The process cube notion offers a wide range of new research questions and challenges. We will not enumerate them in this section. Instead, we give some points of reference for improving and extending the current approach.
Data mining for Construction of Hierarchies
Hierarchies are one of the most powerful elements of the OLAP structures. In our tool, the hierarchy feature is supported only for dimensions with time values. However, meaningful hierarchical structures can be also constructed for other types of dimensions. Machine learn- ing techniques can be applied in obtaining clusters of dimension elements that can be used to create a hierarchy, e.g., hierarchical clustering. Moreover, data mining techniques can be used to combine elements of multiple dimensions to create a single dimension. That can be accomplished by a meaningful partitioning of the elements, e.g., algorithms for partitioning, for instance, large categorical data exist [35].
Reuse of Precomputed Models
Knowledge of the discovered processes can be reused, by storing this precomputed infor- mation, not only creating models on-the-fly. Since producing large models on-the-fly takes
time, performance can be improved by saving parts of the created models or aggregates of the entire models, for further reuse.
Further Visualization Improvement
The visualization proposed in this thesis is based on the simple, traditional 2D visualization. Undoubtedly, more advanced visualization techniques can be found, with the advantage of being more representative for analysis and more user-friendly. Such an example is the icicle plot construction [32], that can be used to enhance the hierarchical representation of dimensions and facilitate the comparison between two sub-processes.
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