No abstract

Information systems support and ensure the practical running of the most critical business processes. There exists (or can be reconstructed) a record (log) of the process running in the information system. Computer methods of data mining can be used for analysis of process data utilizing support techniques of machine learning and a complex network analysis. The analysis is usually provided based on quantitative parameters of the running process of the information system. It is not so usual to analyze behavior of the participants of the running process from the process log. Here, we show how data and process mining methods can be used for analyzing the running process and how participants behavior can be analyzed from the process log using network (community or cluster) analyses in the constructed complex network from the SAP business process log. This approach constructs a complex network from the process log in a given context and then finds communities or patterns in this network. Found communities or patterns are analyzed using knowledge of the business process and the environment in which the process operates. The results demonstrate the possibility to cover up not only the quantitative but also the qualitative relations (e.g., hidden behavior of participants) using the process log and specific knowledge of the business case.

Information systems support and ensure the practical running of most critical business processes. There exists or can be reconstructed a record (log) of the process running in the information system with information about the participants and the processed obj ects for most of the processes. This research was realized in the environment of the enterprise information system SAP. Participants of business processes stand in different relationships. We are interested in the relationships that are not explicitly seen from the process logs, but which are detectable by research methods of social networks and communities in social networks. Our work constructs the social network from the process log in the given context and then it finds communities in this network. Found communities were analyzed using knowledge of the business process and the environment in which the process operates. We found that identified communities have reasonable representation in the actual process, and this opened up a new dimension of knowledge that can be analyzed from the process log. This approach seems to be promising for detailed analysis.

Many decisions during making a project estimation and planning are based on previous experience and competency of a manager. Evaluated completed projects provide rich knowledge about similar decisions and reality. This paper focuses on the approach how to estimate value of specific parameters of the projects utilizing parameterized use case model. The proven methods are used -SOM for grouping similar use-cases and fuzzy rules for calculating the search parameter value. An important part of the approach there is to build up and maintain comparative database of valued use cases.