# A Statistical Approach to Discovering Process Regime Shifts and Their Determinants
## 2 Literature Review  
Process mining initially emerged as a workflow mining technique to extract a process model from software engineering data [21; 22]; other aspects such as process cost, risk reduction, maximizing productivity, resource utilization and improving quality have progressively gained attention [23]. Although this makes the process mining landscape quite extensive with multiple recent literature review papers available providing a comprehensive account [15; 23; 24], we mainly discuss those works that help us position our contribution within the extant literature. Moreover, we focus on identifying the application context, the methodology and/or tools used and the aim in terms of process discovery, conformance or enhancement within those works. [PERSON] et al. [25] used smart meter readings to discover anomalous customer behavior in energy usage over time using Hamming distance and cosine similarity. In another paper, [PERSON] et al. [26] applied process mining on call traces to characterize smartphoneapplications for malware detection. [PERSON] et al. [27] investigated cyberattacks in industrial control systems by comparing five algorithms to create an accurate yet simple process model and recommended Inductive Miner as the most suitable algorithm. [PERSON] et al. [28] introduced genetic process mining to detect deviant user behavior in social media websites. They applied their study on Facebook users by first generating a process model for normal user behavior and then by identifying the abnormal behavior by conformance checking. For web application security, [PERSON] et al. [29] proposed Aegis--a tool to process mine a target web application to discover its workflow model and consequently enhance its security policies. In the same context, [PERSON] et al. [30], using model-driven engineering and process mining, applied Unified Modeling Language to generate the formal model and ProM visualization techniques for deviation identification [31]. In the software development arena, [PERSON] and [PERSON] [32] used process mining as an aid for agile (human-centric) development. In contrast, [PERSON] et al. [33] used it for software maintenance or enhancement. In pre-production software quality assurance, a common process mining-based research trend is either error or bug minimization or their detection. To enhance software reliability, the recent works of [PERSON] et al. [34] and [PERSON] et al. [35] focused on identifying errors or bugs, and [PERSON] [36] focused on generating test cases for conformance checking. In healthcare, one of the early papers by [PERSON] et al. [37] used process discovery for resource analysis and conformance to clinical guidelines. Later, for role description and collaboration among hospital emergency department professionals, [PERSON] [38] and [PERSON] et al. [39] proved the usefulness of Fuzzy Miner and MinAdept algorithms, respectively, to deal with inherent unstructured characteristics of healthcare process models. In emergency medicine, process mining has recently been applied for the initial evaluation and diagnosis of accidental injuries, unexpected diseases and coordination among care providers for surgical and non-surgical treatments [40; 41; 42]. [PERSON] et al. [43] demonstrated process discovery and conformance checking in education to identify student behavior using the ProM tool. In the same domain, [PERSON] et al. [44] discovered behavioral patterns and rules for personalized learning with Web Ontology Language and Semantic Web Rule Language. For improving student learning in a university environment, [PERSON] et al. [45] used the SoftLearn tool to analyze student flows based on social networks with a graphical interface. In the business domain, process mining aims to fill the gap between data mining and business process management [46].