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.
Tekinerdogan, B., Babur, O., Cleophas, L., van den Brand, M., & Aksit, M. (Eds.) (2019). Model Management and Analytics for Large Scale Systems. (1 ed.) Elsevier Academic Press. https://doi.org/10.1016/C2018-0-00106-1