Adbis Tpdl And Eda 2020 Common Workshops And Doctoral Consortium
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ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium
Author: Ladjel Bellatreche
language: en
Publisher: Springer Nature
Release Date: 2020-08-18
This book constitutes thoroughly reviewed and selected papers presented at Workshops and Doctoral Consortium of the 24th East-European Conference on Advances in Databases and Information Systems, ADBIS 2020, the 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, and the 16th Workshop on Business Intelligence and Big Data, EDA 2020, held in August 2020. Due to the COVID-19 the joint conference and satellite events were held online. The 26 full papers and 5 short papers were carefully reviewed and selected from 56 submissions. This volume presents the papers that have been accepted for the following satellite events: Workshop on Intelligent Data - From Data to Knowledge, DOING 2020; Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2020; Workshop on Scientic Knowledge Graphs, SKG 2020; Workshop of BI & Big Data Applications, BBIGAP 2020; International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2020; International Workshop on Assessing Impact and Merit in Science, AIMinScience 2020; Doctoral Consortium.
Implementation and Benefits of Digital Twin on Decision Making and Data Quality Management
Digital twin technology is becoming important for the realization of Industry 4.0 using cyber-physical systems (CPS) and information technology. CPS form the backbone to support the creation of a network for decentralized and autonomous decision-making. The design principles for Industry 4.0 serve as guidelines for virtualization concepts that are virtual copies of the physical world and create a link between the real and virtual worlds to collect data and monitor processes, the so-called digital twin. In this book, a theoretical digital twin-driven decision-making model has been developed that combines corporate data quality management, a process digital twin, and a model-driven decision support system. It leverages the benefits of the digital twin to create, test and build a process in the virtual world that supports decision making by combining data, analytics and visualization of insights to help managers make better decisions
Predicting the Dynamics of Research Impact
Author: Yannis Manolopoulos
language: en
Publisher: Springer Nature
Release Date: 2021-09-22
This book provides its readers with an introduction to interesting prediction and science dynamics problems in the field of Science of Science. Prediction focuses on the forecasting of future performance (or impact) of an entity, either a research article or a scientist, and also the prediction of future links in collaboration networks or identifying missing links in citation networks. The single chapters are written in a way that help the reader gain a detailed technical understanding of the corresponding subjects, the strength and weaknesses of the state-of-the-art approaches for each described problem, and the currently open challenges. While chapter 1 provides a useful contribution in the theoretical foundations of the fields of scientometrics and science of science, chapters 2-4 turn the focal point to the study of factors that affect research impact and its dynamics. Chapters 5-7 then focus on article-level measures that quantify the current and future impact of scientific articles. Next, chapters 8-10 investigate subjects relevant to predicting the future impact of individual researchers. Finally, chapters 11-13 focus on science evolution and dynamics, leveraging heterogeneous and interconnected data, where the analysis of research topic trends and their evolution has always played a key role in impact prediction approaches and quantitative analyses in the field of bibliometrics. Each chapter can be read independently, since it includes a detailed description of the problem being investigated along with a thorough discussion and study of the respective state-of-the-art. Due to the cross-disciplinary character of the Science of Science field, the book may be useful to interested readers from a variety of disciplines like information science, information retrieval, network science, informetrics, scientometrics, and machine learning, to name a few. The profiles of the readers may also be diverse ranging from researchers and professors in the respective fields to students and developers being curious about the covered subjects.