# Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining
## 3 Methods
### New Methodology
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Activity** & **Description** & **Guidelines** \\ \hline \multirow{6}{*}{4.1: Select Data Analysis Techniques} & \multirow{6}{*}{Select statistical analysis and data mining techniques and tools.} & - & It is fundamental at this stage to have knowledge about the different types of analysis, to correctly apply them. Applying incorrect analysis to just obtain good visual models will not necessarily help with answering the frequently posed question correctly. & \\  & & - & Not only complex models and techniques are required to provide answers to FPQ exploratory analysis of the data must be executed first. \\ \hline \multirow{6}{*}{4.2: Statistical Analysis} & \multirow{6}{*}{Used to characterize an event log, identifying the frequency of activities, the distribution of cases over time and variants of process execution, among others.} & - & Have an understanding of the statistical and descriptive methods to be applied. \\  & & - & Verify access to the required tools. \\ \hline \multirow{6}{*}{4.3: Data Mining Analysis} & \multirow{6}{*}{Discovering different patterns and knowledge on data contained in the event logs.} & - & Identify the objective to achieve with the data mining techniques. Understanding the objective will provide guidance on which technique and tool to use. \\  & & - & Evaluate previous studies to check their results and see if they are applicable to replicate them. \\ \cline{1-1}  & & - & Make sure to have access, license agreements and the computational resources needed to execute the tools and their included analysis. \\ \cline{1-1}  & & - & Always check for additional libraries with newer techniques added to the tools. This may be an opportunity to apply a new technique to your analysis. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Guidelines for the data analysis stage. **Activity 4.1**. : Select data analysis techniques:  
The data analysis stage includes two possible types of analysis: statistical analysis and data mining analysis. These analyses are executed according to the relevant requirements for answering the specific question posed by the experts. There are types of analysis for specific questions in which only an exploratory statistical analysis is needed using tools, such as Excel (products.office.com/en-us/excel) or Disco (fluxicon.com/disco), and questions that require the use of both statistical analysis and data mining tools. First, it is required to select the analysis techniques based on the expected outcomes. Outcomes may include a graphical model with data and information about the episodes, or an event log clustered into several sub-event logs. Second, it is required to identify the tools that allow one to perform the chosen techniques. **Activity 4.2**. : Statistical analysis:  
Statistical analysis is used to characterize an event log, identifying the frequency of activities, the distribution of cases over time and variants of process execution, among others. It provides a holistic view of the process from a quantitative perspective and acts as a first step to answering any question. No specific algorithms are associated with this analysis; however, it can be performed using a variety of tools. For example, Disco is more process-oriented, while Excel is more data-oriented. Excel can be useful when the size and amount of data are manageable; but more complex big data solutions may be needed when the size grows, and this is not supported by Excel. **Activity 4.3**. : Data mining analysis:  
Data mining analysis relates to the process of discovering different patterns and knowledge on datasets. There are multiple techniques taken from diverse domains that are applied in data mining in order to obtain the desired results, including, for example, visualization techniques, machine learning, classification algorithms and clustering, among others [28].