BPI cluster meeting 21Feb’18

Our BPI cluster meeting on 21st February will be a joint session with a DSC/e lecture by Marek Reformat.

Title: Fuzziness in Processing and Representation of Web Data


The web represents an immense repository of information. A number of sources of structured and unstructured data is growing every day. There is no doubt that our dependency on web data increases continuously. However, the increased amount of data – although recognized as a positive and beneficial fact – creates challenges regarding our ability to fully utilize that data. Such situation increases pressure as well as expectations for providing better ways of processing data available on the web.

Every day, users search the web for things of their interest. On multiple occasions they expect precise results. However, human’s curiosity and a need for being exposed to different and novel things is an important part of exploration processes. Existing systems supporting users in

 their search activities provide them with some variations, but it is not a controlled process. Diversity is accidental. In the first part of the presentation, we postulate that application of fuzziness in systems supporting users in their search will allow them to guide and control mechanisms that identify alternatives, and influence recommendations. Fuzzy-based methods can be applied to scenarios where users want to relax their requirements. Here, we concentrate on social networks. A methodology for selecting groups of individuals that satisfy linguistically described requirements regarding a degree of matching between users’ interests and collective interests of groups is presented. Additionally, we describe a simple fuzzy-based recommending approach that aims at constructing lists of suggested items. This is accomplished via explicit control of requirements regarding rigorousness of identifying users who become a reference base for generated suggestions.

A novel graph-based data representation format becomes an attractive and important way of storing data. It leads to better utilization of information stored and available on the web. High connectedness of such representation provides a means to create methods and techniques that can assimilate new data and build knowledge-like data structures. Such procedures resemble a human-like way of dealing with information. One of the most popular graph-based data formats is called the Resource Description Framework (RDF). It is a data format introduced together with the concept of Semantic Web. In the second part of the presentation, we present a process of assimilating information from multiple sources of RDF data. A newly proposed form of participatory learning using propositions provides an approximate reasoning-based approach to integrate previously unknown information with already known facts. We show how participatory learning has been adapted to integrating new information represented as relations. The approach recognizes two types of variables: conjunctive and disjunctive, that are common for knowledge graphs existing on the web.

The details of the above methodologies are presented, and multiple examples illustrating behavior of the processes are provided.


Marek Reformat (IEEE SM’05) received the M.Sc. degree (Hons.) from the Technical University of Poznan, Poznan, Poland, and the Ph.D. degree from the University of Manitoba, Winnipeg, MB, Canada. He is currently a Professor with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. The goal of his research activities is to develop methods and techniques for intelligent data modeling and analysis leading to translation of data into knowledge, as well as to design systems that possess abilities to imitate different aspects of human behavior. In this context, the concepts of computational intelligence—with fuzzy computing and possibility theory in particular—are key elements necessary for capturing relationships between pieces of data and knowledge, and for mimicking human ways of reasoning about opinions and facts. He also works on computational intelligence-based approaches for dealing with information stored on the web. He applies elements of fuzzy sets to social networks, linked data, and Semantic Web in order to handle inherently imprecise information, and provide users with unique facts retrieved from the data. All his activities focus on introduction of human aspects to web and software systems which will lead to the development of more human-aware and human-like systems.

You are all kindly invited to this joint session!

Date:21 February

Time:12:30 – 13:30

Remarks:Doors open at 12:00h

Location:TU/e Luna building, Corona room (Koepelzaal)

Registration Link


BPI cluster meeting 7Feb’18

The student who will present is Evertjan Peer. He conducts a joint master project for Operations Management & Logistics and Data Science in Engineering (which is a special track of Computer Science & Engineering). From the faculty IE&IS he is supervised by Yingqian Zhang. Vlado Menkovski is his first supervisor from the Computer Science faculty.


Solving the Train Unit Shunting Problem: A Deep Reinforcement Learning Approach.



The Train Unit Shunting Problem is a complex task which consists of planning train movements and cleaning/maintenance tasks on shunting yards. Current solution techniques fall short by either having a long runtime (linear programs), or producing non-intuitive solutions (local search). In this thesis I investigate whether recent successes of Deep Reinforcement Learning in solving the game of Go, and playing Atari games, can be brought to this real life planning problem. Could a Deep Reinforcement Learning agent build up experience about what good moves are when handling trains on the shunting yard?  An iterative procedure is followed in which the problem formulation complexity is gradually increased. I will introduce Deep Reinforcement Learning (more specifically the Deep Q-Network) and discuss my experiences so far in applying these techniques to planning problems.

You are kindly invited to this presentation!


BPI cluster meeting 10Jan’18

Miranti Rahmani will give a talk during our session. She is a master student of Prof. Uzay Kaymak.

Feature Selection for Predictive Medical Decision Models

This project is part of master graduation project, which the end goal is to construct a new feature selection method to be used in (predictive) medical decision making process. The presentation would largely covers the brief introduction of feature selection in general and within medical domain, basic feature selection methods, state-of-the-art of feature selection for medical data, problem description, research question for the project, also a brief planning for the next step. Since the project is still in early stage, any input or suggestions for content or methodology would be appreciated.

You are all welcome to Miranti’s talk!