Realizing a process cube allowing for the comparison of event data

Abstract

Continuous efforts to improve processes, require a deep understanding of process inner working. In this context, the process mining discipline aims at discovering process behavior from historical records, i.e., event logs. Process mining results can be used for analysis of process dynamics. However, mining on realistic event logs is difficult due to complex interdependencies within a process. Therefore, to gain more in-depth knowledge about a certain process, it can be split into subprocesses, which can then be separately analysed and compared. Typical tools for process mining, e.g., ProM, are designed to handle a single event log at a time, which does not particularly facilitate the comparison of multiple processes. To tackle this issue, Van der Aalst proposed in [4] to organize the event log in a cubic data structure, called process cube, with a selection of the event attributes forming the dimensions of the cube. Although, multidimensional data structures are already employed in various business intelligence tools, the data used has a static character. This is in stark contrast to process mining, since event data characterizes a dynamic process that evolves in time. The aim of this thesis is to develop a framework that supports the construction of the process cube and permits multidimensional filtering on it, in order to separate subcubes for further processing. We start with the OLAP foundation and reformulate its corresponding operations for event logs. Moreover, the semantics of a traditional OLAP aggregate are changed. Numerical aggregates are substituted by sublog data. With these adjustments, a tool is developed and integrated as a plugin in ProM to support the aforementioned operations on the event logs. The user can unload sublogs from the process cube, give them as parameters to other plug-ins in ProM and visualize different results simultaneously. During the development of the tool, we had to deal with a shortcoming of the multidimensional database technologies when storing event logs, i.e., the sparsity of the resulted process cube. Sparsity in multidimensional data structures occurs when a large number of cells in a cube are empty, i.e., there are missing data values at the intersection of dimensions. Taking a single attribute of an event log as a dimension in the process cube results in a very sparse multidimensional data structure. As a result, the computational time required to unload a sublog for processing increases dramatically. This shortcoming was addressed by designing a hybrid database structure that combines a high-speed in-memory multidimensional database with a sparsity-immune relational database. Within this solution, only a subset of event attributes actually contribute to the construction of the process, whereas the rest are stored in the relational database and used further only for event log reconstruction. The hybrid database solution proved to provide the flexibility needed for real-life logs, while keeping response times acceptable for efficient user interaction. The applicability of the tool was demonstrated using two event log examples, a synthetic event log and a real-life event log from the CoSeLog project. The thesis concludes with a detailed loading and unloading performance analysis of the developed hybrid structure, for different database configurations.

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