Adaptive Approach for Intelligent Web to Enhance Business Intelligence Applications

نوع المستند : المقالة الأصلية

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The World Wide Web (WWW) has grown quickly in the past two decades from a small research community to the biggest and most popular infrastructure for communication, information dissemination, search, social interaction and commerce. The continuous growth in size and use of the WWW creates a need for methods to process these wicked volumes of data. Web Intelligence (WI), as a research direction, has a broad agenda to deal with the issues that arise around the WWW phenomenon. WI corresponds to research and development to explore the fundamental roles, artificial intelligence and advanced information technology on the web-empowered systems, services, and activities. In this context, WI is concerning of mining in web data and user behaviors. This creates a demand for using mining technologies to search large volume of data for gaining hidden knowledge. This hidden knowledge helps in gaining competitive advantages, better customers’ relationships, and even fraud detection. Achieving the intelligence to the web enhances Business Intelligence (BI) for the enterprises. Web Usage Mining (WUM) and Web Opinion Mining (WOM) are considered a leading mining approaches in mining the user behavior and reviews.  Most of previous studies depended in building intelligent web on mining and analyzing user’s profiles or opinions separately. This becomes not fair enough and cause of limitations. This limitations could be narrowed if both of user preferences and opinions are considered in building recommendations. The paper proposes a framework for achieving the intelligence via using WUM and WOM. The proposed framework would contribute in solving the problem. The paper also surveys the background of using the intelligent web as an approach to enhance the Business Intelligence (BI) applications

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