Abstract
The proliferation of textual data in society currently is overwhelming, in particular, unstructured textual data is being constantly generated via call centre logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, etc. While the amount of textual data is increasing rapidly, users’ ability to summarise, understand, and make sense of such data for making better business/living decisions remains challenging. This paper studies how to analyse textual data, based on layered software patterns, for extracting insightful user intelligence from a large collection of documents and for using such information to improve user operations and performance.
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Recommended by Associate Editor Hong-Ji Yang
Hossam Hakeem received the B. Sc. degree in business administration from King Abdulaziz University, Saudi Arabia in 1995, the M. Sc. degree in public management from the H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, USA, with the highest distinction in 2004, and the Ph.D. degree in computer science and informatics from the Software Technology Research Laboratory, Faculty of Technology, De Montfort University, UK in 2010. He was the vice dean of Deanship of Library Affair, King Abdulaziz University, Saudi Arabia. He is currently with the Information System Department, Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia. He is the member of the Association for Computing Machinery (ACM), the Institute of Electrical and Electronics Engineers (IEEE) Computer Society and the IEEE Communication Society.
His research interest is software engineering, in particular, software patterns, web designs and software quality improvement.
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Hakeem, H. Layered software patterns for data analysis in big data environment. Int. J. Autom. Comput. 14, 650–660 (2017). https://doi.org/10.1007/s11633-016-1043-x
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DOI: https://doi.org/10.1007/s11633-016-1043-x