Query Processing over Large RDF using SPARQL in Big Data

Khodke, P. A.; Lawange, Saurabh; Bhagat, Amol P.; Dongre, Kiran; Ingole, Chetan

doi:10.1145/2905055.2905124

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…As a result, this system is scalable and efficient enough to handle billions of RDF triples with ease. The authors in [51] include three layers of MDA (Model Driven Architectures) in their paper: the CIM (computational dependent model), the PIM (platform independent model), and the PSM (platform specific model) (platform specific model). These layers are then converted into a Big Data architecture, which entails identifying a set of predefined parameters.…”

Section: Semantics For Big Data Acquisitionmentioning

confidence: 99%

Towards the 5th Industrial Revolution: A literature review and a framework for Process Optimization Based on Big Data Analytics and Semantics

Mourtzis¹

2021

Journal of Machine Engineering

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The digitalization of modern manufacturing systems has resulted to increasing data generation, also known as Big Data. Although there are several technologies and techniques under the term Data Analytics for gathering such data, their interpretation to information, and ultimately to knowledge remains in its infancy. Consequently, albeit engineers currently can monitor the factory level, optimization is cut off of the data acquisition, and is based on data related methodologies. The focus should be pivoted on designing and developing suitable frameworks for integrating Big Data to process optimization based on the context of information gathered from the shopfloor. This paper aims to investigate the opportunities and the gaps as well as the challenges arising in the current industrial landscape, towards the efficient utilization of Big Data, for process optimization based on the integration of semantics. To that end, a literature review is performed, and a data-based framework is presented.

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Section: Semantics For Big Data Acquisitionmentioning

confidence: 99%

Towards the 5th Industrial Revolution: A literature review and a framework for Process Optimization Based on Big Data Analytics and Semantics

Mourtzis¹

2021

Journal of Machine Engineering

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“…Several proposals have been documented in the use of Big Data technologies for storing and querying RDF data [2][3][4][5][6]. The most common way so far to query massive static RDF data has been rewriting SPARQL queries over the MapReduce Programming Model [7] and executing them on Hadoop [8] Ecosystems.…”

Section: Related Workmentioning

confidence: 99%

“…The amount and size of datasets represented in the Resource Description Framework (RDF) [1] language are increasing; this leads to challenging the limits of existing triple stores and SPARQL query evaluation technologies, requiring more efficient query evaluation techniques. Several proposals have been documented in state of the art use of Big Data technologies for storing and querying RDF data [2][3][4][5][6]. Some of these proposals have focused on executing SPARQL queries on the MapReduce Programming Model [7] and its implementation, Hadoop [8].…”

Section: Introductionmentioning

confidence: 99%

SPARQL2Flink: Evaluation of SPARQL Queries on Apache Flink

et al. 2021

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Existing SPARQL query engines and triple stores are continuously improved to handle more massive datasets. Several approaches have been developed in this context proposing the storage and querying of RDF data in a distributed fashion, mainly using the MapReduce Programming Model and Hadoop-based ecosystems. New trends in Big Data technologies have also emerged (e.g., Apache Spark, Apache Flink); they use distributed in-memory processing and promise to deliver higher data processing performance. In this paper, we present a formal interpretation of some PACT transformations implemented in the Apache Flink DataSet API. We use this formalization to provide a mapping to translate a SPARQL query to a Flink program. The mapping was implemented in a prototype used to determine the correctness and performance of the solution. The source code of the project is available in Github under the MIT license.

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Query Processing over Large RDF using SPARQL in Big Data

Cited by 2 publications

References 15 publications

Towards the 5th Industrial Revolution: A literature review and a framework for Process Optimization Based on Big Data Analytics and Semantics

Towards the 5th Industrial Revolution: A literature review and a framework for Process Optimization Based on Big Data Analytics and Semantics

SPARQL2Flink: Evaluation of SPARQL Queries on Apache Flink

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