Intelligent context-based pattern matching approaches to enhance decision making

Abstract

In this Internet and Cloud Computing era, every second, there is huge volume of data, whether structured or unstructured, is being stored or retrieved by various applications for use in different ways to support decision making. These business applications certainly require effective and accurate means to store and retrieve information on contextual basis to support decision making. Merely using pattern matching methods without considering the context may not help to retrieve the most suitable and accurate information for decision making. This paper therefore introduces three web applications that apply intelligent pattern matching approaches to retrieve accurate information to enhance decision making on contextual basis. In the first study, stemming and Boyer-Moore methods are incorporated with company policies to auto search, and recommend the right candidate to attend the most appropriate training course. The second study, through several iterations of pattern matching using a lookup table, locates the best three real estate properties that match potential buyer’s preferences. In the third study, a color matching scheme is used to find users’ preferred images or photos stored in a Cloud storage. Testing and performance evaluation of these methods using the web applications show results that could effectively enhance decision making.