A Two-Phase Method for Optimization of the SPARQL Query
With a rapid growth in the available resource description framework (RDF) data from disparate domains, the SPARQL query processing with graph structures has become increasingly important. In this pursuit, we designed a two-phase SPARQL query optimization method to process the SPARQL query. The struc...
Gespeichert in:
| Veröffentlicht in: | Journal of sensors 2022-08, Vol.2022, p.1-12 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 12 |
|---|---|
| container_issue | |
| container_start_page | 1 |
| container_title | Journal of sensors |
| container_volume | 2022 |
| creator | Lin, Xiaoqing Jiang, Dongyang |
| description | With a rapid growth in the available resource description framework (RDF) data from disparate domains, the SPARQL query processing with graph structures has become increasingly important. In this pursuit, we designed a two-phase SPARQL query optimization method to process the SPARQL query. The structural characteristics of RDF data graphs, predicate path sequence indices (PPS-indices), were used to efficiently prune the search space, which captured the inherent features of the RDF data graphs, while the database is updated. Our storage model was based on a relational database. Compared to a baseline solution, the proposed method effectively reduced the cardinalities of the intermediate results during the query processing, and at least an order of magnitude improvement is achieved in filtering performance, thereby improving the efficiency of the query execution. |
| doi_str_mv | 10.1155/2022/4624856 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2709597224</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2709597224</sourcerecordid><originalsourceid>FETCH-LOGICAL-c404t-100e36d04d678707dee3569afbfccfd034bcc7732c9a4f80c118f28e2264d7a93</originalsourceid><addsrcrecordid>eNp90EtLAzEQwPEgCtbqzQ8Q8KhrJ49NssdSfEGlrVbwFtI82C22qcmWUj-9LS0ePc0cfszAH6FrAveElGWPAqU9LihXpThBHSKULCQV6vRvLz_P0UXOcwDBJGMdJPt4uonFuDbZ41ff1tHhEBMerdpm0fyYtolLHANua4_fx_23yRBP1j5tL9FZMF_ZXx1nF308PkwHz8Vw9PQy6A8Ly4G3BQHwTDjgTkglQTrvWSkqE2bB2uCA8Zm1UjJqK8ODAkuIClR5SgV30lSsi24Od1cpfq99bvU8rtNy91JTCVVZSUr5Tt0dlE0x5-SDXqVmYdJWE9D7NHqfRh_T7PjtgdfN0plN87_-BZ35YKw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2709597224</pqid></control><display><type>article</type><title>A Two-Phase Method for Optimization of the SPARQL Query</title><source>EZB-FREE-00999 freely available EZB journals</source><source>Wiley Online Library (Open Access Collection)</source><source>Alma/SFX Local Collection</source><creator>Lin, Xiaoqing ; Jiang, Dongyang</creator><contributor>Li, Yuan ; Yuan Li</contributor><creatorcontrib>Lin, Xiaoqing ; Jiang, Dongyang ; Li, Yuan ; Yuan Li</creatorcontrib><description>With a rapid growth in the available resource description framework (RDF) data from disparate domains, the SPARQL query processing with graph structures has become increasingly important. In this pursuit, we designed a two-phase SPARQL query optimization method to process the SPARQL query. The structural characteristics of RDF data graphs, predicate path sequence indices (PPS-indices), were used to efficiently prune the search space, which captured the inherent features of the RDF data graphs, while the database is updated. Our storage model was based on a relational database. Compared to a baseline solution, the proposed method effectively reduced the cardinalities of the intermediate results during the query processing, and at least an order of magnitude improvement is achieved in filtering performance, thereby improving the efficiency of the query execution.</description><identifier>ISSN: 1687-725X</identifier><identifier>EISSN: 1687-7268</identifier><identifier>DOI: 10.1155/2022/4624856</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Data mining ; Graphs ; Ontology ; Optimization ; Optimization techniques ; Queries ; Query processing ; Relational data bases ; Semantic web ; Semantics ; Variables</subject><ispartof>Journal of sensors, 2022-08, Vol.2022, p.1-12</ispartof><rights>Copyright © 2022 Xiaoqing Lin and Dongyang Jiang.</rights><rights>Copyright © 2022 Xiaoqing Lin and Dongyang Jiang. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c404t-100e36d04d678707dee3569afbfccfd034bcc7732c9a4f80c118f28e2264d7a93</citedby><cites>FETCH-LOGICAL-c404t-100e36d04d678707dee3569afbfccfd034bcc7732c9a4f80c118f28e2264d7a93</cites><orcidid>0000-0003-2966-6810 ; 0000-0003-3613-3756</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Li, Yuan</contributor><contributor>Yuan Li</contributor><creatorcontrib>Lin, Xiaoqing</creatorcontrib><creatorcontrib>Jiang, Dongyang</creatorcontrib><title>A Two-Phase Method for Optimization of the SPARQL Query</title><title>Journal of sensors</title><description>With a rapid growth in the available resource description framework (RDF) data from disparate domains, the SPARQL query processing with graph structures has become increasingly important. In this pursuit, we designed a two-phase SPARQL query optimization method to process the SPARQL query. The structural characteristics of RDF data graphs, predicate path sequence indices (PPS-indices), were used to efficiently prune the search space, which captured the inherent features of the RDF data graphs, while the database is updated. Our storage model was based on a relational database. Compared to a baseline solution, the proposed method effectively reduced the cardinalities of the intermediate results during the query processing, and at least an order of magnitude improvement is achieved in filtering performance, thereby improving the efficiency of the query execution.