The Six Technical Gaps between Intelligent Applications and Real-Time Data Mining: A Critical Review

Intelligent application is characterized by its ability to make decision autonomously. Some examples are distributed agents that collaboratively complete a complex task, yet each one of them is able to work and reason independently given the dynamic situation where they are in. The underlying think tank is often a collection of core mechanisms that include environment sensing, data capturing, data mining, ETL, knowledge processing, and decision making etc. All these techniques when placed and function together as a whole intelligent system, they will have to fulfill stringent deadlines imposed by the requirements of a real-time system. In the literature many research papers can be found on a wide variety of data mining techniques that enable intelligent applications operating in real-time. This report offers a critical review of the relevant literature, and contributes to the knowledge of identifying their shortcomings, so-called the technical gaps between the real-time requirements of a general intelligent system and the supporting components. A discussion follows on the possibilities of future intelligent applications that are empowered by data stream mining.