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Real-time stream data is characterized by spatial and temporal variability and is subject to unbounded or constantly evolving entities. The challenge is how to aggregate these unbounded data streams at different spaces and times to provide effective decisions making in real-time. This paper proposes a rough set-based sliding window framework for stream data aggregation. Based on current data streams,...
This paper proposes a soft computing approach to manage uncertainty and rule discovery by reasoning over inconsistent, incomplete and fragmentary information using dominance-based rough set theories. A methodological and computational basis is illustrated in a sensor network application scenario of a forest fire detection system.
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