Deltagraph free download1/11/2024 In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), page 721, 2002. gspan: Graph-based substructure pattern mining. A platform based on the multi-dimensional data model for analysis of bio-molecular structures. In Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (PODS'02), pages 39-52, 2002. Algorithmics and applications of tree and graph searching. In Proceedings of the 25th Very Large Data Bases (VLDB'99), pages 302-314, 1999. Relational databases for querying XML documents: Limitations and opportunities. IEEE Transactions on Knowledge and Data Engineering, 9(3):435-447, 1997. Similarity searching in medical image databases. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'04), pages 647-652, 2004. A quickstart in frequent structure mining can make a difference. In Proceeding of the 7th International Conference on Database Theory (ICDT'99), pages 277-295, 1999. ![]() Lore: A database management system for semistructured data. ![]() In Proceedings of the 15th International Conference on Data Engineering (ICDE'99), pages 572-581, 1999. A graph query language and its query processing. In Proceedings of the 10th European software engineering conference (ESEC/FSE'05), pages 286-295, 2005. Sober: statistical model-based bug localization. In Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM'01), pages 313-320, 2001. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM'03), page 549, 2003. Efficient mining of frequent subgraphs in the presence of isomorphism. In Proceedings AAAI'94 of the Workshop on Knowledge Discovery in Databases (KDD'94), 1994. Substructure discovery in the subdue system. In Proceedings of the 22nd International Conference on Data Engineering (ICDE'06), page 38, 2006. Closure-tree: An index structure for graph queries. Computers and Intractability: A Guide to the Theory of NP-Completeness. A step towards unification of syntactic and statistical pattern recognition. On power-law relationships of the internet topology. Efficient matching and indexing of graph models in content-based retrieval. The Design and Analysis of Computer Algorithms. Our experimental studies demonstrate that (Tree+Δ) has a compact index structure, achieves an order of magnitude better performance in index construction, and most importantly, outperforms up-to-date graph-based indexing methods: gIndex and C-Tree, in graph containment query processing. ![]() It has two implications: (1) the index construction by (Tree+Δ) is efficient, and (2) the graph containment query processing by (Tree+Δ) is efficient. Our study verifies that ( Tree+Δ) is a better choice than graph for indexing purpose, denoted ( Tree+Δ ≥Graph), to address the graph containment query problem. In order to achieve better pruning ability than existing graph-based indexing methods, we select, in addition to frequent tree-features ( Tree), a small number of discriminative graphs (Δ) on demand, without a costly graph mining process beforehand. We analyze the effectiveness and efficiency of tree as indexing feature from three critical aspects: feature size, feature selection cost, and pruning power. In this paper, we propose a new cost-effective graph indexing method based on frequent tree-features of the graph database. Due to the vast number of graphs in G and the nature of complexity for subgraph isomorphism testing, it is desirable to make use of high-quality graph indexing mechanisms to reduce the overall query processing cost. Given a graph database G, and a query raph q, the graph containment query is to retrieve all graphs in G which contain q as subgraph(s). ![]() As a result, it is of special interest to process graph containment queries effectively on large graph databases. Recent scientific and technological advances have witnessed an abundance of structural patterns modeled as graphs.
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