The size of a streaming graph is possibly unbounded, and it is updated by a continuous sequence of edges over time. Due to numerous types of real-world interactions, the nature of edge arrival in a streaming graph is dynamic and holds different types of temporal subgraphs, such as stars, bipartite forms, cliques, and chains. The most current techniques find such subgraphs in each snapshot of a dynamic graph and use a dictionary or hash-based summary to compress the graph before applying a stitching technique to demonstrate its temporal behavior. However, it remains difficult to discover those subgraph structures from the continuous stream of edges found in large and rapidly changing dynamic graphs. In this paper, we propose a streaming graph compression algorithm, StarZIP, that uses a new encoding scheme. Our motivational factor is real-world graphs that contain an overwhelmingly large number of stars and a few other structures. We have observed that all subgraph structures can be represented as star-shaped subgraphs. Moreover, the star-shaped representation can easily be arranged in the form of an inverted index, which enables the application of different inverted list encoding techniques for compression. Therefore, we shatter a graph into a uniform representation of stars to compress it. The evaluation of StarZIP on real-world datasets shows that our proposed system reduced the size of a highly dense graph to 60 times less than its original size. Moreover, the experimental results indicate that StarZIP compression is 4 times better than the stateof-the-art techniques.

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Abstract:The amount of human action video data is increasing rapidly due to the growth of multimedia data, which increases the problem of how to process the large number of human action videos efficiently. Therefore, we devise a novel approach for human action similarity estimation in the distributed environment. The efficiency of human action similarity estimation depends on feature descriptors. Existing feature descriptors such as Local Binary Pattern and Local Ternary Pattern can only extract texture information but cannot obtain the object shape information. To resolve this, we introduce a new feature descriptor, namely Edge based Local Pattern descriptor (ELP). ELP can extract object shape information besides texture information and ELP can also deal with intensity fluctuations. Moreover, we explore Apache Spark to perform feature extraction in the distributed environment. Finally, we present an empirical scalability evaluation of the task of extracting features from video datasets.

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