</description><subject>Data mining</subject><subject>Graphs</subject><subject>Ontology</subject><subject>Optimization</subject><subject>Optimization techniques</subject><subject>Queries</subject><subject>Query processing</subject><subject>Relational data bases</subject><subject>Semantic web</subject><subject>Semantics</subject><subject>Variables</subject><issn>1687-725X</issn><issn>1687-7268</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp90EtLAzEQwPEgCtbqzQ8Q8KhrJ49NssdSfEGlrVbwFtI82C22qcmWUj-9LS0ePc0cfszAH6FrAveElGWPAqU9LihXpThBHSKULCQV6vRvLz_P0UXOcwDBJGMdJPt4uonFuDbZ41ff1tHhEBMerdpm0fyYtolLHANua4_fx_23yRBP1j5tL9FZMF_ZXx1nF308PkwHz8Vw9PQy6A8Ly4G3BQHwTDjgTkglQTrvWSkqE2bB2uCA8Zm1UjJqK8ODAkuIClR5SgV30lSsi24Od1cpfq99bvU8rtNy91JTCVVZSUr5Tt0dlE0x5-SDXqVmYdJWE9D7NHqfRh_T7PjtgdfN0plN87_-BZ35YKw</recordid><startdate>20220825</startdate><enddate>20220825</enddate><creator>Lin, Xiaoqing</creator><creator>Jiang, Dongyang</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SP</scope><scope>7U5</scope><scope>7XB</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KB.</scope><scope>L6V</scope><scope>L7M</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0003-2966-6810</orcidid><orcidid>https://orcid.org/0000-0003-3613-3756</orcidid></search><sort><creationdate>20220825</creationdate><title>A Two-Phase Method for Optimization of the SPARQL Query</title><author>Lin, Xiaoqing ; Jiang, Dongyang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c404t-100e36d04d678707dee3569afbfccfd034bcc7732c9a4f80c118f28e2264d7a93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Data mining</topic><topic>Graphs</topic><topic>Ontology</topic><topic>Optimization</topic><topic>Optimization techniques</topic><topic>Queries</topic><topic>Query processing</topic><topic>Relational data bases</topic><topic>Semantic web</topic><topic>Semantics</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Xiaoqing</creatorcontrib><creatorcontrib>Jiang, Dongyang</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computing Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of sensors</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Xiaoqing</au><au>Jiang, Dongyang</au><au>Li, Yuan</au><au>Yuan Li</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Two-Phase Method for Optimization of the SPARQL Query</atitle><jtitle>Journal of sensors</jtitle><date>2022-08-25</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>12</epage><pages>1-12</pages><issn>1687-725X</issn><eissn>1687-7268</eissn><abstract>With a rapid growth in the available resource description framework (RDF) data from disparate domains, the SPARQL query processing with graph structures has become increasingly important. In this pursuit, we designed a two-phase SPARQL query optimization method to process the SPARQL query. The structural characteristics of RDF data graphs, predicate path sequence indices (PPS-indices), were used to efficiently prune the search space, which captured the inherent features of the RDF data graphs, while the database is updated. Our storage model was based on a relational database. Compared to a baseline solution, the proposed method effectively reduced the cardinalities of the intermediate results during the query processing, and at least an order of magnitude improvement is achieved in filtering performance, thereby improving the efficiency of the query execution.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2022/4624856</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-2966-6810</orcidid><orcidid>https://orcid.org/0000-0003-3613-3756</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1687-725X |
| ispartof | Journal of sensors, 2022-08, Vol.2022, p.1-12 |
| issn | 1687-725X 1687-7268 |
| language | eng |
| recordid | cdi_proquest_journals_2709597224 |
| source | EZB-FREE-00999 freely available EZB journals; Wiley Online Library (Open Access Collection); Alma/SFX Local Collection |
| subjects | Data mining Graphs Ontology Optimization Optimization techniques Queries Query processing Relational data bases Semantic web Semantics Variables |
| title | A Two-Phase Method for Optimization of the SPARQL Query |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-30T21%3A54%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Two-Phase%20Method%20for%20Optimization%20of%20the%20SPARQL%20Query&rft.jtitle=Journal%20of%20sensors&rft.au=Lin,%20Xiaoqing&rft.date=2022-08-25&rft.volume=2022&rft.spage=1&rft.epage=12&rft.pages=1-12&rft.issn=1687-725X&rft.eissn=1687-7268&rft_id=info:doi/10.1155/2022/4624856&rft_dat=%3Cproquest_cross%3E2709597224%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2709597224&rft_id=info:pmid/&rfr_iscdi=true |