Paper-071-31 Investigating Open Source Project Success: A Data Mining Approach to Model Formulation, Validation and Testing ABSTRACT

This paper demonstrates the use of Data Mining (DM) techniques in exploratory research. A robust model for identifying the factors that explain the success of Open Source Software (OSS) projects is created, validated and tested. The predictive modeling techniques of Logistic Regression (LR), Decision Trees (DT) and Neural Networks (NN) are used together in this analysis. Using Text Mining results in the predictive modeling process strengthens the model. SAS ® Enterprise Miner and SAS ® Text Miner are used in this research

The Impact of Special Requirements on the Estimation of Electrical Demand

Estimating electrical demand in 15---30-minute intervals plays a crucial role in the determination of electric utility rates and the planning functions of utility companies. In order to achieve accurate estimation results, the Public Utilities Regulatory Policies Act of 1978 specifies requirements that are unique to this estimation problem. This study investigates the impact of these requirements on estimation effectiveness. The results indicate that these requirements fail to achieve their purpose. The study also notes the importance of addressing the form of the underlying population of electrical demand.measurement: electrical demand, electric utility rates, PURPA requirements