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Framework for modelling and implementing secure NoSQL document databases

The great amount of data managed by Big Data technologies have to be correctly assured in order to protect critical enterprise and personal information. Nevertheless, current security solutions for Big Data technologies such as NoSQL databases do not take into account the special characteristics of these technologies. In this paper, we focus on assuring NoSQL document databases proposing a framework composed of three stages: (1) the source data set is analysed by using Natural Language Processing techniques and ontological resources in order to detect sensitive data. (2) we define a metamodel for document NoSQL databases that allows designer to specify both structural and security aspects. (3) this model is implemented into a specific document database tool, MongoDB. Finally, we apply the framework proposed to a case study with a dataset of medical domain.

Autores: Carlos Blanco Bueno / Jesus Peral / Juan Trujillo / Eduardo Fernandez-Medina / 
Palabras Clave: big data - model - Natural Language Processing - No SQL - security

Configurable feature models

Feature models represent all the products that can be built under a variability-intensive system such as a software product line, but they are not fully configurable. There exist no explicit effort in defining configuration models that enable making decisions on attributes and cardinalities in feature models that use these artefacts. In this paper we present configurable feature models as an evolution from feature models that integrate configuration models within, improving the configurability of variability-intensive systems.

Autores: Pablo Trinidad / Antonio Ruiz-Cortés / Jesús García-Galán / 
Palabras Clave: configurable feature models - configuration - model - software product lines

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