Robert Kessl

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Discovering frequent subgraph patterns from a set of graphs (multi-graph) or a single graph. Practical applications in biology (PPI network), chemistry (similar compounds), or social networks (similar communities), etc The problem is computationally hard as it requires: Enumeration of an exponential number of candidate subgraph patterns Checking their(More)
To make the task simpler to the user, context-aware applications try to infer the current context of the task through a set of sensors. These sensors deliver information which is the basis for the context model helping the application to take appropriate actions. Up to recently, most of the context-aware systems and frameworks made the assumption of dealing(More)
Frequent sequence mining is well known and well studied problem in datamining. The output of the algorithm is used in many other areas like bioinformatics, chemistry, and market basket analysis. Unfortunately, the frequent sequence mining is computationally quite expensive. In this paper, we present a novel parallel algorithm for mining of frequent(More)
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