Computer Laboratory

Technical reports

Semantic optimization of OQL queries

Agathoniki Trigoni

October 2002, 171 pages

This technical report is based on a dissertation submitted October 2001 by the author for the degree of Doctor of Philosophy to the University of Cambridge, Pembroke College.


This work explores all the phases of developing a query processor for OQL, the Object Query Language proposed by the Object Data Management Group (ODMG 3.0). There has been a lot of research on the execution of relational queries and their optimization using syntactic or semantic transformations. However, there is no context that has integrated and tested all the phases of processing an object query language, including the use of semantic optimization heuristics. This research is motivated by the need for query execution tools that combine two valuable properties: i) the expressive power to encompass all the features of the object-oriented paradigm and ii) the flexibility to benefit from the experience gained with relational systems, such as the use of semantic knowledge to speed up query execution.

The contribution of this work is twofold. First, it establishes a rigorous basis for OQL by defining a type inference model for OQL queries and proposing a complete framework for their translation into calculus and algebraic representations. Second, in order to enhance query execution it provides algorithms for applying two semantic optimization heuristics: constraint introduction and constraint elimination techniques. By taking into consideration a set of association rules with exceptions, it is possible to add or remove predicates from an OQL query, thus transforming it to a more efficient form.

We have implemented this framework, which enables us to measure the benefits and the cost of exploiting semantic knowledge during query execution. The experiments showed significant benefits, especially in the application of the constraint introduction technique. In contexts where queries are optimized once and are then executed repeatedly, we can ignore the cost of optimization, and it is always worth carrying out the proposed transformation. In the context of adhoc queries the cost of the optimization becomes an important consideration. We have developed heuristics to estimate the cost as well as the benefits of optimization. The optimizer will carry out a semantic transformation only when the overhead is less than the expected benefit. Thus transformations are performed safely even with adhoc queries. The framework can often speed up the execution of an OQL query to a considerable extent.

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BibTeX record

  author =	 {Trigoni, Agathoniki},
  title = 	 {{Semantic optimization of OQL queries}},
  year = 	 2002,
  month = 	 oct,
  url = 	 {},
  institution =  {University of Cambridge, Computer Laboratory},
  number = 	 {UCAM-CL-TR-547}