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When a person interested in a topic enters a keyword into a Web search engine, the response is nearly instantaneous (and sometimes overwhelming). The impressive speed is due to clever inverted index structures, caching, and a domain-independent knowledge of strings. Our project seeks to construct algorithms, data structures, and software that approach the speed of keyword-based search engines for queries on structural databases. A structural database is one whose data objects include trees, graphs, or a set of interrelated labeled points in two, three, or higher dimensional space. Examples include databases holding (i) protein secondary and tertiary structure, (ii) phylogenetic trees, (iii) neuroanatomical networks, (iv) parse trees, (v) molecular diagrams, and (vi) XML documents. Comparison queries on such databases require solving variants of the graph isomorphism or subisomorphism problems (for which all known algorithms are exponential), so we have explored a large heuristic space.
@inproceedings{ author = {Jason Tsong-Li Wang and Xiong Wang and Dennis Shasha and Bruce A. Shapiro and Kaizhong Zhang and Xinhuan Zheng and Qicheng Ma and Zasha Weinberg}, editor = {Weidong Chen and Jeffrey F. Naughton and Philip A. Bernstein}, title = {An Approximate Search Engine for Structural Databases}, booktitle = {Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, May 16-18, 2000, Dallas, Texas, USA}, journal = {SIGMOD Record}, publisher = {ACM}, volume = {29}, number = {2}, year = {2000}, isbn = {1-58113-218-2}, pages = {584}, crossref = {DBLP:conf/sigmod/2000}, bibsource = {DBLP, http://dblp.uni-trier.de} } }, DiSC'01 Copyright ©2002 ACM Inc. |
