@inproceedings{DBLP:conf/sigmod/BunemanDHS96, author = {Peter Buneman and Susan B. Davidson and Gerd G. Hillebrand and Dan Suciu}, editor = {H. V. Jagadish and Inderpal Singh Mumick}, title = {A Query Language and Optimization Techniques for Unstructured Data}, booktitle = {Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, June 4-6, 1996}, publisher = {ACM Press}, year = {1996}, pages = {505-516}, ee = {http://doi.acm.org/10.1145/233269.233368, db/conf/sigmod/BunemanDHS96.html}, crossref = {DBLP:conf/sigmod/96}, bibsource = {DBLP, http://dblp.uni-trier.de} }
A New kind of data model has recently emerged in which the database is not constrained by a conventional schema. Systems like ACeDB, which has become very popular with biologists, and the recent Tsimmis proposal for data integration organize data in tree-like structures whose components can be used equally well to represent sets and tuples. Such structures allow great flexibility in data representation.
What query language is appropriate for such structures? Here we propose a simple language UnQL for querying data organized as a rooted, edge-labeled graph. In this model, relational data may be represented as fixed-depth trees, and on such trees UnQL is equivalent to the relational algebra. The novelty of UnQL is consists in its programming constructs for arbitrarly deep data and for cyclic structures. While strictly more powerful than query languages with path expressions like XSQL, UnQL can still be efficiently evaluated. We describe new optimization techniques for the deep or "vertical" dimension of UnQL queries. Furthermore, we show that known optimization techniques for operators on flat relations apply to the "horizontal" dimension on UnQL.
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