One person's trash is another person's treasure"? With the abundance of data available to us today, can we really find useful knowledge in it? And, is knowledge even useful? Join us in this mini-symposium where we will discuss what makes knowledge useful, when is knowledge useful, how we can represent it and its different applications. Our focus is on knowledge graphs and how to translate knowledge to the user, we discuss the open challenges and research directions around knowledge usability.

Location and Program

The mini-symposium will run from 9:00AM to 11:00AM on April 19, on the Eindhoven University of Technology (TU/e) campus in the MetaForum building on room MF 5, on floor 0


Time Program
9:00-9:10 Welcome
9:10-9:40 Opening Statements
9:40-10:00 Break
10:00-11:00 Panel Discussion: Is knowledge useful?
13:30-14:30 Larissa C. Shimomura - PhD Defense (Altas 0.710)


Alex Thomo

Alex Thomo is a professor of Computer Science at the University of Victoria, British Columbia, Canada. He is recognized as an expert in Databases, Data Mining, and Distributed Computing and has served as a program committee member for numerous conferences of the area, including VLDB, SIGKDD, ICDE, CIKM, EDBT, etc. His recent research interests include algorithms for big data, large-scale data mining and machine learning, social network analytics, and data privacy.

Felix Naumann

Professor Felix Naumann is the Chair for Information Systems at the Hasso Plattner Institute (HPI) at the University of Potsdam in Germany. Next to numerous PC memberships for international conferences, he has organized several conferences and workshops in various roles, including VLDB 2021 as PC co-chair. Felix Naumann was named an ACM Distinguished Member for outstanding engineering contributions to computing. His research interests include data profiling, data cleansing, and data integration.

Fernando Paulovich

Fernando V. Paulovich is an Associate Professor in Visual Analytics for Data Science at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), the Netherlands. Before moving to the Netherlands, he was a Professor and Canada Research Chair at Dalhousie University, Canada (2017-2022), and an associate professor at University of São Paulo, Brazil (2009-2017). He has been researching information visualization and visual analytics, focusing on integrating machine learning and visualization tools and techniques, taking advantage of the automation provided by machine learning approaches, and user knowledge through interactions with visual representations to help people understand and take advantage of complex and massive data collections. In the past years, his primary focus has been on designing and developing visual analytics techniques for the general public to advance the concept of data democratization, promoting unconstrained access to data analysis and widening the analytic capability of lay users in transforming data into insights.

Katja Hose

Katja Hose is a full professor of Data Management at TU Wien‘s Databases and Artificial Intelligence research unit. Prior to joining TU Wien, Katja was a full professor at Aalborg University and a postdoc at the Max Planck Institute for Informatics in Saarbrücken. Her research is rooted in data and knowledge engineering and spans theory, algorithms, and applications of data science including graph databases, knowledge graphs, querying, analytics, and machine learning. In the recent years, she has gained extensive experience in interdisciplinary data science in collaborations with colleagues from bioscience, health, and environmental assessment.

Nikolay Yakovets (Chair)

Nikolay Yakovets is an Assistant Professor at the Department of Mathematics and Computer Science, Database Group at Eindhoven University of Technology (TU/e). His main area of study is databases and data intensive systems. He is particularly interested in foundations of databases, efficient data analytics and engineering of high-performance data processing systems. Nikolay’s current focus is on design and implementation of core database technologies, management of massive graph data, and efficient processing of queries on graphs.

Larissa C. Shimomura - PhD Defense

Title: "On Graph Generating Dependencies and Their Application in Data Profiling"

Summary: Every day, we generate new data through various activities such as browsing new websites, conducting online searches, and accepting social media friend requests. Among the various types of data being generated, one type that has been gaining attention is graph data. Graph data represents information in a network-like structure, where objects are interconnected based on relationships or associations. For data analysts, an important part of their job is to comprehend and extract relevant data that can improve the applications and solutions they are developing. However, given the diversity of daily generated data and the complexity of graph data, understanding it can be both time-consuming and challenging. In the field of Computer Science, data profiling is a research topic that aims at developing new methods to represent data and algorithms for gaining insights into datasets. Logical expressions are commonly used to represent information in this field, and with the rise of graph data, there's growing interest in how to express information from graphs. In this context, this thesis introduces a novel formalism known as Graph Generating Dependencies (GGDs), which enables the expression of semantic information derived from graph data. For example, in a social network graph, GGDs can express that if two people share the same last name and are labeled as "friends," they should also be connected as "family." Additionally, this thesis proposes automated methods for discovering information represented by GGDs within datasets, demonstrating their utility in detecting potential errors in the data. By introducing this formalism and outlining its practical applications, this research contributes to advancing the field of data analysis and profiling, particularly in the context of graph data. Furthermore, the thesis outlines open challenges and directions for tasks like data profiling and data cleaning in graph data management systems.


For any further questions about the event, contact us at: