Embedding tree metrics into low dimensional Euclidean spaces
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On the Local Form and Transitions of Symmetry Sets, Medial Axes, and Shocks
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
D(k)-index: an adaptive structural summary for graph-structured data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
SMI '04 Proceedings of the Shape Modeling International 2004
Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Indexing Hierarchical Structures Using Graph Spectra
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing through laplacian spectra
Computer Vision and Image Understanding
An adaptive path index for XML data using the query workload
Information Systems
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Graphs provide effective data structures modeling complex relations and schemaless data such as images, XML documents, circuits, compounds, and proteins. Given a query graph, efficiently finding all database graphs in which the query is a subgraph is an important problem raising in different domains. In this paper, we propose a new method for indexing tree structures based on a graph-theoretic concept called caterpillar decomposition and discuss its advantages over two previous indexing algorithms. Experimental evaluation of the proposed framework including the comparison with the previous approaches demonstrates the efficacy of the overall approach.