No abstract

The propagation of pollutants between regions has become a noticeable factor affecting air pollution. Given the complicated propagation relationship, most of the existing works lack an effective perception mechanism of geographic correlations and time-varying features, which is crucial in exploring and understanding the propagation mechanism by integrating empirical knowledge and data inherent characteristics. In this paper, we abstract the complicated propagation relationship between regions as a dynamic network, and introduce visual analytics techniques to explore the spatiotemporal multivariate patterns of air pollution propagation. A particle tracking based model is first proposed to construct pollution propagation networks under multi-source factors. It combines numerical simulation and data characteristics simultaneously, and detects active pollution source areas based on long-term transport relationships and temporal correlations. Based on it, we extract propagation patterns and analyze the temporal evolution of diachronic propagation networks. Moreover, we design an interactive system to achieve an in-depth analysis of air pollution issues. Through elaborate multi-level glyphs and linkage views, the system facilitates users to perceive and explore propagation mechanism in spatiotemporal multivariate information, and compare propagation patterns from global and local perspectives. We present several case studies to demonstrate the usefulness of our work in air pollution propagation analysis.

With the continual growth of network speed and the increasing sophistication of network applications, keeping network operations efficient and secure becomes more challenging. Pattern matching is one of the key technologies for content-ware network processing, such as traffic classification, application identification and intrusion prevention. In this paper, we propose a hybrid pattern matching algorithm optimized for multi-core network processing platforms. As a system-level solution, our scheme focuses on both performance stability and hardware/software co-design. To verify the effectiveness of our design, the proposed algorithm is implemented on a stateof-art 16-MIPS-core network processing platform and evaluated with real-life data sets. Experimental results show that, when compared with the traditional Aho-Corasick algorithm, our hybrid solution saves 60~95% memory space while guarantees stable performance on large pattern sets and against adverse test traffic.