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Working Groups

The KDubiq coordination action works through a network structure, involving active centres of basic and applied research in Knowledge Discovery and related areas. Six Working Groups (WG) are reflecting the "design space" (Michael May) that we have to decide on, while we describe the design of a Ubiquitous Knowledge Discovery System.

With a personal account on the KDubiq Website you can access to all KDubiq Working Groups in Media WIKI.



WG1: Application Environment
Responsible: Koen Vanhoof Hasselt University, Belgium
Mailing List Email: kdubiq_wg1(AT)list2.ais.fraunhofer.de
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The Application Environment is concerned with the various ubiquitous computing environments from a knowledge discovery point of view. It will systematically collect application scenarios for knowledge discovery in these areas, building on results from current research projects and roadmaps such as the ISTAG grand challenges paper; identify opportunities, challenges and risk in the various areas; match applications to the technological challenges (distributed technologies, learning components, data types, security & privacy, HCI); interface with the various ubiquitous computing communities; consolidate research area group meeting results, surveys and blueprint editing activities in a chapter for the KDubiq blueprint.

Research Areas

  • mobile data mining
  • sensor networks
  • calm technology
  • wireless mobile networks

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WG2: Ubiquitous Technologies
Responsible: WG Chair: Assaf Schuster assisted by Ran Wolff, Technion University, Haifa, Israel
Mailing List Email: kdubiq_wg2(AT)list2.ais.fraunhofer.de
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Ubiquitous Technologies surveys systematically existing and emerging solutions for distributed handling of data and computation. The various architectural options form the technological basis for a ubiquitous discovery system. Ubiquitous Technologies will: survey results from past and ongoing projects, match technologies to applications, identify research challenges and opportunities, contribute to the blueprint.

Research Areas

  • distributed agent technology
  • grid computing
  • peer to peer
  • semantic-based self-organizing networks
  • distributed databases
  • autonomic computing
  • distributed data mining

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WG3: (Learning Components) Resource-aware distributed algorithms
Responsibles: Joao Gama, Porto University, Portugal and Antoine Cornuéjols, LRI Paris-Sud, France
Mailing List Email: kdubiq_wg3(AT)list2.ais.fraunhofer.de
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This Working Group deals with algorithmic issues at the heart of machine learning and knowledge discovery. One objective is to provide a coherent perspective on resource constrained algorithms that are fundamentally designed to handle limited bandwidth, limited computing and storage capabilities, limited battery power, and specific network-communication protocols.

Research Areas

  • evolutionary computing
  • machine learning
  • adaptive learning
  • human and animal learning
  • online and anytime learning
  • data streams
  • real time learning
  • statistical relational learning

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WG4: (Data types) Ubiquitous Data -- Interaction and Data Collection
Responsibles: Andreas Hotho, Kassel University, Germany and Rasmus Pedersen, University of Copenhagen, Denmark
Mailing List Email: kdubiq_wg4(AT)list2.ais.fraunhofer.de
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The purpose of this WG ist to investigate about the possible data types for an ubiquitous KDubiq system and how do we handle the data interaction and collection?

This WG4 will: provide an overview on the heterogeneous data types relevant for ubiquitous, knowledge discovery, summarize the state-of-the-art in the various sub areas, identify areas of overlap and cross-fertilization, asses the various methods in cross area activities in the light of the design constraints posed by mobile systems (WG1) and the various distributed architectures.

Research Areas

  • semantic web mining
  • network and heterogeneous data
  • audio / video
  • text mining
  • multilingual ontologies
  • spatio-temporal data mining

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WG5: Security and Privacy
Responsible: Fosca Giannotti, PisaKDDLab, Italy; Co-Chair: Yuecel Saygin, Sabaci University, Istanbul, Turkey
Mailing List Email: kdubiq_wg5(AT)list2.ais.fraunhofer.de
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WG5 will analyse the possible security and privacy preserving issues that we have to solve while designing our KDubiq system? This WG will: identify relevant privacy and security issues and report on the state of the art, gather results from related disciplines such as statistical disclosure control, have the role of promoter of awareness with the respect the privacy issue, in order to constantly compare the new invented technologies with such requirements, search collaboration with authorities and organizations addressing privacy and security.

Research Areas

  • privacy preserving data mining
  • security
  • legal aspects

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WG6: Human Computer Interfaces
Responsible: Bettina Berendt, KU Leuven, Belgium; Co-Chair: Ernestina Menasalvas, Madrid University, Spain
Mailing List Email: kdubiq_wg6(AT)list2.ais.fraunhofer.de
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The WG6 try to investigate the Human-Computer-Interfaces and device related-issues that have to be solved for our correspondent application.

HCI provides a unifying perspective for the different roles the concept of intelligence (as the prime requisite for knowledge discovery) plays in ubiquitous knowledge discovery , i.e.: on the application side (e.g. knowledge bases, computational intelligence and information retrieval), within intelligent user interfaces (as in agents/avatars within virtual worlds), on the part of the human user (cognition).

From a Knowledge Discovery point of view, important human-computer interaction issues are: Collaborative problem solving and DDM, e.g. collaborative filtering, Alternate human-computer interfaces for DDM programs that allow non-intrusive pervasive interactions, Privacy and social impact from integration of the DDM technology with daily life, to provide effective visual data mining tools for small devices and for quick use.

Research Areas

  • user interfaces
  • user profiling
  • web usage mining
  • cognitive modelling
  • ergonomics
  • visual data mining

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