Image copyright by Professor Wil van der Aalst, Eindhoven University of Technology
I am taking process mining to a different arena, using the basic methodology and event logs. I understand the necessity for well defined proces
ses in relation to things like ISO 9001 and quality management and the achievement of Six Sigma. I started my career as a circuit designer in a military electronics shop with a major designer/manufacturer and not only did we have to have incredibly good yields from the fab shop, but we had to have reliable equipment that we sold to NATO forces. Process improvement involved saving time, money and resources and creating optimal performance.
However I have moved on and now I am using process discovery in the opposite sense -- in e-Commerce to improve revenue streams. Essentially, we have a captive platform where self-identified industry insiders buy and sell to each other on a wholesale level. Our platform has several areas where our clients spend time. They can create trusted buyer zones with their circle of buyers and sellers (platform enabled geo-location). They can create packages and offer them for sale to platform escalated groups. They can invoke software robots to buy and sell for them. They can offer and buy and sell from classified listings. In short, we want to map the processes of how our customers use our platform, and hence optimize the UIX or User Interface Experience to maximize revenue.
We have event logs and timestamps for everything, from when they log in, to when they change their buyer/seller groups, to when they consign inventory, or make offers and counter offers, or do browse the listings. However the event logs and time stamps are not located in one database table. The challenge was to create an effective case id to tie the disparate event logs together. Luckily our platform is based on Java Classes, Java Server Pages, Facelets, Taglets and the whole J2EE environment. As a result, we have a discrete session which is serializable and by simply altering all the event logs by caching the system-generated session id keyed to each event in the disparate event logs, we will have created a powerful customer analysis tool on our platform.
This will enable us to take things a step further. You have heard of responsive UIX designs to adapt to whatever device is utilizing the platform. The process discovery outlined above with enable us to push the boundaries of responsive design to create a machine-customized UIX that facilitates customer behavior on our platform to maximize revenue stream. Each customer will have a process map based on past behavior, and that process map will generate the UIX with a custom menu, that will be different for each customer types.
Our previous datamining looked for relationships between product groups and buying behavior. It looked at time-domain factors and essentially all sorts of data-dimensions and Bayesian inference of the interrelationships between those dimensions, to enhance revenue stream.
I realize that this doesn't exactly fit into the accepted semantics of a what a process is in the context of this course, but in a larger sense, we are discovering the buying process or the behavior processes on a trade platform that leads to facilitating buying behavior in our users. It adds event processing to our data mining, and this is where this course adds value for me.