Process discovery method for distributed software systems with web interface

2019;
: pp. 70-77
1
Lviv Polytechnic National University, Institute of Computer Sciences and Information Technologies, ACS Department
2
Lviv Polytechnic National University, Institute of Computer Sciences and Information Technologies, ACS Department

As an applied discipline, process mining emerged about two decades ago, and its methods have been increasingly used in practice for recent few years. What differentiates process mining from the conventional data mining is considering process nature of the analyzed data. Rapid development of the process mining software market niche has risen relevance of such task as scalability of process mining methods. Adaptation of the process discovery method, called Fuzzy Miner, to distributed software systems with web interface has been proposed by the authors. To address the scalability requirements, the calculation procedures are implemented on different part of the system: the most computer resource consuming algorithms are executed on the server side whilst less resource consuming calculations are placed on the client side. In turns, the server-side components belong either to the data layer or service layer. The data layer is accountable for storing event data in XES format and measuring process metrics. Building a process graph and communication with the client web application is the responsibility of the service layer. The purpose of the client-side web application is to render a process graph generated in the server-side. The calculation logic is covered with unit and integration tests so that its correctness is checked automatically. In order to reduce total cost of ownership of the system, it is implemented with free software. From the performed calculations and comparison of the outcomes with the results received by means of the existing ProM 6.8 plugins (Fuzzy Miner and Alpha++ Miner), it can be concluded that the proposed adaptation of the Fuzzy Miner method ensures representation of the behavior seen in an event log (like the ProM 6.8 plugins successfully do). In turns, from the software architecture standpoint, the proposed solution demonstrates better scalability characteristics, i.e., ability to increase capacity in order to handle bigger amount of event data, in comparison with the mentioned above ProM plugins.

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