openalex.org/I223532165"],"apc_list":null,"apc_paid":null,"fwci":0.6679,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.69750456,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"468","last_page":"471"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":1.0,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9891999959945679,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7330662608146667},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.7097451686859131},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6581634879112244},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.6293213367462158},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6122695803642273},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6026929616928101},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5737677216529846},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.5332901477813721},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5266770720481873},{"id":"https://openalex.org/keywords/euclidean-space","display_name":"Euclidean space","score":0.4842793345451355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4433450400829315},{"id":"https://openalex.org/keywords/euclidean-distance-matrix","display_name":"Euclidean distance matrix","score":0.43945038318634033},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4336652457714081},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.42712438106536865},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33767688274383545},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33393603563308716},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24695679545402527},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13678225874900818}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7330662608146667},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.7097451686859131},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6581634879112244},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.6293213367462158},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6122695803642273},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6026929616928101},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5737677216529846},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.5332901477813721},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5266770720481873},{"id":"https://openalex.org/C186450821","wikidata":"https://www.wikidata.org/wiki/Q17295","display_name":"Euclidean space","level":2,"score":0.4842793345451355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4433450400829315},{"id":"https://openalex.org/C171558263","wikidata":"https://www.wikidata.org/wiki/Q5406122","display_name":"Euclidean distance matrix","level":3,"score":0.43945038318634033},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4336652457714081},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.42712438106536865},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33767688274383545},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33393603563308716},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24695679545402527},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13678225874900818},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3347146.3359098","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3347146.3359098","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3347146.3359098","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3347146.3359098","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3347146.3359098","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3347146.3359098","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.800000011920929,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G2515598214","display_name":null,"funder_award_id":"1443046","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3626920041","display_name":"CAREER: Foundations for Geometric Analysis of Noisy Data","funder_award_id":"1350888","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3775080162","display_name":null,"funder_award_id":"CNS-1514520","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6237683444","display_name":null,"funder_award_id":"1816149","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7510318212","display_name":null,"funder_award_id":"IIS-1816149","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7571944083","display_name":null,"funder_award_id":"NSF CCF-1350888","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8035954285","display_name":"TWC: Medium: Collaborative: Seal: Secure Engine for AnaLytics - From Secure Similarity Search to Secure Data Analytics","funder_award_id":"1514520","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8587161528","display_name":null,"funder_award_id":"ACI-1443046","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8698182252","display_name":null,"funder_award_id":"1350888,1443046,1514520,1564287,1816149","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8942888148","display_name":null,"funder_award_id":"1564287","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8988480673","display_name":null,"funder_award_id":"CCF-1350888","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/F4320334164","display_name":"Simons Institute for the Theory of Computing, University of California Berkeley","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2982740562.pdf","grobid_xml":"https://content.openalex.org/works/W2982740562.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1534707121","https://openalex.org/W1864972570","https://openalex.org/W2076014932","https://openalex.org/W2106274194","https://openalex.org/W2118371392","https://openalex.org/W2147880780","https://openalex.org/W2151263703","https://openalex.org/W2171944046","https://openalex.org/W2216025085","https://openalex.org/W2266931272","https://openalex.org/W2472323202","https://openalex.org/W2751600782","https://openalex.org/W2799180173","https://openalex.org/W2901104719","https://openalex.org/W2955779057"],"related_works":["https://openalex.org/W2148008870","https://openalex.org/W2381195555","https://openalex.org/W2368606575","https://openalex.org/W4246757943","https://openalex.org/W4294328694","https://openalex.org/W2908582041","https://openalex.org/W2132753198","https://openalex.org/W2097149198","https://openalex.org/W3171568022","https://openalex.org/W2105228010"],"abstract_inverted_index":{"We":[0,96],"develop":[1],"a":[2,14,26],"new":[3],"class":[4],"of":[5,16,83],"distances":[6,19,86,106],"for":[7],"trajectories,":[8,41],"based":[9],"on":[10],"the":[11,72],"distance":[12],"to":[13,25,31],"set":[15],"landmarks.":[17],"These":[18,85],"easily":[20],"and":[21,33,44,61,99],"interpretably":[22],"map":[23],"objects":[24],"Euclidean":[27],"space,":[28],"are":[29,107],"simple":[30,101],"compute,":[32],"perform":[34],"well":[35],"in":[36,45,58,76,93],"data":[37],"analysis":[38],"tasks.":[39],"For":[40],"they":[42],"match":[43],"some":[46],"cases":[47],"significantly":[48],"out-perform":[49,71],"all":[50],"state-of-the-art":[51],"other":[52],"metrics,":[53],"can":[54],"effortlessly":[55],"be":[56],"used":[57],"k-means":[59],"clustering,":[60],"directly":[62],"plugged":[63],"into":[64],"approximate":[65],"nearest":[66],"neighbor":[67],"approaches":[68],"which":[69,104],"immediately":[70],"best":[73],"recent":[74],"advances":[75],"trajectory":[77,94],"similarity":[78],"search":[79],"by":[80],"several":[81],"orders":[82],"magnitude.":[84],"do":[87],"not":[88],"require":[89],"complicated":[90],"alignment":[91],"(common":[92],"case).":[95],"show":[97],"reasonable":[98],"often":[100],"conditions":[102],"under":[103],"these":[105],"metrics.":[108]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}