2017 IEEE 33rd International Conference on Data Engineering (2017)
San Diego, California, USA
April 19, 2017 to April 22, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2017.190
There is an increasing demand to explore similar entities in big graphs. For example, in domains like biomedical science, identifying similar entities may contribute to developing new drugs or discovering new diseases. In this paper, we demonstrate a graph exploration system, called GQFast, which provides a graphical interface to help users efficiently explore similar entities. Methodologically, GQFast first builds efficient indices combining column database optimizations and compression techniques, then it explores similar entities by using the indices. GQFast operates on the real-world Pubmed dataset consisting of over 23 million biomedical entities and 1.3 billion relationships. Relying on GQFast's high performance, GQFast provides (i) type-ahead-search to instantly visualize search results while a user is typing a query, and (ii) context-aware query completion to guide users typing queries.
Indexes, Space exploration, Visualization, Arteriosclerosis, Data models, Generators
C. Lin, J. Wang and Y. Papakonstantinou, "GQFast: Fast Graph Exploration with Context-Aware Autocompletion," 2017 IEEE 33rd International Conference on Data Engineering(ICDE), San Diego, California, USA, 2017, pp. 1389-1390.