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Knowledge discovery for Deloitte invision webservices

Nieuwboer, F.W. van den (2006) Knowledge discovery for Deloitte invision webservices. Master's Thesis / Essay, Computing Science.

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Abstract

In many systems log information concerning event timing is available. Information about the performance of the system is concealed within this log information. This thesis describes a method to extract the performance information, using data mining techniques. In order to extract performance information, we need to detect at which moment a system reacts slow. The first technique applied to detect outliers is anomaly detection. We make use of an algorithm called Smartsifter, which uses Gaussian Mixture Models to maintain models of a distribution of the input data. Each time a new data point is added, the model is updated. A score is calculated out of the change of the model, if this score is above some threshold, an outher is reported. The next step is to classify the concurrent situations. Using Relevance Learning Vector Quantization we create so-called prototype vectors and relevance vectors, from which we can deduct which events are important for which class membership. We implemented this framework in a prototype application, and set it to work at a 40 gigabyte large database of time log information, which was extracted from the Deloitte IN Vision framework. From the results of the prototype application we can conclude that we can implement a performance measure framework using data mining techniques, which extracts system performance information from log timing information. For Deloitte INVision, a better timeout procedure can be developped using this technique.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Computing Science
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:30
Last Modified: 15 Feb 2018 07:30
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/8902

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