Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis

In the academic world, a large amount of data is handled each day, ranging from student’s assessments to their socio-economic data. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. However, Data Warehouses are not able to perform predictive analysis by themselves, so machine intelligence techniques can be used for sorting, grouping, and predicting based on historical information to improve the analysis quality. This work describes a Data Warehouse architecture to carry out an academic performance analysis of students

Knowledge grid: high performance knowledge discovery services on the grid

Abstract. Knowledge discovery tools and techniques are used in an increasing number of scientific and commercial areas for the analysis of large data sets. When large data repositories are coupled with geographic distribution of data, users and systems, it is necessary to combine different technologies for implementing high-performance distributed knowledge discovery systems. On the other hand, computational grid is emerging as a very promising infrastructure for high-performance distributed computing. In this paper we introduce a software architecture for parallel and distributed knowledge discovery (PDKD) systems that is built on top of computational grid services that provide dependable, consistent, and pervasive access to high-end computational resources. The proposed architecture uses the grid services and defines a set of additional layers to implement the services of distributed knowledge discovery process on grid-connected sequential or parallel computers.

Parallel Nonlinear Optimisation for Decision Making Under Uncertainty

This paper presents an overview of our current research on parallel nonlinear optimization for decision making under uncertainty at Imperial College, London. We are interested in the optimization of nonlinear systems in which some of the key parameters are subject to uncertainty characterised by continuous probability density functions with general algebraic and/or differential-algebraic models (equality constraints) and linear/nonlinear inequality constraints, both affected by the uncertainty. The research has resulted in the development of riskaverse (robust) formulations within the meanvariance optimization model capable of controlling the effects of uncertainty. The formulations enable the user to select the level of risk she/he is prepared to take as well as the feasibility requirements of the nonlinear stochastic inequality constraints. The paper also describes the use of high performance computing in the optimization process. This can be divided into parallel algorithms for spe..