• Publications
  • Influence
On supporting containment queries in relational database management systems
tl;dr
In this paper, we explore some performance implications of both options using native implementations in two commercial relational database systems and in a special purpose inverted list engine. Expand
  • 876
  • 92
DB2 advisor: an optimizer smart enough to recommend its own indexes
tl;dr
This paper introduces the concept of letting an RDBMS optimizer optimize its own environment. Expand
  • 261
  • 33
DB2 Design Advisor: Integrated Automatic Physical Database Design
tl;dr
The DB2 Design Advisor in IBM® DB2 Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Expand
  • 329
  • 28
DB2 with BLU Acceleration: So Much More than Just a Column Store
tl;dr
DB2 with BLU Acceleration deeply integrates innovative new techniques for defining and processing column-organized tables that speed read-mostly Business Intelligence queries by 10 to 50 times and improve compression by 3 to 10 times, compared to traditional row- organized tables. Expand
  • 203
  • 28
Measuring the Complexity of Join Enumeration in Query Optimization
tl;dr
This paper describes and measures the performance of the Starburst join enumerator, which can parameterically adjust for each query the space of join sequences that arc evaluated by the optimizer to allow or disallow (I) composite tables (i.e., tables that are themselves the result of a join) as the inner operand of the join and (2) joins between two tables having no join predicate linking them. Expand
  • 206
  • 20
LEO - DB2's LEarning Optimizer
tl;dr
In this paper we introduce LEO, DB2's LEarning Optimizer, as a comprehensive way to repair incorrect statistics and cardinality estimates of a query execution plan. Expand
  • 340
  • 17
Robust query processing through progressive optimization
tl;dr
We present an approach to query processing that is extremely robust because it is able to detect and recover from cardinality estimation errors. Expand
  • 237
  • 16
Extensible query processing in starburst
tl;dr
We describe the design of Starburst's query language processor and discuss the ways in which the language processor can be extended to achieve Starburst’s goals. Expand
  • 272
  • 13
Automating physical database design in a parallel database
tl;dr
We present a comprehensive solution to the problem that has been tightly integrated with the optimizer of a commercial shared-nothing parallel database system, in which data is horizontally partitioned among multiple independent nodes. Expand
  • 218
  • 13
SQAK: doing more with keywords
tl;dr
We propose a framework called SQAK1 (SQL Aggregates using Keywords) that enables users to pose aggregate queries using simple keywords with little or no knowledge of the schema. Expand
  • 113
  • 13