Although you might not have realized, processes take up an indispensable role in our daily lives. Your actions and those of others generate an extensive amount of data. Whether you are ordering a book, a train crosses a red light, or your thermostat heats your bathroom, every second millions of events are taking place which are stored in data centers around the world. These enormous sets of event data can be used to gain insight into processes in a virtually unlimited range of fields. However, the analysis of this data requires its own set of specific formats and techniques. This course will introduce you to process mining with R and demonstrate the different steps needed to analyze business processes.
Introduction to process analysisFree
The amount of event data has grown enormously during the last decades. A considerable amount of this data is recorded within the context of various business process. In this chapter, you will discover a methodology for analyzing process data, consisting of three stages: extraction, processing and analysis. You will have our first encounter with the specific elements of process data which are required for analysis, and have a first deep dive into the world of activities and traces, which will allow you to reveal of first glimpse of the process.Introduction to process analytics50 xpIdentify process elements50 xpProcess Analysis Workflow50 xpActivities as cornerstones of processes50 xpRetrieve basic process information100 xpExplore activities100 xpThe happy path100 xpCreate process maps100 xpComponents of process data50 xpEvent data identifiers50 xpConstructing event data objects100 xpInspecting event data objects50 xp
A process can be looked at from different angles: the control-flow, the performance and the organizational background. In this chapter, you will make a deep dive into each of these perspectives. The control-flow refers to the different ways in which the process can be executed, and thus, how it is structured. Considering performance, we are both interested in discovering how long things take, as well as when they take place. Finally, the organizational perspective looks at the actors in the process.Organizational analysis50 xpSpecialization of resources100 xpDetecting roles and specializations50 xpInvolvement of resources100 xpStructuredness50 xpProcess variants100 xpRework100 xpPrecedence matrix50 xpPerformance analysis50 xpEating habits100 xpEating habits - Dotted chart50 xpEat, sleep, repeat100 xpLinking perspectives50 xpThe right person on the job I100 xpThe right person on the job II50 xpThe right person on the job faster100 xp
Event data processing
Event data rarely comes in a form which is ready to analyze. Therefore, you often require a set of tools to get the data in the right shape, before we can answer our research question. At the end of this chapter, you will be familiar with three common preprocessing tasks: filtering data, aggregating events and enriching data.Filtering cases50 xpThe path well traveled100 xpThe downside of rejections100 xpThe downside of refusals100 xpFiltering events50 xpThe vicious disapproval circle100 xpWorking with high-paid R&D candidates100 xpAggreggating events50 xpDisapprovals are disapprovals100 xpZooming out100 xpAdmiring the view50 xpEnriching events50 xpEverything has a cost100 xpCost versus urgency100 xp
In this final chapter we will use everything we have learned so far to do and end-to-end analysis of an order-to-cash process. Firstly, we will transform data from various sources to an event log. Secondly, we will have a helicopter view of the process, exploring the dimensions of the data and the different activities, stages and flows in the process. Finally, we will combine preprocessing and analysis tools to formulate an answer to several research questions.Preparing the event data50 xpTransforming Sales Data (1/3)100 xpTransforming Sales Data (2/3)100 xpTransforming Sales Data (3/3)100 xpPutting the process together100 xpCreating the event log100 xpGetting to know the process50 xpExamining Traces100 xpZooming Out100 xpRoles and Rules50 xpResource Roles100 xpThe 4-Eye Principle100 xpControl-flow deviations100 xpFast Production, Fast Delivery50 xpThroughput time analysis100 xpCourse Recap50 xp
Gert JanssenswillenSee More
Author of bupaR package
Gert Janssenswillen is a PhD candidate at Hasselt University. His research focusses mainly on the quality measurement of discovered process models and inferential statistics in process mining. At Hasselt University, he teaches exploratory data analysis and business process analytics. As creator of the bupaR packages for business process analytics with R, he has introduced a framework for easy and reproducible analysis of event data.