Big Data and Knowledge Management: How to implement conceptual models in NoSQL systems?

Abdelhédi, Fatma, Ait Brahim, Amal, Atigui, Faten and Zurfluh, Gilles (2016) Big Data and Knowledge Management: How to implement conceptual models in NoSQL systems? In: 8th International Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016), 9 November 2016 - 11 November 2016, Porto.

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Abstract

In 2014, Big Data has passed the top of the Gartner Hype Cycle, proving that Big Data technologies and application start to be mature, becoming more realistic about how Big Data can be useful for organizations . NoSQL data stores are becoming widely used to handle Big Data; these databases operate on schema-less data model enabling users to incorporate new data into their applications without using a predefined schema. But, there is still a need for a conceptual model to define how data will be structured in the database. In this paper, we show how to store Big Data within NoSQL systems. For this, we use the Model Driven Architecture (MDA) that provides a framework for models automatic transformation. Starting from a conceptual model that describes a set of complex objects, we propose transformation rules formalized with QVT to generate a column-oriented NoSQL model. To ensure efficient automatic transformation, we use a logical model that limits the impacts related to technical aspects of column-oriented platforms. We provide experiments of our approach using a case study example taken from the health care domain. The results of our experiments show that the proposed logical model can be effectively implemented in different column-oriented systems independently of their specific technical details.

Item Type: Conference or Workshop Item (Paper)
Date: 2016
Uncontrolled Keywords: Big Data ; NoSQL ; Knowledge ; MDA ; QVT transformation
Subjects: H- INFORMATIQUE
Divisions: Institut de Recherche en Informatique de Toulouse
Site: UT1
Date Deposited: 24 Jan 2019 15:13
Last Modified: 20 Mar 2019 14:00
URI: http://publications.ut-capitole.fr/id/eprint/28895

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