Guest Editorial

Business Intelligence (BI) is a concept of applying a set of technologies to convert data into meaningful information. Traditionally, BI is implemented as standalone decision-support systems that provide vital information probably in some form of reports, for enabling business analysts or managers to formulate business strategies. In recent years, BI and e-Business Applications have been developed as independent research fields which has received considerable amount of attention. The main challenges now are how BI can be fused into an e-Business application as a part of the business processes. Some advantages of integrating BI into e-Business applications include facilitating automation and decision making capabilities with real-time knowledge by software agents. When the two trends couple, new emerging models of e-Business applications become possible; Examples are creating new kinds of e-Business services such as agent-mediated B2B e-Commerce, intelligent B2C recommender, smart online service, and knowledge-driven customer relationship management. This special issue of JETWI consists of six papers that represent the latest research on the leading edge BI technology in different application scenarios. These papers are invited for extended versions, solicited from selected papers that have been presented at the International Workshop on Business Intelligence for Emerging e-Business Applications (BieeBa 2009), now its sequel called International Conference of Data Mining and Intelligent Information Technology Applications (ICMIA 2010 and 2011) being sponsored by IEEE Korea Council, IEEE Seoul Section, University of Macau, and AICIT. The first paper introduces six technical gaps for consideration in designing intelligent applications powered by data-mining. It is an insightful guide full of tips and pitfalls for software developers who are keen on designing a real-time based intelligent application. This design guide is accompanied by another technical paper that carries in-depth details on integrating Data Mining into OLAP technology from Auckland University of Technology, New Zealand. Followed by other interesting papers in this special issue, from the University of Macau, such as applying Sematic Web Services on telecommunication industry, data mining in GSM mobile network, and even an intelligent algorithm that can predict trends in stock market with positive results! We hope that via this special issue not only readers will be enlightened about what data mining and intelligent applications can do, but research endeavor from a wider community would be