By Jamie Beckett, March 2008
Goetz Graefe, one of the most accomplished and influential technologists in the areas of database query optimization and query processing, is HP Labs' newest HP Fellow.
Graefe, who joined HP in 2007, is currently pursuing research in business intelligence, specifically on improving robustness, reliability and total cost of ownership of HP's business intelligence products.
Novel work in query optimization
He is best known for his pioneering work on top-down, rule-based query optimization, which dramatically shifted how database query optimizers are architected. More flexible than the traditional approach, this strategy allows the set of query operators to be extended in order to integrate more functionality into a database query processor. His survey of query evaluation techniques for large databases is similarly well-known.
This work is even more relevant today, as enterprise-scale data warehousing, decision support and business intelligence are becoming the dominant users of high-end parallel database technology.
Before joining HP, Graefe spent 12 years at Microsoft, where he was instrumental in the development, testing and release of the SQL Server product. SQL Server changed the focus in the database market from performance, scalability and specific features to ease-of-use, automation and total cost of ownership.
Awards and publications
Graefe's publications have become standard reference in the fields of query evaluation, dynamic query execution techniques and query performance. Several of his recent publications have focused on novel techniques for B-tree indexes.
Two of the top three database conferences have recognized his contributions. In 2000, Graefe received the “ten-year test of time award” at the ACM Special Interest Group on Management Of Data ( SIGMOD) International Conference for his 1990 paper on parallel query execution.
In 2005, he received the inaugural “influential paper award” at the IEEE International Conference on Data Engineering (ICDE) for a 1993 paper on extensible query optimization.