Using Information Filtering in Web Data Mining Process

Web service-oriented Grid is becoming a standard for achieving loosely coupled distributed computing. Grid services could easily be specified with web-service based interfaces. In this paper we first envisage a realistic Grid market with players such as end-users, brokers and service providers participating co-operatively with an aim to meet requirements and earn profit. End-users wish to use functionality of Grid services by paying the minimum possible price or price confined within a specified budget, brokers aim to maximise profit whilst establishing a SLA (Service Level Agreement) and satisfying end-user needs and at the same time resisting the volatility of service execution time and availability. Service providers aim to develop price models based on end-user or broker demands that will maximise their profit. In this paper we focus on developing stochastic approaches to end-user workflow scheduling that provides QoS guarantees by establishing a SLA. We also develop a novel 2-stage stochastic programming technique that aims at establishing a SLA with end-users regarding satisfying their workflow QoS requirements. We develop a scheduling (workload allocation) technique based on linear programming that embeds the negotiated workflow QoS into the program and model Grid services as generalised queues. This technique is shown to outperform existing scheduling techniques that don't rely on real-time performance information

Software Defect Association Mining and Defect Correction Effort Prediction

Much current software defect prediction work concentrates on the number of defects remaining in software system. In this paper, we present association rule mining based methods to predict defect associations and defect-correction effort. This is to help developers detect software defects and assist project managers in allocating testing resources more effectively. We applied the proposed methods to the SEL defect data consisting of more than 200 projects over more than 15 years. The results show that for the defect association prediction, the accuracy is very high and the false negative rate is very low. Likewise for the defect-correction effort prediction, the accuracy for both defect isolation effort prediction and defect correction effort prediction are also high. We compared the defect-correction effort prediction method with other types of methods: PART, C4.5, and Na¨ıve Bayes and show that accuracy has been improved by at least 23%. We also evaluated the impact of support and confidence levels on prediction accuracy, false negative rate, false positive rate, and the number of rules. We found that higher support and confidence levels may not result in higher prediction accuracy, and a sufficient number of rules is a precondition for high prediction accuracy

Human Resource Inputs and Educational Outcomes in Botswana’s Schools: Evidence from SACMEQ and TIMMS

This study explores the important relationship between policy variables that represent a school’s human resources and product variables in the form of student performance in Botswana’s schools. A focus of particular interest is if the teaching environment is related to student success and whether it can promote equity in learning between students from different socioeconomic backgrounds. Data for the study are drawn from a rich survey of students, teachers and schools in Southern and Eastern Africa. There is modest evidence to suggest that students attending well resourced schools are likely to perform better, irrespective of their background. The results points to a clear association between teacher content preparation and student achievement. Regular assessment is associated with better performance and greater social equity between students within the same school. Policy implications related to teacher preparation programmes in Botswana are discussed.Botswana, education production function, demand for schooling, teacher evaluation, teacher knowledge, teacher education

Hybrid Association Rule Mining using AC Tree

In recent years, discovery of association rules among item sets in large database became popular. It gains its attention on research areas. Several association rule mining algorithms were developed for mining frequent item set. In this papers, a new hybrid algorithm for mining multilevel association rules called AC Tree i.e., AprioriCOFI tree was developed. This algorithm helps in mining association rules at multiple concept levels. The proposed algorithm works faster compared to traditional association rule mining algorithm and it is efficient in mining rules from large text documents. Keywords: Association rules, Apriori, FP tree, COFI tree, Concept hierarchy