Using Subsequence Mining to Identify Business Processes in Data Networks

Master's Thesis from the year 2016 in the subject Computer Science - Commercial Information Technology, grade: -, Hamburg University of Technology (TUHH; Universitat zu Lubeck), language: English, abstract: To manage business processes, companies must previously define, configure, implementand enact them. Analysts try to identify companies business processes. However, largecompanies might have complex business processs (BPs) and consist of many businessunits. Therefore, classical business process modelling hardly scales. Both, companies andanalysts are interested in automated approaches for business process modelling, savingtime and money. Today s business process analysts often use process mining techniques toextract company s business processes by analyzing event logs of applications. This techniquehas its limitations, and is strongly dependent on the kind of log files of deployedapplications. By designing our mission oriented network analysis (MONA) approach usingalgorithms having polynomial complexity, we show that identification of business processesis tractable. Identification of related tasks which constitute business processes is basedon analysis of communication patterns in network traffic. We assume that today s businessprocesses are based on network-aided applications. Our software presents identifiedbusiness processes using business process modelling notation.