# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 2 Fundamentals  
Before defining the research questions pursued in the SLR, some definitions are presented here. These definitions may enable the reader to be informed about fundamental terminology used throughout the rest of the paper. Definitions 1 and 2 are essential to understand the essence of process mining in terms of its inputs and outputs, and hence how different concept drift analysis approaches differ in terms of input artefacts they use. Definition 3 defines different process aspects considered by a concept drift analysis approach. These aspects characterise any business process no matter which representation is used for this process. Definition 4 contrasts two different representations of a business process. One representation considers the effect of time dimension in differentiating resulting process models; whereas the other considers the effect of different execution mechanisms in the differentiation. Definition 5 is essential for understanding a middle artefact used in an approach surveyed in the SLR. Definition 6 is needed to give the reader an introduction about a fundamental artefact that each concept drift approach is relying on (no matter whether it is given as an input or constructed by the approach) to study relations between different activities or events executed in the context of a business process. As a result of studying patterns and inter-activities relations, different versions of the same business process can be compared. Definition 7 provides an introduction to different forms of deriving and representing a business process. **Definition 1** (Normative and descriptive process models). : _A normative process model describes an ideal view on how the activities of a business process shall be performed. It is used for defining how a business process will be executed, and afterwards it is used for monitoring and checking conformance of the running process with the process model. A descriptive model in turn, is specified to capture reality and describe how activities are exactly carried out. In the context of process mining, discovered process models are descriptive._  
**Definition 2** (Event log, event stream). : _An event log records the actual execution of a process. Each log entry, that is, each case, corresponds to a specific process instance._  
A case consists of ordered events, each one having one or more attributes, for example, activity name, time, cost, and resource. Each event refers to a single case and may share similarities with other events from other cases, but two events do not have exactly the same values of all associated attributes. Finally, not all events have the same set of attributes, but events referring to the same activity should have the same set of attributes. XES and MXML are well-known formats for representing an event log [2]. Table 1 provides an example of an event log. The case ID column represents the process instance ID. Each case is represented by multiple rows corresponding to the number of events occurred from the start till the completion of this case. Note that in this article, we use the terms _process instance_, _Trace_, and _case_ interchangeably to refer to the same concept. However, we tend to use the term _case_ whenever we refer to the data view of a business process. Whereas, we use the term _trace_ whenever we tend to describe a concept or a related technique concerned with the logical view of a business process. We use the term _process instance_ whenever we are concerned with an idea related to the conceptual view of a business process. Each event is recorded by one row with a unique identifier (Event ID column). For any event, it is captured when it occurred (Timestamp column), which activity class it belongs to (activity column), which resource was responsible for this event (Resource column), and which data are associated with it (Cost column).