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Data mining on architecture simulation

Maden, Engin
Data mining is the process of extracting patterns from huge data. One of the branches in data mining is mining sequence data and here the data can be viewed as a sequence of events and each event has an associated time of occurrence. Sequence data is modelled using episodes and events are included in episodes. The aim of this thesis work is analysing architecture simulation output data by applying episode mining techniques, showing the previously known relationships between the events in architecture and providing an environment to predict the performance of a program in an architecture before executing the codes. One of the most important points here is the application area of episode mining techniques. Architecture simulation data is a new domain to apply these techniques and by using the results of these techniques making predictions about the performance of programs in an architecture before execution can be considered as a new approach. For this purpose, by implementing three episode mining techniques which are WINEPI approach, non-overlapping occurrence based approach and MINEPI approach a data mining tool has been developed. This tool has three main components. These are data pre-processor, episode miner and output analyser.