The mining and visualisation of application services data

Many network monitoring tools do not provide sufficiently in-depth and useful reports on network usage, particularly in the domain of application services data. The optimisation of network performance is only possible if the networks are monitored effectively. Techniques that identify patterns of network usage can assist in the successful monitoring of network performance. The main goal of this research was to propose a model to mine and visualise application services data in order to support effective network management. To demonstrate the effectiveness of the model, a prototype, called NetPatterns, was developed using data for the Integrated Tertiary Software (ITS) application service collected by a network monitoring tool on the NMMU South Campus network. Three data mining algorithms for application services data were identified for the proposed model. The data mining algorithms used are classification (decision tree), clustering (K-Means) and association (correlation). Classifying application services data serves to categorise combinations of network attributes to highlight areas of poor network performance. The clustering of network attributes serves to indicate sparse and dense regions within the application services data. Association indicates the existence of any interesting relationships between different network attributes. Three visualisation techniques were selected to visualise the results of the data mining algorithms. The visualisation techniques selected were the organisation chart, bubble chart and scatterplots. Colour and a variety of other visual cues are used to complement the selected visualisation techniques. The effectiveness and usefulness of NetPatterns was determined by means of user testing. The results of the evaluation clearly show that the participants were highly satisfied with the visualisation of network usage presented by NetPatterns. All participants successfully completed the prescribed tasks and indicated that NetPatterns is a useful tool for the analysis of network usage patterns

A Visualization Tool for Interactive Learning of Large Decision Trees

Decision tree induction is certainly among the most applicable learning techniques due to its power and simplicity. However learning decision trees from large datasets, particularly in data mining, is quite different from learning from small or moderately sized datasets. When learning from large datasets, decision tree induction programs often produce very large trees. How to visualize efficiently trees in the learning process, particularly large trees, is still questionable and currently requires efficient tools. This paper presents an visualization tool for interactive learning of large decision trees, that includes a new visualization technique called T2.5D (satnds for Trees 2.5 Dimensions). After a brief discussion on requirements for tree visualizers and related work, the paper focuses on presenting developing techniques for two issues: (1) how to visualize efficiently large decision trees; and (2) how to visualize decision trees in the learning process.AR8