Abstract

Abstract
Mobile, self-organising robots are seen to be a possible solution to overcome the current limitations of fixed, dedicated automation systems particularly in the area of large structure assembly. Two of the key challenges for traditional dedicated automation systems in large structure assembly are considered to be the transportation of products and the adaptation of manufacturing processes to changes in requirements. In order to make dynamic, self-organising systems a reality, several challenges in the process dynamics and logistical control need to be solved. In this paper, we propose a Multi-Agent System (MAS) approach to self-organise mobile robots in large structure assembly. The model is based on fixed-priority pre-emptive scheduling and uses a blackboard agent as a central information source and to facilitate more common goal directed distributed negotiation and decision making between agents representing the different needs of products and available mobile resources (robots).

Abstract
Cyber Physical Production Systems (CPSS) built on the concept of “Plug-and-Produce” aim at delivering truly agile production systems. These systems are modular by nature and can be adapted based on changing requirements. One of the challenges in this domain is how to store and propagate information within CPPS. In this paper, a Redundant and Decentralised Directory Facilitator will be introduced to provide the capability to store and broadcast the existing system assembly capabilities. Additionally, this solution will provide redundancy and delocalization of the assembly capabilities information. The model used is described, as well as interactions, behaviours and deployment strategies. Finally a validation scenario is presented and conclusions are discussed. © Springer International Publishing AG 2017.

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.