Objectworlds : a class of computer-based discovery learning environments

It is possible to discern a class of Computer-Based Discovery Learning Environments which centre on novel, concept rich,simulated objects and which include simple but general functions with which the objects may be manipulated. This thesis provides a history of this class of environments, which we call objectworlds, and we also give them a strict definition. We describe Gravitas, a new objectworld we have built, which allows learners to work with objects that behave like gravitating masses moving in a two dimensional space.Gravitas contains a powerful programmable interface to the objects, in the form of a set of Logo commands, and a functionally equivalent but easier to use graphical interface which is controlled by the mouse. We show that the combination of interfaces helps learners to explore the world of these objects more effectively.We contrast the educational experiences learners are afforded by objectworlds with those offered by two closely related kinds of Discovery Learning Environment: Simulations and Modelling Systems. We also describe a psychological framework which provides a useful way of thinking about the construction of computer simulated objects for discovery learning applications

Architecture and Design of an XML Application Platform

this report, we introduce the XML Application Platform (XAP), which aims to address this need. The XAP is shown to have wide application to any domain where XML processing is encountered. We also describe a research prototype we have built called Dexter. Dexter provides execution of XML workflows to achieve complex XML transformations whilst providing many low- level support features such as caching and resource pooling. Several examples are shown to introduce readers to typical usages and to illustrate the workflow language that was developed for creating applications within Dexte

Subgroup Discovery in Smart Electricity Meter Data

This work presents data mining methods for discovering unusual consumption patterns and their associated descriptive models from smart electricity meter data. At present, data mining and knowledge discovery in electricity meter data suffer from three notable weaknesses: 1) insufficient focus on intelligent data analysis of subgroups (subsets) whose patterns vary significantly from aggregate patterns embodied in an entire dataset; 2) a lack of effort towards generating intuitively understandable and practically applicable knowledge for industrial practitioners to identify such subgroups; and 3) limited knowledge regarding the link between unusual consumption patterns and household consumers' socio-demographic characteristics. This paper addresses these practically important but technically challenging issues by applying subgroup discovery algorithms to a real smart electricity meter dataset. Subgroups whose patterns are unusual and whose sizes are large enough are discovered, and their descriptive and predictive models are generated. Furthermore, to enrich subgroup discovery algorithms, three new-quality measures for real-valued targets are proposed. The comparative studies empirically evaluate the effectiveness and usefulness of subgroup discovery on classification accuracy, predictive power, and computational resources. The methodologies and algorithms presented are generic, and therefore applicable to a wider range of data mining problems