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Kineograph is a distributed system that takes a stream of incoming data to construct a continuously changing graph, which captures the relationships that exist in the data feed. As a computing platform, Kineograph further supports graph-mining algorithms to extract timely insights from the fast-changing graph structure. To accommodate graph-mining(More)
As the study of graphs, such as web and social graphs, becomes increasingly popular, the requirements of efficiency and programming flexibility of large graph processing tasks challenge existing tools. We propose to demonstrate <i>Surfer</i>, a large graph processing engine designed to execute in the cloud. Surfer provides two basic primitives for(More)
As the study of large graphs over hundreds of gigabytes becomes increasingly popular for various data-intensive applications in cloud computing, developing large graph processing systems has become a hot and fruitful research area. Many of those existing systems support a <i>vertex-oriented</i> execution model and allow users to develop custom logics on(More)
Grace is a graph-aware, in-memory, transactional graph management system, specifically built for real-time queries and fast iterative computations. It is designed to run on large multi-cores, taking advantage of the inherent parallelism to improve its performance. Grace contains a number of graph-specific and multi-core-specific optimizations including(More)
As the study of large graphs over hundreds of gigabytes becomes increasingly popular in cloud computing, efficiency and programmability of large graph processing tasks challenge existing tools. The inherent random access pattern on the graph generates significant amount of network traffic. Moreover, implementing custom logics on the unstructured data in a(More)
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