Collection

Knowledge Discovery in Big Data

Submission status
Closed
Technological advancements pertaining to big data such as artificial intelligence, business analytics, data mining and machine learning, help greatly in making the right decisions at the right time. The WWW and social area networks (SANs) have contributed greatly to the volume and heterogeneity of data available, (e.g., text, images, videos, audio and drawings). Using big data to obtain greater insight poses significant challenges in terms of computing efficiency, business analytical problem-solving and knowledge discovery. This special issue is intended to be a meeting point between researchers and big data analysts. The issue focuses on problems relating to big data, giving the opportunity to researchers to propose new methods of transforming technological frameworks into big data techniques and to produce new research results pertaining to knowledge discovery. For this special issue on knowledge discovery in big data (KDBD), we have collected 10 articles that address relevant developments and advances in the area. These articles were selected and improved during a two-round review process, in accordance with the standard practices of the Journal of Grid Computing. The resulting 10 contributions cover some key aspects and developments regarding big data technologies in the realm of knowledge discovery.

Editors

  • Sajid Anwar Sajid Anwar  &

    Sajid Anwar

    Dr Sajid Anwar is an Associate Professor at the Center of Excellence in Information Technology Institute of Management Sciences (IMSciences), Peshawar, Pakistan. He received his MS (Computer Science) and Ph.D degrees (Software Engineering) from NUCES-FAST, Islamabad. Dr Anwar is leading expert in Software Architecture Engineering and Software Maintenance Prediction. His research interests are cross-disciplinary & industry focused and include: search based software engineering, prudent based expert systems; customers churn prediction modelling, active learning, applying data mining and machine learning techniques to real world problems.

  • Álvaro Rocha Álvaro Rocha

    Álvaro Rocha

    Álvaro Rocha is a Professor of Information Systems at the University of Lisbon - ISEG. He is Vice-Chair of the IEEE SMC Portugal Chapter and Editor-in-Chief of Journal of Information Systems Engineering & Management and Iberian Journal of Information Systems and Technologies. He has served as Vice-Chair of Experts for the European Commission’s Horizon 2020 program, and an Expert at the COST - intergovernmental framework for European Cooperation in Science and Technology, at Italy’s Ministry of Universities and Research, Latvia’s Ministry of Finance, at Mexico's National Council of Science and Technology, and Poland's National Science Centre.

Articles (17 in this collection)