{"id":"https://openalex.org/W2056169962","doi":"https://doi.org/10.1109/bigdata.2014.7004371","title":"Extending SPARQL with graph functions","display_name":"Extending SPARQL with graph functions","publication_year":2014,"publication_date":"2014-10-01","ids":{"openalex":"https://openalex.org/W2056169962","doi":"https://doi.org/10.1109/bigdata.2014.7004371","mag":"2056169962"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2014.7004371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2014.7004371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 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/A5055093845","display_name":"David Mizell","orcid":null},"institutions":[{"id":"https://openalex.org/I4210090485","display_name":"Crystal Research (United States)","ror":"https://ror.org/0001tya14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210090485"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"David Mizell","raw_affiliation_strings":["YarcData/Cray, Inc","YarcData / Cray, Inc"],"affiliations":[{"raw_affiliation_string":"YarcData/Cray, Inc","institution_ids":["https://openalex.org/I4210090485"]},{"raw_affiliation_string":"YarcData / Cray, Inc","institution_ids":["https://openalex.org/I4210090485"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029871169","display_name":"Kristyn Maschhoff","orcid":null},"institutions":[{"id":"https://openalex.org/I4210090485","display_name":"Crystal Research (United States)","ror":"https://ror.org/0001tya14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210090485"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kristyn J. Maschhoff","raw_affiliation_strings":["YarcData/Cray, Inc","YarcData / Cray, Inc"],"affiliations":[{"raw_affiliation_string":"YarcData/Cray, Inc","institution_ids":["https://openalex.org/I4210090485"]},{"raw_affiliation_string":"YarcData / Cray, Inc","institution_ids":["https://openalex.org/I4210090485"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070448591","display_name":"Steven P. Reinhardt","orcid":"https://orcid.org/0000-0003-4355-6693"},"institutions":[{"id":"https://openalex.org/I4210090485","display_name":"Crystal Research (United States)","ror":"https://ror.org/0001tya14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210090485"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Steven P. Reinhardt","raw_affiliation_strings":["YarcData/Cray, Inc","YarcData / Cray, Inc"],"affiliations":[{"raw_affiliation_string":"YarcData/Cray, Inc","institution_ids":["https://openalex.org/I4210090485"]},{"raw_affiliation_string":"YarcData / Cray, Inc","institution_ids":["https://openalex.org/I4210090485"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5055093845"],"corresponding_institution_ids":["https://openalex.org/I4210090485"],"apc_list":null,"apc_paid":null,"fwci":1.6361,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.87295548,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"46","last_page":"53"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9933000206947327,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9933000206947327,"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/T10720","display_name":"Complexity and Algorithms in Graphs","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9908000230789185,"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/betweenness-centrality","display_name":"Betweenness centrality","score":0.8348702192306519},{"id":"https://openalex.org/keywords/sparql","display_name":"SPARQL","score":0.804661750793457},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.696765124797821},{"id":"https://openalex.org/keywords/named-graph","display_name":"Named graph","score":0.5365806221961975},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5347959399223328},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5023760795593262},{"id":"https://openalex.org/keywords/graph-database","display_name":"Graph database","score":0.47176435589790344},{"id":"https://openalex.org/keywords/null-graph","display_name":"Null graph","score":0.44720259308815},{"id":"https://openalex.org/keywords/distance-hereditary-graph","display_name":"Distance-hereditary graph","score":0.42926815152168274},{"id":"https://openalex.org/keywords/centrality","display_name":"Centrality","score":0.38442638516426086},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.3422092795372009},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.2981436252593994},{"id":"https://openalex.org/keywords/graph-power","display_name":"Graph power","score":0.22935041785240173},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.22103658318519592},{"id":"https://openalex.org/keywords/voltage-graph","display_name":"Voltage graph","score":0.20813775062561035},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20014333724975586},{"id":"https://openalex.org/keywords/rdf","display_name":"RDF","score":0.16449695825576782},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.11589100956916809}],"concepts":[{"id":"https://openalex.org/C117045392","wikidata":"https://www.wikidata.org/wiki/Q4899215","display_name":"Betweenness centrality","level":3,"score":0.8348702192306519},{"id":"https://openalex.org/C41009113","wikidata":"https://www.wikidata.org/wiki/Q54871","display_name":"SPARQL","level":4,"score":0.804661750793457},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.696765124797821},{"id":"https://openalex.org/C110893760","wikidata":"https://www.wikidata.org/wiki/Q3115590","display_name":"Named graph","level":5,"score":0.5365806221961975},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5347959399223328},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5023760795593262},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.47176435589790344},{"id":"https://openalex.org/C17169500","wikidata":"https://www.wikidata.org/wiki/Q3033506","display_name":"Null