Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Modeling, Simulation and Optimization of Wind Farms and Hybrid Systems

The reduction of greenhouse gas emissions is a major governmental goal worldwide. The main target, hopefully by 2050, is to move away from fossil fuels in the electricity sector and then switch to clean power to fuel transportation, buildings and industry. This book discusses important issues in the expanding field of wind farm modeling and simulation as well as the optimization of hybrid and micro-grid systems. Section I deals with modeling and simulation of wind farms for efficient, reliable and cost-effective optimal solutions. Section II tackles the optimization of hybrid wind/PV and renewable energy-based smart micro-grid systems

Deliberation, Representation, Equity

"What can we learn about the development of public interaction in e-democracy from a drama delivered by mobile headphones to an audience standing around a shopping center in a Stockholm suburb? In democratic societies there is widespread acknowledgment of the need to incorporate citizens’ input in decision-making processes in more or less structured ways. But participatory decision making is balancing on the borders of inclusion, structure, precision and accuracy. To simply enable more participation will not yield enhanced democracy, and there is a clear need for more elaborated elicitation and decision analytical tools. This rigorous and thought-provoking volume draws on a stimulating variety of international case studies, from flood risk management in the Red River Delta of Vietnam, to the consideration of alternatives to gold mining in Roșia Montană in Transylvania, to the application of multi-criteria decision analysis in evaluating the impact of e-learning opportunities at Uganda's Makerere University. Editors Love Ekenberg (senior research scholar, International Institute for Applied Systems Analysis [IIASA], Laxenburg, professor of Computer and Systems Sciences, Stockholm University), Karin Hansson (artist and research fellow, Department of Computer and Systems Sciences, Stockholm University), Mats Danielson (vice president and professor of Computer and Systems Sciences, Stockholm University, affiliate researcher, IIASA) and Göran Cars (professor of Societal Planning and Environment, Royal Institute of Technology, Stockholm) draw innovative collaborations between mathematics, social science, and the arts. They develop new problem formulations and solutions, with the aim of carrying decisions from agenda setting and problem awareness through to feasible courses of action by setting objectives, alternative generation, consequence assessments, and trade-off clarifications. As a result, this book is important new reading for decision makers in government, public administration and urban planning, as well as students and researchers in the fields of participatory democracy, urban planning, social policy, communication design, participatory art, decision theory, risk analysis and computer and systems sciences.