Developing process mining algorithms for finding meaningful patterns

Process mining is a technique for extracting knowledge from event logs recorded by an information system. In the process discovery phase of process mining, a process model is constructed to represent the business processes systematically and to give a general opinion about the progressive of processes in the event log. Considering in advance the trend and different features of running process is important. Especially, time management is crucial in designing and conducting business processes. Every day information systems collect different kind of process instances of a business flow. As time goes on, size of collected data builds up speedily and constitutes a huge volume of data. It is a very challenging task to obtain valuable information and features of processes from such a large volume of data. This thesis proposes a novel algorithm, Interactive Process Miner (IPM), to create process model based on event logs and, also a new approach that contains three different features; including activity deletion, aggregation and addition operations on the existing process model. The proposed algorithm, IPM, is enhanced by introducing time perspective. Time-oriented IPM algorithm, T-IPM, is capable of predicting the remaining and completion time of each process in a flow. This thesis also includes the development of a new process mining tool, ProLab, in order to work on large volume of event logs and to handle the execution records of running process instances. Experimental studies demonstrate the capability of IPM and T-IPM algorithms and, also ProLab tool on both real-life and experimental datasets, including low memory usage, modification opportunity and improvement in performance compared to the existing algorithms.