graph","level":5,"score":0.44720259308815},{"id":"https://openalex.org/C147792647","wikidata":"https://www.wikidata.org/wiki/Q5282847","display_name":"Distance-hereditary graph","level":5,"score":0.42926815152168274},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.38442638516426086},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.3422092795372009},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2981436252593994},{"id":"https://openalex.org/C149530733","wikidata":"https://www.wikidata.org/wiki/Q5597091","display_name":"Graph power","level":4,"score":0.22935041785240173},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.22103658318519592},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.20813775062561035},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20014333724975586},{"id":"https://openalex.org/C147497476","wikidata":"https://www.wikidata.org/wiki/Q54872","display_name":"RDF","level":3,"score":0.16449695825576782},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.11589100956916809}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2014.7004371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2014.7004371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6499999761581421}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W40890042","https://openalex.org/W189202421","https://openalex.org/W347697680","https://openalex.org/W1524811847","https://openalex.org/W1535271440","https://openalex.org/W1555445537","https://openalex.org/W1854214752","https://openalex.org/W1971937094","https://openalex.org/W1972793074","https://openalex.org/W1978979183","https://openalex.org/W1989009626","https://openalex.org/W2019724001","https://openalex.org/W2106553832","https://openalex.org/W2110942501","https://openalex.org/W2114996745","https://openalex.org/W2123562143","https://openalex.org/W2134618506","https://openalex.org/W2141380216","https://openalex.org/W2154413134","https://openalex.org/W2182110407","https://openalex.org/W2184078222","https://openalex.org/W2295786194","https://openalex.org/W2411085520","https://openalex.org/W3099706321","https://openalex.org/W3144730817","https://openalex.org/W6607569353","https://openalex.org/W6697377598"],"related_works":["https://openalex.org/W2038821533","https://openalex.org/W1976467436","https://openalex.org/W76044956","https://openalex.org/W4298860421","https://openalex.org/W4385958747","https://openalex.org/W4362598466","https://openalex.org/W2786642168","https://openalex.org/W2951852920","https://openalex.org/W2784308500","https://openalex.org/W4302024884"],"abstract_inverted_index":{"Much":[0],"of":[1,23,40,46,65,114,163,182,196,248],"the":[2,38,49,144,152,194,237],"early":[3],"domain-specific":[4],"success":[5],"with":[6,11,90,155,175,255],"graph":[7,19,73,121,165,187,197,215,239],"analytics":[8],"has":[9],"been":[10],"algorithms":[12,95,122,269],"whose":[13,30],"results":[14,130,140],"are":[15],"based":[16],"on":[17,37],"global":[18],"structure.":[20],"An":[21],"example":[22],"such":[24,75],"an":[25,133,190],"algorithm":[26],"is":[27,245],"betweenness":[28,77],"centrality,":[29],"value":[31],"for":[32,123,222,230],"any":[33],"vertex":[34],"potentially":[35],"depends":[36],"number":[39],"shortest":[41,99],"paths":[42],"between":[43],"all":[44],"pairs":[45],"vertices":[47],"in":[48,193],"entire":[50],"graph.":[51],"YarcData's":[52],"Urika":[53,147],"<sup":[54],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[55],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">TM</sup>":[56],"customers":[57],"use":[58,206],"SPARQL's":[59],"graph-oriented":[60],"pattern-matching":[61],"capabilities,":[62],"but":[63],"many":[64],"them":[66],"also":[67],"require":[68],"a":[69,106,111,120,156,160,246,258],"capability":[70,158],"to":[71,84,119,132,213,241,270],"call":[72],"functions":[74,188,216,240,253],"as":[76,141],"centrality.":[78],"This":[79],"customer":[80],"feedback":[81],"led":[82],"us":[83],"combine":[85],"SPARQL":[86,107,135,153,256],"1.1's":[87],"query":[88,108,136],"capabilities":[89],"classical":[91],"and":[92,126,159,172,199,235],"emerging":[93],"graph-analytic":[94],"(e.g.,":[96],"community":[97],"detection,":[98],"path,":[100],"betweenness,":[101],"BadRank).":[102],"With":[103,143],"this":[104,176],"capability,":[105],"can":[109,218],"select":[110],"specific":[112,214],"subgraph":[113,118],"interest,":[115],"pass":[116,128],"that":[117,137,217],"deep":[124],"analysis,":[125],"then":[127],"those":[129,139,208,233],"back":[131],"enclosing":[134],"post-processes":[138],"needed.":[142],"Summer":[145],"2014":[146],"release,":[148,178],"we":[149],"have":[150],"extended":[151],"implementation":[154],"graph-function":[157],"small":[161],"set":[162],"built-in":[164],"functions.":[166],"We":[167],"describe":[168],"our":[169,173],"design":[170],"approach":[171],"experiences":[174],"first":[177],"including":[179],"anecdotal":[180],"evidence":[181],"dramatically":[183],"higher":[184,224],"performance.":[185],"Built-in":[186],"represent":[189],"important":[191],"step":[192],"maturation":[195],"analysis":[198],"SPARQL.":[200,231],"As":[201],"common":[202],"motifs":[203,209,234],"emerge":[204],"from":[205],"cases,":[207],"may":[210,266],"be":[211,219,228],"mapped":[212],"highly":[220],"tuned":[221],"much":[223],"performance":[225],"than":[226],"will":[227],"possible":[229],"Identifying":[232],"developing":[236],"underlying":[238],"accelerate":[242],"their":[243,268],"execution":[244],"topic":[247],"intense":[249],"effort":[250],"industry-wide.":[251],"Graph":[252],"merged":[254],"provide":[257],"new":[259],"mechanism":[260],"by":[261],"which":[262],"third-party":[263],"graph-algorithm":[264],"developers":[265],"expose":[267],"widespread":[271],"use.":[272]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
