A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
GBLENDER: towards blending visual query formulation and query processing in graph databases
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
GBLENDER: visual subgraph query formulation meets query processing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
TreeSpan: efficiently computing similarity all-matching
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
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In a previous paper, we laid out the vision of a novel graph query processing paradigm where instead of processing a visual query graph after its construction, it interleaves visual query formulation and processing by exploiting the latency offered by the GUI [4]. Our recent attempts at implementing this vision [4,6], show significant improvement in the system response time (SRT) for subgraph queries. However, these efforts are designed specifically for graph databases containing a large collection of small or medium-sized graphs. Consequently, its frequent fragment-based action-aware indexing schemes and query processing strategy are unsuitable for supporting subgraph queries on large networks containing thousands of nodes and edges. In this demonstration, we present a novel system called QUBLE (QUery Blender for Large nEtworks) to realize this novel paradigm on large networks. We demonstrate various innovative features of QUBLE and its promising performance.