Using Subsequence Mining to Identify Business Processes in Data Networks (PDF)

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