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  4. OPTIMA: Framework Selecting Optimal Virtual Model to Query Large Heterogeneous Data
 
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July 26, 2022
Conference Paper
Title

OPTIMA: Framework Selecting Optimal Virtual Model to Query Large Heterogeneous Data

Abstract
OPTIMA is a framework that enables querying the original data on-the-fly without any materialization. It implements two different virtual data models, GRAPH and TABULAR, to join and aggregate data. OPTIMA leverages ontology-based data access and calls the deep learning method to predict the optimal virtual data model using the features extracted from SPARQL queries. Extensive experiments show a reduction in query execution time of over 40% for the TABULAR model selection, and over 30% for the GRAPH model selection.
Author(s)
Belmehdi, Chahrazed B. Bachir
ESI-SBA Institute, Sidi Bel Abbès, Algeria
Khiat, Abderrahmane  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keskes, Nabil
ESI-SBA Institute, Sidi Bel Abbès, Algeria
Mainwork
Big Data Analytics and Knowledge Discovery. 24th International Conference, DaWaK 2022. Proceedings  
Conference
International Conference on Big Data Analytics and Knowledge Discovery 2022  
DOI
10.1007/978-3-031-12670-3_18
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keyword(s)
  • Data Virtualization

  • OBDA

  • Big Data

  • Deep Learning

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