# Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
## 1 Introduction  
Organizations use information systems to support an increasing number of business processes, whereby a process is a set of related activities that happen over time in order to achieve a goal [1]. Many such systems record what happens in running processes as event logs that contain information such as process instances, activities, timestamps, and resources. Event logs form a large amount of data ready to be exploited through process mining (PM), which aims to discover processes to improve process understanding, compliance, and quality aspects based on evidence [2]. PM is often needed because concrete processes are often more complex than their idealized process definitions or models. In this context, PM provides evidence-based process descriptions that cover all the process variations that are used by the organization, many of which being often ignored by the idealized process models. The interest in information systems exploiting cloud computing is also rapidly growing in the industry and research communities [3]. The number of applications developed in a cloud environment is increasing because of benefits that a cloud infrastructure offers, especially regarding scalability, availability, and maintainability [4]. Clickstream data is used to represent the electronic record of a user's behavior on Web-based information systems and cloud applications [5]. This data shows the path a user takes while navigating the website or application. This path reflects the different choices that the user takes while navigating. Given that clickstream event logs might include a record of every page visited by the user, they usually form large amounts of behavioral data. Such logs provide opportunities for a detailed look at the decision-making processof users. When applying process mining techniques on such data, process maps can be generated that represent the user actions on web/cloud applications. These process maps give detailed information about users' navigation habits. It is important to visualize and understand user actions while interacting with a cloud application so that the latter be engineered to better satisfy user needs and ensure that users come back to the application. In order to reduce the complexity of raw event logs, [PERSON] et al. [6] introduced several pattern-based approaches to improve the process models discovery by preprocessing the event log datasets for the purpose of applying process mining techniques. Unfortunately, the complexity of clickstream data and other event logs from cloud-based applications is typically too difficult to handle by process mining tools, out of the box. A research challenge here includes how to reduce, in a repeatable way, log complexity caused by numerous cloud event attributes, action orderings (e.g., clicks and refreshes), and low-level events. Our research objective is to develop and assess a method to preprocess event logs collected from cloud-based applications to discover user actions and processes. A secondary objective is to examine how process mining can be applied on clickstream data collected from software-as-a-service (SaaS) applications and discover users' behavioral characteristics. A suitable method should provide high automation (e.g., by being scriptable), high abstraction, high scalability, and low effort to accommodate changes frequently occurring in cloud-based logging infrastructures. The focus here is also on advanced preprocessing of event logs, where the preprocessed logs can then be fed to many existing process mining tools for visualization and analysis. This research was conducted following design science research in information systems [7]. This methodology consists of five iterative steps, (1) awareness of problem, (2) suggestion, (3) development, (4) evaluation, and (5) conclusion, that are illustrated throughout the different sections in this paper.