Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution

The distinct evaluation of information in a new managerial function of information – decision

The function defined as information-decision can be considered today the central function of management; we believe that the option for a compromise of the type: prognosis of product or service, organization, information - decision, stimulation and control better responds to the new managerial conditions. Any decision primarily means correct information, in order to be able to choose. Surprisingly, from the old Greek term entropis to the actual managerial information it is not such a long way and stages of the new function of information-decision emphasise the continuous interdependences between information and decision, as well as a large number of characteristic features as a result of a necessary compromise in contemporary management.information-decision, entropy, redundancy, managerial information and decision

The distinct evaluation of information in a new managerial function of information – decision

The function defined as information-decision can be considered today the central function of management; we believe that the option for a compromise of the type: prognosis of product or service, organization, information - decision, stimulation and control better responds to the new managerial conditions. Any decision primarily means correct information, in order to be able to choose. Surprisingly, from the old Greek term entropis to the actual managerial information it is not such a long way and stages of the new function of information-decision emphasise the continuous interdependences between information and decision, as well as a large number of characteristic features as a result of a necessary compromise in contemporary management