An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
A relational model of data for large shared data banks
Communications of the ACM
Types and programming languages
Types and programming languages
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Logic and Learning
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Filling in the Blanks - Krimp Minimisation for Missing Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Computability and Randomness
ACM Transactions on Knowledge Discovery from Data (TKDD)
A co-relational model of data for large shared data banks
Communications of the ACM
Compression picks item sets that matter
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Information and Computation
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If we view data as a set of queries with an answer, what would a model be? In this paper we explore this question. The motivation is that there are more and more kinds of data that have to be analysed. Data of such a diverse nature that it is not easy to define precisely what data analysis actually is. Since all these different types of data share one characteristic --- they can be queried --- it seems natural to base a notion of data analysis on this characteristic. The discussion in this paper is preliminary at best. There is no attempt made to connect the basic ideas to other --- well known --- foundations of data analysis. Rather, it just explores some simple consequences of its central tenet: data is a set of queries with their answer.