Extracting relevant knowledge for decision makers is the key activity in all Business Intelligence areas. Different areas provide capabilities with which we can extract different knowledge from our data. For example, we can discover patterns, clusters and etc. using data mining. An example of pattern mining techniques is sequence mining, where we aim to discover the sequential order of events that happens in a data. Despite the wide use of sequence mining in different areas like biology, these algorithms have not been employed much to add values to business domains. The reason might be the complexity that exists in businesses and the way that we handle our businesses. Many businesses are running through a formal or informal agenda, called business processes. These processes define how different activities in a business process should be handled to fulfill the goal of the business. The relation between these activities can be quite complex, so the analysis of their data could be quite challenging. Let's see the situation with some example.
Imagine that we have a company consists of several informal business processes. This means that we have not modeled our processes, and people know it by heart. If we want to define the business process, we can interview different people who are involved. This is very costly, and it can be biased based on the information that people give to us. We should always consider the probability that not all people tell the way that they work; instead, many might tell the way that they should work! Although we do not have formal business process models, we have the result of execution of our business in different Information Systems. This includes different databases that record different activities, or different log files that persist different actions through time. Business Process Discovery is a sub-area of the process mining that aims to discover business process models through these information. It offers a different algorithm that enables us to discover these models from captured information.
Interesting? Yes! but it is not the end of the story!
Imagine that you have a formal business process model, and the information that records the activities that happened in your business. How can you make sure if what is happening in your business is complying with what you have defined in business process model? Is there any fraud case? Is there any employee who does not know the work but (s)he doesn't know! These sort of questions can be answered if we are able to compare our process model with the information that has been captured in the log files. This is another area of Process Mining which is called Conformance checking.
Wow! so far so good! Can we expect more from Process Mining?
Off course! business processes can capture many different perspectives. For example, they can be so basic that only describes which activity should be performed when! but they can be extended to explain who should perform each activity! Consider a company that has a basic process model. The company might not be able to define who should perform each task at beginning. Instead, the manager lets people work, and after a while (s)he wants to assign people to different activities based on the successful experiences of running the business process. I am sure you are sharp enough to realize that this information are already captured in our databases and log files! so, can we give the basic version of the process model and our log file to an algorithm and expect to receive a evolved version of the business process capturing who should do each activity? Off course! It is called Process Enhancement!
That is amazing! Can we be even more greedy to expect even more?
Sure! The good news is that we can combine Process Mining with other Mining techniques like Rule Mining and etc to expand the power of our magic! If you are interested to know more, there is a research group at the Eindhoven University of Technology that conducts this project, you can find more information on their site.
Today is the third year of this blog! I will get the data mining course next term, and I am very eager to learn and perform it in different contexts.