The delay and window size problems in rule-based stream reasoning

Expressive Rule-Based Stream Reasoning

Stream reasoning is the task of continuously deriving conclusions on streaming data. As a research theme, it is targeted by different communities which emphasize different aspects, e.g., throughput vs. expressiveness. This thesis aims to advance the theoretical foundations underlying diverse stream reasoning approaches and to convert obtained insights into a prototypical expressive rule-based r...

Answer Update for Rule-Based Stream Reasoning

Stream reasoning is the task of continuously deriving conclusions on streaming data. To get results instantly one evaluates a query repeatedly on recent data chunks selected by window operators. However, simply recomputing results from scratch is impractical for rule-based reasoning with semantics similar to Answer Set Programming, due to the trade-off between complexity and data throughput. To...

Adaptive window size image denoising based on ICI rule

An algorithm for image noise-removal based on local adaptive window size ¿ltering is developed in this paper. Two features to use into local spatial/transform-domain ¿ltering are suggested. First, ¿ltering is performed on images corrupted not only by additive white noise, but also by imagedependent (e.g. ¿lm-grain noise) or multiplicative noise. Second, used transforms are equipped with a varyi...

Stream-Based Reasoning in DyKnow

The information available to modern autonomous systems is often in the form of streams. As the number of sensors and other stream sources increases there is a growing need for incremental reasoning about the incomplete content of sets of streams in order to draw relevant conclusions and react to new situations as quickly as possible. To act rationally, autonomous agents often depend on high lev...

Robust Reasoning: Integrating Rule-Based and Similarity-Based Reasoning