Pattern match queries oriented to uncertain planar graphs

Abstract

Pattern match search oriented to planar graphs is widely used in biological networks,social networks,fingerprint identification and image segmentation.Meanwhile,data extracted from those applications is inherently uncertain due to noise,incompleteness and inaccuracy,and query processing techniques of certain planar graphs cannot be applied to uncertain graph data.Therefore,in this paper,the pattern match query oriented to uncertain planar graphs was studied under the probabilistic semantics.Specifically,Uncertain Pattern Match(UPM) queries using the possible world semantics were investigated.Firstly,to avoid enumerating all possible worlds,a basic deterministic algorithm that can exactly compute UPM query was proposed.To further speed up the basic algorithm,an improved deterministic approach was developed based on tighten bounds.Secondly,a sampling algorithm that can quickly and accurately estimate matching probability was designed.The effectiveness of the proposed solutions for UPM queries was verified through extensive experiments on real uncertain planar graph datasets.Finally,UPM queries were applied to the segmentation on pulmonary CT images.The results show that the proposed approaches are superior to classical techniques of image segmentation.