# Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
## 8 Conclusions  
This paper contributes a data preprocessing method (CPA-PM) for event logs generated by cloud-based information systems, with an emphasis on clickstream data. To properly apply process mining on such logs, advanced and iterative preprocessing must be done in order simplify the resulting mined processes without losing valuable information. It is also important that the preprocessing steps be repeatable. CPA-PM includes different steps needed to clean and restructure cloud-based event logs and address relevant challenges in that area (especially #2 to #9 in Section 2), including a large number of events and attributes, spurious and repetitive events, and unstructured orderings of events. As an additional contribution, the method is supported by a new API with an implementation in R, which enable the scripting and reuse of preprocessing steps. CPA-PM goes beyond what is usually found in ETL tools and in the preprocessing capabilities of process mining tools. There are many opportunities to extend this research work, some of which is identified in the previous section. We also plan to extend the CPA-PM method to include more functions for preprocessing event logs, provide a Python implementation, and integrate the API to an existing PM tool. We are currently exploring an integration with the free PM4 PY tool, but as this API is really for preprocessing logs, any PM tool can benefit from our approach, including commercial solutions such as Celonis [31], Signavio [32], UiPath Process Mining [33], and Apromore [34]. Additionally, more case studies can be done to apply the method on event logs that represent user actions while navigating the different parts of an application, or different application domains. The integration of similarity and information loss metrics, together with support for multiple, different logging systems in cloud environments, would also be beneficial. The usability of the API also deserves further attention in the future  
Conceptualization, N.M.E.-G. and D.A. ; methodology, N.M.E.-G. and D.A. ; software, N.M.E.-G.; validation, N.M.E.-G. and D.A. ; formal analysis, N.M.E.-G.; investigation, N.M.E.-G. and D.A. ; resources, N.M.E.-G.; data curation, N.M.E.-G.; writing--original draft preparation, N.M.E.-G. and D.A. ; writing--review and editing, N.M.E.-G. and D.A. ; visualization, N.M.E.-G.; supervision, D.A. ; project administration, N.M.E.-G. and D.A. ; funding acquisition, D.A. All authors have read and agreed to the published version of the manuscript. This research was funded by the University of Ottawa and the Ontario Graduate Scholarship. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The R implementation of the API and an example are freely available online at: [[https://github.com/[PERSON]/CPA_PM](https://github.com/[PERSON]/CPA_PM)]([https://github.com/[PERSON]/CPA_PM](https://github.com/[PERSON]/CPA_PM)), accessed on 13 April 2022. Acknowledgments: The authors thank [PERSON] and [PERSON] for their help in providing event logs and assessing the resulting process models. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript: API Application Programming Interface CPA-PM Cloud Pattern API - Process Mining CSV Comma-Separated Values DSR Design Science Research ETL Extract Transform, and Load PM Process Mining SaaS Cloud Pattern API - Process Mining References
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