Crucial Barriers of Knowledge Society

  • Jaroslav Král
  • Michal Žemlička
Part of the Communications in Computer and Information Science book series (CCIS, volume 112)


Modern information technologies offer, at least in principle, many exciting opportunities. One of them is the concept of knowledge society (KS). Practical results of KS are, however, substantially restricted by non-technical limitations, especially by the legislative bans on the use of existing data, on the data searching and collecting, and on the information and knowledge computed from them.

We show the consequences it has on the experience with use of a health related databases in Czech Republic. Czech system of health services produces a lot of data that could be used to supervise/monitor drug addictions, epidemic outbreak, quality of health procedures and medicaments, etc. It is, however, forbidden by law. Some other well started and very useful projects in Czech Republic using medical data were cancelled. It reduces the quality of medical care, medical research, and medical knowledge. Similar issues have appeared during the attempts to evaluate education processes and institutions or to analyze global economic processes. We must suspect that there is hidden interest not to secure data but to prevent the desirable effects of the health data analysis and the generation of knowledge. We propose a solution based on the existence of accredited bodies allowed to produce open information and open knowledge from sensitive data.

The bodies can be queried by public and they should support knowledge-based processes of filtering and cleaning of the data and the filtering of queries to admit the acceptable ones and to Control whether the data in answers on the queries do not provide sensitive information or enable undesirable knowledge. The structure of the queries can itself provide interesting knowledge on social processes in knowledge society.


Description Logic Sensitive Data Private Data Knowledge Society Accredited Body 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jaroslav Král
    • 1
    • 2
  • Michal Žemlička
    • 1
  1. 1.Faculty of Mathematics and PhysicsCharles UniversityPraha 1Czech Republic
  2. 2.Faculty of InformaticsMasaryk UniversityBrnoCzech Republic

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