Storing Semistructured Data in Relations

this paper we argue that one can store semistructured data in relational format, by exploiting the regularities inherent in existing semistructured data instances. "Most" of the data will be stored in relational format: the outliers, and possible future insertions, will be still stored in a self-describing way. We propose to use data mining techniques to extract a "good" relational schema for a given semistructured data instance. Our algorithm accepts a variety of input parameters, such as maximum number of relations allowed, maximum number of attributes per relation, and, optionally, a collection of queries on the semistructured data for which the relational storage has to be optimized. Experimental results on the DBLP data show that around 90% of the data can be stored in relational format. The techniques described here are presented in more details in [DFS98]