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Enhancing EUCalipTool Service Composition through Natural Language Processing

Although end-users have available a lot of on-line services to be con- sumed individually, it is their composed usage what has the potential to create new value-added services for end-users. In this sense, many efforts have been done to allow end-users to compose the services that they need by themselves. However, most of these solutions present two main problems: (1) they provide little support to help end-users to browse interminable lists of services, and (2) they present the blank piece of paper problem, which appears when end-users have to face an empty canvas to define a composition without any help to find the services that better fit their needs. In this paper, we present a solution to im- prove these problems by using natural language processing techniques in order to search and select the services end-users need to accomplish a specific goal. This solution has been implemented in the context of the EUCalipTool platform.

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.