Process discovery method for distributed software systems with web interface

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