Abstract
Due to the wide applications, subgraph queries have attracted lots of attentions in database community. In this paper, we focus on subgraph queries over a large graph G. Different from existing feature-based approaches, we propose a bitmap structure based on edges to index the graph. At run time, we decompose Q into a set of adjacent edge pairs (AEP). We develop edge join (EJ) algorithms to address AEP subqueries. The bitmap index can reduce both I/O and CPU cost. More importantly, the bitmap index has the linear space complexity instead of the exponential complexity in feature-based approaches, which confirms its good scalability. Extensive experiments show that our method outperforms existing ones in both online and offline performances significantly.
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Peng, P., Zou, L., Chen, L., Lin, X., Zhao, D. (2011). Subgraph Search over Massive Disk Resident Graphs. In: Bayard Cushing, J., French, J., Bowers, S. (eds) Scientific and Statistical Database Management. SSDBM 2011. Lecture Notes in Computer Science, vol 6809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22351-8_19
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DOI: https://doi.org/10.1007/978-3-642-22351-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22350-1
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