[AAA16a] Big Data and Knowledge Management: How to implement conceptual models in NoSQL systems ?

Conférence Internationale avec comité de lecture : the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, November 2016, Vol. 3, pp.235-240, Porto, Portugal,

Auteurs: F. Abdelhedi , A. Ait Brahim , F. Atigui , G. Zurfluh

Mots clés: Big Data, NoSQL, Knowledge, MDA, QVT transformation

Résumé: 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.

BibTeX

@inproceedings {
	AAA16a,
	title	=	"{Big Data and Knowledge Management: How to implement conceptual models in NoSQL systems ?}",
	author	=	" F. ABDELHEDI and A. Ait Brahim and F. Atigui and G. Zurfluh ",
	booktitle	=	"{the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management}",
	year	=	2016,
	month	=	"November",
	volume	=	3,
	pages	=	"235-240",
	address	=	"Porto, Portugal",
}