Description:
Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics.
This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management.
Brief description: Loek Cleophas is an assistant professor in the Model-Driven Software Engineering (MDSE) section at Eindhoven University of Technology (TU/e) and a research fellow at Stellenbosch University, South Africa. He obtained his doctorate in computer science and engineering at TU/e. His work in MDSE has varied from model-driven virtualization of high-tech systems, to generating efficient algorithm toolkits based on algorithm taxonomies. More recent work focuses on analyzing large collections of models and extracting variability and commonality information from them. His research in algorithm engineering and algorithm comparison focuses on pattern matching and finite automata for processing text and tree-shaped data. He worked in industry in the Netherlands and the USA, and at universities in South Africa, Sweden, and Germany, on research funded by various national and international projects as well as by industrial partners. He is also managing director of the Dutch research school on programming and algorithmics (IPA).