Reservoir provides many benefits to human life generally and public society specifically. It leads to the generalization of energy, supplying and irrigating water for human use, hence improving human daily basis activities and fulfilling human needs. Yet, everything has its own weaknesses. As for the reservoir situation, there is a possibility of the operation to be failed and will leads to the creation of flood that is very harmful for the area of the reservoir. This research had been conducted with the implementation of information visualization, data mining and case based reasoning techniques in order to help the process of generating visualization result in opening or closing the gate of the dam to channel out excessive water. As a conclusion, the process of visualizing numerical values is proven to be faster than the normal process. Using visualization, people tend to make fewer mistakes for a large quantity of data. However, for upcoming time-being, it is advised that some methods of calculating the similarity of these visualize cases is implemented since visualization alone is still not enough to provide precise results. � 2010 IEEE.

Data mining (DM) is the extraction of hidden predictive information from large databases that has becoming a powerful new technology with great potential to help companies to focus on the most important information in their data warehouses. A predictive model makes a prediction about values of data using known results found from historical data where the best possible outcome based on the previous data is derived. Telekom Malaysia Berhad (TM) is Malaysia's premier communications provider that provides the digital backbone and communication facilities. Direct Exchange Line (DEL) is one of its core telephony services that handle large volume and variety of data in its daily operations. Therefore, it is hard to reveal knowledge structures that can guide decisions in conditions of limited certainty. The main objective of this study is to identify the most appropriate DM techniques (logistic regression, decision trees and neural networks) for predicting DEL growth based on five physical attributes constitute of 672 instances leading to a target (either increase or decrease). The finding is important especially in assisting the prediction of DEL growth in Telekom, thus leading on gaining better understanding on the future of the market based on the current and previous situation. � 2010 IEEE.