WIS-Circle

The Web Information Systems (WIS) group focuses on research & education in data-based information systems on the Web and making them more effective in retrieving, processing and interpreting data generated by humans and machines. The overall mission of the WIS research group is:

  • to develop a deep understanding of the construction and use of web-based information systems,
  • to develop novel methods, techniques, and tools that advance the way in which web-based information systems are constructed and used, and
  • to offer students education that prepares them for a leading role in web-based information systems science and technology.

Latest news


  • We are organizing this year's Dutch Belgian Database Day (DBDBD21) in Delft. More information can be found in the official event page.
  • We are looking for a new colleague in NLP to join us at the Web Information Systems group. See details here.
  • Amazon Science Best Paper Award at AAAI HCOMP 2021. The paper presents a checklist to combat cognitive biases in crowdsourcing.
  • Best paper award in ACM DEBS. The paper deals with transactions on stateful functions in the Cloud.
  • We are honoured and happy that our group is part of (the) two consortia invited by NWO to work out a 10-year Long Term Programme. We are with our knowledge management and human computing expertises part of ROBUST for trusthworthy AI and of Plant-XR.
  • Valentine, an open source tool and benchmarking suite on matching techniques for dataset discovery will be presented in ICDE'21 and demoed in VLDB'21.
  • WIS Delft will be very well represented at the premier Human Computation and Crowdsourcing conference, AAAI HCOMP 2021. We have 3 full research papers and 3 demo papers accepted!
  • Douglas Engelbert Best Paper Award at ACM HT 2021. The paper explores the obfuscation and labeling of search results to mitigate confirmation bias on the web.
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Focus

In the Web Information Systems research group, we aim at making web information systems more effective in retrieving, processing and interpreting data generated by humans and machines.

Objectives

  • To understand what human‐ and machine-generated web data represents in terms of people's actions, interests, intents, and behaviors on the web.
  • To develop new solutions to meet the fundamental challenges in how systems effectively attribute and exploit semantics for human‐ and machine-generated data, given the size and dynamic nature of the web.

Approach

Our approach towards our objectives of understanding and developing, combines four main research areas:
  • Data Management
  • Human Computing & Human-Centered AI
  • Information Retrieval
  • User Modeling & Learning Analytics

Disciplines

As WIS researchers, together with our students, we strive to advance the state-of-the-art in relevant disciplines like user modeling, Web science, information retrieval, natural language processing, database systems, Web engineering, Web data management, user interaction, human computing, and human-AI interaction. We contribute to and impact these scientific disciplines by publishing in the top venues in these fields and by actively serving in the organization and program committees of relevant conferences and in editorial boards of relevant journals.