Abstract

Abstract
This article presents a literature review and the discussion about the key concepts associated with data markets. Data markets have multiple centralized and decentralized approaches. The main problem is the trust and reliability of supplies, inflows, and suppliers. The proposed study object is the decentralized marketplace data supported by Blockchain technology to ensure confidence in the supply chain of data, in the actors involved in the market and the data sources. The application scenarios are proposed in a model with four levels, data provision, data delivery, rights management, and producer internal sources. That will be done with Blockchain technology, through contracting using smart contracts, the controlled delivery of data by the data producers, the management of flows of data, and access control to data.

Abstract
The 4th industrial revolution advent promotes the reorganization of the traditional hierarchical automation systems towards decentralized Cyber-Physical Systems (CPS). In this context, Artificial Intelligence (AI) can address the new requirements through the use of data-driven and distributed problem solving approaches, such those based on Machine-Learning and Multi-agent Systems. Although their promising perspectives to enable and manage intelligent Internet of Things environments, the traditional Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive. The solution lies in taking advantage of Edge and Fog computing to create a decentralized multi-level data analysis computing infrastructure that supports the development of industrial CPS. However, this is not a straightforward task, posing several challenges and demanding new approaches and technologies. In this context, this work discusses the distribution of intelligence along Cloud, Fog and Edge computing layers in industrial CPS, leveraging some research challenges and future directions. Copyright

Abstract

Abstract
Embracing agility brings change to companies and everyone involved, so people have a fundamental role in project development. It is essential that teams have a good communication, motivation and concern about quality and client satisfaction, Agile Manufacturing is using Agile Project Management to provide the right team to performance difficult tasks and achieve goals. This paper proposes a team that deals with Agile Project Management, named as AGILE team, composed by a product owner, a team leader and team members, and a communication workflow proposal concerning the conjunction of 3 momentums (requirements analyze, planning and design) along the Agile Project Management. The proposed model is being implemented in a lightening manufacturing company in a very early stage. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the CENTERIS -International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies.

Abstract
Frequently software testing tends to be neglected at the beginning of the projects, only performed on the late stage. However, it is possible to benefit from combining requirement with testing specification activities. On one hand, acceptance tests specification will require less manual effort since they are defined or generated automatically from the requirements specification. On the other hand, the requirements specification itself will end up having higher quality due to the use of a more structured language, reducing typical problems such as ambiguity, inconsistency and incorrectness. This research proposes an approach that promotes the practice of tests specification since the very beginning of projects, and its integration with the requirements specification itself. It is a model-driven approach that contributes to maintain the requirements and tests alignment, namely between requirements, test cases, and low-level automated test scripts. To show the applicability of the approach, two complementary languages are adopted: the ITLingo RSL that is particularly designed to support both requirements and tests specification; and the Robot language, which is a low-level keyword-based language for the specification of test scripts. The approach includes model-to-model transformation techniques, such as test cases into test scripts transformations. In addition, these test scripts are executed by the Robot test automation framework.