Parallel and scalable processing of spatio-temporal RDF queries using Spark
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| 出版年: | GeoInformatica vol. 25, no. 4 (Oct 2021), p. 623 |
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| 第一著者: | |
| その他の著者: | , , |
| 出版事項: |
Springer Nature B.V.
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text - PDF |
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| 045 | 2 | |b d20211001 |b d20211031 | |
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| 100 | 1 | |a Nikitopoulos Panagiotis |u University of Piraeus, Department of Digital Systems, School of Information and Communication Technologies, Piraeus, Greece (GRID:grid.4463.5) (ISNI:0000 0001 0558 8585) | |
| 245 | 1 | |a Parallel and scalable processing of spatio-temporal RDF queries using Spark | |
| 260 | |b Springer Nature B.V. |c Oct 2021 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The ever-increasing size of data emanating from mobile devices and sensors, dictates the use of distributed systems for storing and querying these data. Typically, such data sources provide some spatio-temporal information, alongside other useful data. The RDF data model can be used to interlink and exchange data originating from heterogeneous sources in a uniform manner. For example, consider the case where vessels report their spatio-temporal position, on a regular basis, by using various surveillance systems. In this scenario, a user might be interested to know which vessels were moving in a specific area for a given temporal range. In this paper, we address the problem of efficiently storing and querying spatio-temporal RDF data in parallel. We specifically study the case of SPARQL queries with spatio-temporal constraints, by proposing the DiStRDF system, which is comprised of a Storage and a Processing Layer. The DiStRDF Storage Layer is responsible for efficiently storing large amount of historical spatio-temporal RDF data of moving objects. On top of it, we devise our DiStRDF Processing Layer, which parses a SPARQL query and produces corresponding logical and physical execution plans. We use Spark, a well-known distributed in-memory processing framework, as the underlying processing engine. Our experimental evaluation, on real data from both aviation and maritime domains, demonstrates the efficiency of our DiStRDF system, when using various spatio-temporal range constraints. | |
| 653 | |a Information processing | ||
| 653 | |a Data exchange | ||
| 653 | |a Storage | ||
| 653 | |a Surveillance systems | ||
| 653 | |a Electronic devices | ||
| 653 | |a Query processing | ||
| 653 | |a Distributed memory | ||
| 653 | |a Computer networks | ||
| 653 | |a Aircraft | ||
| 653 | |a Datasets | ||
| 653 | |a Aviation | ||
| 653 | |a Resource Description Framework-RDF | ||
| 653 | |a Surveillance | ||
| 653 | |a Queries | ||
| 653 | |a Data compression | ||
| 653 | |a Efficiency | ||
| 653 | |a Distributed processing | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Vlachou Akrivi |u University of Piraeus, Department of Digital Systems, School of Information and Communication Technologies, Piraeus, Greece (GRID:grid.4463.5) (ISNI:0000 0001 0558 8585) | |
| 700 | 1 | |a Doulkeridis Christos |u University of Piraeus, Department of Digital Systems, School of Information and Communication Technologies, Piraeus, Greece (GRID:grid.4463.5) (ISNI:0000 0001 0558 8585) | |
| 700 | 1 | |a Vouros, George A |u University of Piraeus, Department of Digital Systems, School of Information and Communication Technologies, Piraeus, Greece (GRID:grid.4463.5) (ISNI:0000 0001 0558 8585) | |
| 773 | 0 | |t GeoInformatica |g vol. 25, no. 4 (Oct 2021), p. 623 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2589634141/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2589634141/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |