A Web Service Similarity Refinement Framework Using Automata Comparison

Chen, Lu; Chow, Randy

doi:10.1007/978-3-642-12035-0_6

Cited by 2 publications

(1 citation statement)

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“…Our problem also has some similarity to the problem of measuring the similarity of two deterministic finite automata, as considered by Schwefel, Wegener, and Weinert [4], who solved it using evolutionary algorithms. Chen and Chow also used a scheme for comparing automata, specifically in the context of web services [14]. Our work is differs because of the unique challenges inherent in the differences between filters and automata-because the behavior of a filter may be undefined for certain state-action pairs, the problem of measuring similarities between filters is more challenging.…”

Section: Filter Reductionmentioning

confidence: 99%

Improper Filter Reduction

Saberifar,

Mohades,

Razzazi

et al. 2017

Preprint

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Combinatorial filters have been the subject of increasing interest from the robotics community in recent years. This paper considers automatic reduction of combinatorial filters to a given size, even if that reduction necessitates changes to the filter's behavior. We introduce an algorithmic problem called improper filter reduction, in which the input is a combinatorial filter F along with an integer k representing the target size. The output is another combinatorial filter F ′ with at most k states, such that the difference in behavior between F and F ′ is minimal.We present two metrics for measuring the distance between pairs of filters, describe dynamic programming algorithms for computing these distances, and show that improper filter reduction is NP-hard under these metrics. We then describe two heuristic algorithms for improper filter reduction, one greedy sequential approach, and one randomized global approach based on prior work on weighted improper graph coloring. We have implemented these algorithms and analyze the results of three sets of experiments.

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Section: Filter Reductionmentioning

confidence: 99%