{"id":"https://openalex.org/W2952276741","doi":"https://doi.org/10.1145/3327964.3328496","title":"Experiences with Implementing Landmark Embedding in Neo4j","display_name":"Experiences with Implementing Landmark Embedding in Neo4j","publication_year":2019,"publication_date":"2019-06-19","ids":{"openalex":"https://openalex.org/W2952276741","doi":"https://doi.org/10.1145/3327964.3328496","mag":"2952276741"},"language":"en","primary_location":{"id":"doi:10.1145/3327964.3328496","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3327964.3328496","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences &amp; Systems (GRADES) and Network Data Analytics (NDA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://kops.uni-konstanz.de/server/api/core/bitstreams/4fb2642f-fa5c-4b81-9566-b67d6ce1e1d0/content","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032754500","display_name":"Manuel Hotz","orcid":null},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Manuel Hotz","raw_affiliation_strings":["University of Konstanz, Konstanz, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Konstanz, Konstanz, Germany","institution_ids":["https://openalex.org/I189712700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000400301","display_name":"Theodoros Chondrogiannis","orcid":"https://orcid.org/0000-0002-9623-9133"},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Theodoros Chondrogiannis","raw_affiliation_strings":["University of Konstanz, Konstanz, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Konstanz, Konstanz, Germany","institution_ids":["https://openalex.org/I189712700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028355735","display_name":"Leonard W\u00f6rteler","orcid":"https://orcid.org/0009-0003-0532-0505"},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Leonard W\u00f6rteler","raw_affiliation_strings":["University of Konstanz, Konstanz, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Konstanz, Konstanz, Germany","institution_ids":["https://openalex.org/I189712700"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000695235","display_name":"Michael Grossniklaus","orcid":"https://orcid.org/0000-0003-1609-2221"},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Grossniklaus","raw_affiliation_strings":["University of Konstanz, Konstanz, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Konstanz, Konstanz, Germany","institution_ids":["https://openalex.org/I189712700"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I189712700"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9997000098228455,"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":0.9997000098228455,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9980000257492065,"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"}},{"id":"https://openalex.org/T10317","display_name":"Advanced Database Systems and Queries","score":0.9957000017166138,"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/reachability","display_name":"Reachability","score":0.8580731749534607},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.8553240895271301},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8284205198287964},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.699962317943573},{"id":"https://openalex.org/keywords/shortest-path-problem","display_name":"Shortest path problem","score":0.6497802734375},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6046107411384583},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5961383581161499},{"id":"https://openalex.org/keywords/graph-database","display_name":"Graph database","score":0.5929011702537537},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5364931225776672},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.49662429094314575},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49204346537590027},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4640205502510071},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.45431894063949585},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2237541377544403},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19784069061279297},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08975768089294434}],"concepts":[{"id":"https://openalex.org/C136643341","wikidata":"https://www.wikidata.org/wiki/Q1361526","display_name":"Reachability","level":2,"score":0.8580731749534607},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.8553240895271301},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8284205198287964},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.699962317943573},{"id":"https://openalex.org/C22590252","wikidata":"https://www.wikidata.org/wiki/Q1058754","display_name":"Shortest path problem","level":3,"score":0.6497802734375},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6046107411384583},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5961383581161499},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.5929011702537537},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5364931225776672},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.49662429094314575},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49204346537590027},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4640205502510071},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.45431894063949585},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2237541377544403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19784069061279297},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08975768089294434},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3327964.3328496","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3327964.3328496","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences &amp; Systems (GRADES) and Network Data Analytics (NDA)","raw_type":"proceedings-article"},{"id":"pmh:oai:kops.uni-konstanz.de:123456789/46242","is_oa":true,"landing_page_url":"http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1dsppp9sa57ng0","pdf_url":"https://kops.uni-konstanz.de/server/api/core/bitstreams/4fb2642f-fa5c-4b81-9566-b67d6ce1e1d0/content","source":{"id":"https://openalex.org/S4306401487","display_name":"KOPS (University of Konstanz)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I189712700","host_organization_name":"University of Konstanz","host_organization_lineage":["https://openalex.org/I189712700"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) - GRADES-NDA'19. New York: ACM Press, 2019, 7. ISBN 978-1-4503-6789-9. Verf\u00fcgbar unter: doi: 10.1145/3327964.3328496","raw_type":"doc-type:conferenceObject"}],"best_oa_location":{"id":"pmh:oai:kops.uni-konstanz.de:123456789/46242","is_oa":true,"landing_page_url":"http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1dsppp9sa57ng0","pdf_url":"https://kops.uni-konstanz.de/server/api/core/bitstreams/4fb2642f-fa5c-4b81-9566-b67d6ce1e1d0/content","source":{"id":"https://openalex.org/S4306401487","display_name":"KOPS (University of Konstanz)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I189712700","host_organization_name":"University of Konstanz","host_organization_lineage":["https://openalex.org/I189712700"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) - GRADES-NDA'19. New York: ACM Press, 2019, 7. ISBN 978-1-4503-6789-9. Verf\u00fcgbar unter: doi: 10.1145/3327964.3328496","raw_type":"doc-type:conferenceObject"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G8478256384","display_name":null,"funder_award_id":"GR 4497/2","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2952276741.pdf","grobid_xml":"https://content.openalex.org/works/W2952276741.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W599597715","https://openalex.org/W1512819151","https://openalex.org/W1810931192","https://openalex.org/W1969483458","https://openalex.org/W2014889099","https://openalex.org/W2019003829","https://openalex.org/W2022704179","https://openalex.org/W2023015232","https://openalex.org/W2052703084","https://openalex.org/W2098636939","https://openalex.org/W2117897639","https://openalex.org/W2119286127","https://openalex.org/W2140271346","https://openalex.org/W2142517301","https://openalex.org/W2146591355","https://openalex.org/W2152651944","https://openalex.org/W2169528473","https://openalex.org/W2172107427","https://openalex.org/W2293891875","https://openalex.org/W2295129968","https://openalex.org/W2296659056","https://openalex.org/W2487614805","https://openalex.org/W2489919717","https://openalex.org/W2553211886","https://openalex.org/W2604146997","https://openalex.org/W2605123839","https://openalex.org/W2613189058","https://openalex.org/W2781191210","https://openalex.org/W4301500808","https://openalex.org/W6747193487"],"related_works":["https://openalex.org/W2056853153","https://openalex.org/W2057559274","https://openalex.org/W2005087563","https://openalex.org/W2378111931","https://openalex.org/W2052388267","https://openalex.org/W2950647290","https://openalex.org/W1968481813","https://openalex.org/W2620829895","https://openalex.org/W2356918560","https://openalex.org/W2128644323"],"abstract_inverted_index":{"Reachability,":[0],"distance,":[1,79],"and":[2,21,48,80,94,112,135],"shortest":[3,81],"path":[4,82],"queries":[5,83],"are":[6],"fundamental":[7],"operations":[8],"in":[9,19],"the":[10,33,38,74,85,99,105,128,131,136,139],"field":[11],"of":[12,35,40,61,77,107,138,142,145],"graph":[13,44,64],"data":[14],"management":[15,46],"with":[16],"various":[17,25],"applications":[18],"research":[20],"industry.":[22],"However,":[23],"while":[24],"preprocessing-based":[26],"methods":[27,42],"have":[28],"been":[29,52],"proposed":[30],"to":[31,72],"optimize":[32],"computation":[34,76],"such":[36],"queries,":[37],"integration":[39],"existing":[41],"into":[43],"database":[45],"systems":[47],"processing":[49,133],"frameworks":[50],"has":[51],"limited.":[53],"In":[54],"this":[55],"paper,":[56],"we":[57,116],"present":[58],"an":[59,118],"implementation":[60],"a":[62],"static":[63],"index":[65,147],"that":[66],"employs":[67],"landmark":[68,101,109],"embedding":[69],"for":[70,91,97],"Neo4j,":[71],"enable":[73],"index-based":[75],"reachability,":[78],"on":[84],"database.":[86],"We":[87,126],"explore":[88],"different":[89,95,143],"strategies":[90],"selecting":[92],"landmarks":[93],"schemes":[96],"storing":[98],"precomputed":[100],"distances.":[102],"To":[103],"evaluate":[104],"efficiency":[106],"each":[108,113],"selection":[110],"strategy":[111],"storage":[114],"scheme,":[115],"conduct":[117],"experimental":[119],"evaluation":[120],"using":[121],"four":[122],"real-world":[123],"network":[124],"datasets.":[125],"measure":[127],"preprocessing":[129],"cost,":[130],"query":[132],"time,":[134],"accuracy":[137],"distance":[140],"estimation":[141],"configurations":[144],"our":[146],"structure.":[148]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
