Efficient and Scalable Subgraph Search from Big Graphs in Cloud

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This project seeks to develop knowledge that will form the foundation for next-generation graph search engines of structural knowledge. Subgraph search is in high demand for many applications that deal with big graphs, such as social network marketing, crime detection and sales pattern discovery. However, there are many challenges when the search needs to be conducted in cloud environments. This project aims to develop efficient and scalable query processing algorithms to search subgraphs from a big graph stored in a cluster of machines in the cloud. The expected outcomes may lay the theoretical foundations for subgraph search in the cloud, and establish a unified graph search engine that integrates various query semantics for use in many applications.