Organizations are nowadays storing huge amounts of data related to various business processes. Process mining provides different methods and techniques to analyze and improve these processes. This allows companies to gain a competitive advantage. Process mining initiated with the discovery of work-flow models from event data. However, over the past 20 years, the process mining field has evolved into a broad and diverse research discipline.
There is no doubt about the current role of Machine Learning in the fascinating world of Business Intelligence. Predicting whether a customer will be loyal to the company or not, understanding customers’ behavior or anticipating market fluctuations are typical examples on which Machine Learning may be pivotal. Unfortunately, most successful Machine Learning algorithms like Random Forests, Neural Networks or Support Vector Machines do not provide any mechanism to explain how they arrived at a particular conclusion and behave like a “black box”. This means that they are neither transparent nor interpretable. We could understand transparency as the algorithm’s ability to explain its reasoning mechanism, while interpretability refers to the algorithm’s ability to explain the semantics behind the problem domain.
Teaching R to students with little to no experience in programming or data analysis is a challenging task. Our talk at useR!2017 showed how different ingredients of our course Exploratory and Descriptive Data Analysis at UHasselt are used to facilitate the learning of R.
Firstly, the educational environment at UHasselt, based on guided self-study and the use of small group working sessions, allows each student to have an individual pace of learning and gives them frequent feedback.
Currently, Professor Leticia Arco García of the Central University of Las Villas (Cuba) is visiting our research group. As a member of the Computer Science Department of the Artificial Intelligence Lab, she is an expert within the domain of text mining. Given her expertise and our curiosity, we asked her if she could give an introduction seminar. And so she did, by organizing not one but two seminars, one on text mining and one on opinion mining.
Traditionally, the students business informatics of UHasselt organise a biennial trip to visit leading-edge technology companies around the world. This year, our group had the pleasure to visit the Intel facilities in Dublin. For sure, Intel did a wonderful job in having us. The staff showed real commitment and organised a very interesting day filled with talks, demonstrations and tours.
The problem we are addressing originates at a transportation company. When analyzing the results of their shipments, they would like to break this down into categories. For example, how many packages contain clothing and how many contain laptops, and does different content lead to different results during shipment. Instead of having to manually label all these packages, we look for a more efficient solution. The goal of this blog post is to show you the steps we took to solve this problem using machine learning. Steps that can be followed when you undertake your own machine learning projects.
The world of education is changing more than ever. Since the emergence of the internet, platforms for e-learning are becoming omnipresent. The use of video lectures and other online learning materials are rapidly replacing traditional lectures and textbooks. As learning is occurring more and more online, teachers are facing new challenges. The increasing distance between student and instructor asks for new tools to track and adjust learning activities.
The huge potential of process mining applications is -luckily- already discovered in a variety of business settings. In industry, more and more companies are learning about its potential value. In meanwhile, academic researchers continue their quest to the best algorithm, the most meaningful metrics, the most understandable visualisations, etcetera. Whatever ‘best’, ‘meaningful’, and ‘understandable’ may be… These are food for thought and discussion on their own. But I’d like to address a different mini-research-topic-on-its-own: the event log.
An implicit assumption in process mining (both research and applications), is the existence of an event log.
Last week, the business informatics group was happy to invite Hugo de Groot, an agile coach at Cegeka, for a workshop about “Agile project management in higher education”.
In the context of an innovative education project that was earned by our research group, Marijke Swennen, one of our researchers, started in October 2016 with the introduction of agile project management at Hasselt University.
The picture above shows a list of possible actions that might be going on in an organization. As a process modeler, would you include all of them as activities in your diagram? Or do you think some are too detailed or rather not detailed enough to be considered an activity? Maybe you even argue that some of them are not relevant enough to figure in a process at all…