Advances in Data Science and Information Engineering

Robert Stahlbock is a researcher and lecturer at the Institute of Information Systems at the University of Hamburg. He is also lecturer at the FOM University of Applied Sciences since 2003. He holds a diploma in Business Administration and a PhD from the University of Hamburg. His research interests are focused on managerial decision support and issues related to maritime logistics and other industries as well as operations research, information systems, business intelligence and data science. He is author of research studies published in international prestigious journals, conference proceedings and book chapters. He serves as guest editor of data science related books, as reviewer for international leading journals as well as a member of conference program committees. He is General Chair of the annual International Conference on Data Science since 2006. He also consults companies in various sectors and projects. Dr. Gary Weiss is an associate professor in the Department of Computer and Information Science at Fordham University. His main research area is data mining. His early research focused on real-world issues that make learning from data difficult, such as class imbalance. Over the last decade he has directed the WIreless Sensor Data Mining (WISDM) lab, and has focused on extracting knowledge from sensors on smartphones and smartwatches, mainly to perform automated activity recognition and behavioral biometrics. He is currently devoting much of his attention to data mining of educational data. Dr. Weiss has published over ninety papers in Data Mining and other areas of Computer Science. Dr. Weiss received his B.S. in Computer Science from Cornell University, his M.S. in Computer Science from Stanford University, and his Ph.D. in Computer Science from Rutgers University. Prior to starting work at Fordham University in 2004, Dr. Weiss spent more than 15 years working for AT&T Labs. Dr. Mahmoud Abou-Nasr, AdjunctFaculty, CIS Department, University of Michigan Dearborn, USA. Technical Expert, Neural Networks & Intelligent Systems, Research & Advanced Engineering, Ford Motor Company, USA (1993-2018). Dr. Abou-Nasr is a Senior Member of the IEEE and Vice Chair Technical Activities Computational Intelligence & Systems Man and Cybernetics SEM Chapter (2011-2014). He has received the B.Sc. degree in Electrical Engineering in 1977 from the University Of Alexandria, Alexandria, Egypt, the M.S. and the Ph.D. degrees in 1984 and 1994 respectively from the University Of Windsor, Ontario, Canada, both in Electrical Engineering. He has been a Technical Expert with Ford Motor Company, Research and Advanced Engineering, where he led research & development of deep learning, recurrent neural networks and advanced computational intelligence techniques for automotive applications. His research interests are in the areas of deep learning, deep convolutional networks, recurrent neural networks, reinforcement learning, pattern recognition, forecasting, data mining, optimization and control. Currently he is an adjunct faculty member of the computer and information science department, of the University of Michigan Dearborn. Prior to joining Ford, he held electronics and software engineering positions with the aerospace and robotics industries in the areas of real-time control and embedded communications protocols. He is a co-editor of a book on "Real World Data Mining Applications," Annals of Information Systems, Springer and associate editor of the DMIN'09-DMIN'17, ICDATA'18-ICDATA'19 proceedings. He is a member of the program and technical committees of IJCNN, WCCI, ISVC and ECAI. He is also a reviewer for IJCNN, MSC, CDC, Neural Networks and IEEE Transactions on Neural Networks & Learning Systems. Dr. Abou-Nasr has organized and chaired symposia & special sessions in SSCI16, WCCI 2015, DMIN and IJCNN conferences as well as international classification compet

Introduction.- Part I: Data Mining/Machine Learning Tasks.- Data Mining Algorithms.- Data Mining Integration.- Data Mining Process.- Data Mining Applications.- Data Mining Software.- Algorithms for Big Data.- Big Data Fundamentals.- Infrastructures for Big Data.- Big Data Management and Frameworks.- Big Data Search.- Privacy in the Era of Big Data.- Applications of Big Data.- Part II: information Retrieval Systems.- Knowledge Management and Cyber-Learning.- Database Engineering and Systems.- Data and Knowledge Processing.- Databanks: Issues, Methods, and Standards.- Data Warehousing and Datacenters.- Health Information Systems.- Data Security and Privacy Issues.- Information Reliability and Security.- Information and Knowledge Structures.- Knowledge Life Cycle.- Conclusion.