Abstract
This paper stems from the Poetry Standardization and Linked Open Data project (POSTDATA). As its name reveals, one of the main aims of POSTDATA is to provide a means to publish European poetry (EP) data as Linked Open Data (LOD). Thus, developing a metadata application profile (MAP) as a common semantic model to be used by the EP community is a crucial step of this project. This MAP will enhance interoperability among the community members in particular, and among the EP community and other contexts in general (e.g. bibliographic records). This paper presents the methodology followed in the process of defining the concepts of the domain model of this MAP, as well as some issues that arise when labeling philological terms.

Abstract
This chapter presents the process of developing a Metadata Application Profile for the Social and Solidarity Economy (DCAP-SSE) using Me4MAP, a method for developing Application Profiles that was being put forth by the authors. The DCAPSSE and Me4MAP were developed iteratively, feeding new developments into each other. This paper presents how the DCAP-SSE was developed showing the steps followed through the development of the activities and the techniques used, and the final deliverables obtained at the end of each activity. It also presents the work-team and how each profile of the team contributed for the DCAP-SSE development process. The DCAP-SSE has been endorsed by the SSE community and new perspectives of SSE activities have been defined for future enlargement of the DCAP-SSE. At the time of writing this chapter, Linked Open SSE Data is being published, they are the first examples of use of the DCAP-SSE. © 2017, IGI Global.

Abstract
The prevalence of data science has grown exponentially in recent years. Increases in data exchange have created the need for standards and formats on handling data from different sources. Developing Metadata Application Profiles is an innovative reference source that discusses the latest trends and techniques for effectively managing and exchanging metadata. Including a range of perspectives on schemas and application profiles, such as interoperability, ontology-based design, and model-driven approaches, this book is ideally designed for researchers, academics, professionals, graduate students, and practitioners actively engaged in data science.

Abstract
We present a novel way to link business process model with data protection risk management. We use established body of knowledge regarding risk manager concepts and business process towards data protections. We try to contribute to the problems that today organizations should find a suitable data protection model that could be used in as a risk framework. The purpose of this document is to define a model to describe data protection in the context of risk. Our approach including the identification of the main concepts of data protection according to the scope of the with EU directive data protection regulation. We outline data protection model as a continuous way of protection valued organization information regarding personal identifiable information. Data protection encompass the preservation of personal data information from unauthorized access, use, modification, recording or destruction. Since this kind of service is offered in a continuous way, it is important to stablish a way to measure the effectiveness of awareness of data subject discloses regrading personal identifiable information.

Abstract
Financial data provides a valuable up-to-date knowledge of the world economy. However, it is presented in extremely large data volumes, in diverse formats, and is constantly being updated at a high speed. The Ramex-Forum algorithm is oriented to guide financial experts in finding new and relevant information. We present a sensitivity analysis and new visualizations using an improved version of the Ramex-Forum algorithm. The proposed algorithm is applied to two case studies - the petroleum production chain and the European financial institutions risk analysis. Different combinations of parameters and new ways to visualize data are used. Results highlight the importance of Ramex-Forum for analysing relevant relationships in price variations in financial markets.

Abstract
Feature selection is one of the most important concepts in data mining when dimensionality reduction is needed. The performance measures of feature selection encompass predictive accuracy and result comprehensibility. Consistency based feature selection is a significant category of feature selection research that substantially improves the comprehensibility of the result using the parsimony principle. In this work, the feature selection algorithm LAID, Logical Analysis of Inconsistent Data, is applied to large volumes of data. In order to deal with hundreds of thousands of attributes, a problem de-composition strategy associated with a set covering problem formulation is used. The algorithm is applied to artificial datasets with genome-like characteristics of patients with rare diseases.