As the spread of high-speed networks and the development of network technologies, P2P technologies are actively used today for information exchange in the network. While information exchange in a P2P network is quite flexible, there is an important problem--lack of reliability. Since we cannot know the details of how the data was obtained, it is hard to fully rely on it. To ensure the reliability of exchanged data, we have proposed the framework of a traceable P2P record exchange based on database technologies. In this framework, records are exchanged among autonomous peers, and each peer stores its exchange and modification histories in it. The framework supports the function of tracing queries to query the details of the obtained data. A tracing query is described in datalog and executed as a recursive query in the P2P network. In this paper, we focus on the query processing strategies for the framework. We consider two types of queries, ad hoc queries and continual queries, and present the query processing strategies for their executions.

Visual tracking has been studied for several decades but continues to draw significant attention because of its critical role in many applications. Recent years have seen greater interest in the use of correlation filters in visual tracking systems, owing to their extremely compelling results in different competitions and benchmarks. However, there is still a need to improve the overall tracking capability to counter various tracking issues, including large scale variation, occlusion, and deformation. This paper presents an appealing tracker with robust scale estimation, which can handle the problem of fixed template size in Kernelized Correlation Filter (KCF) tracker with no significant decrease in the speed. We apply the discriminative correlation filter for scale estimation as an independent part after finding the optimal translation based on the KCF tracker. Compared to an exhaustive scale space search scheme, our approach provides improved performance while being computationally efficient. In order to reveal the effectiveness of our approach, we use benchmark sequences annotated with 11 attributes to evaluate how well the tracker handles different attributes. Numerous experiments demonstrate that the proposed algorithm performs favorably against several state-of-the-art algorithms. Appealing results both in accuracy and robustness are also achieved on all 51 benchmark sequences, which proves the efficiency of our tracker.