On estimating the cardinality of the projection of a database relation
ACM Transactions on Database Systems (TODS)
On the propagation of errors in the size of join results
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Multiple join size estimation by virtual domains (extended abstract)
PODS '93 Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
On the estimation of join result sizes
EDBT '94 Proceedings of the 4th international conference on extending database technology: Advances in database technology
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Materialized views and data warehouses
ACM SIGMOD Record
Efficient mid-query re-optimization of sub-optimal query execution plans
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Cost-based query scrambling for initial delays
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Answering complex SQL queries using automatic summary tables
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Learning table access cardinalities with LEO
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Optimizing Queries with Materialized Views
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
VLDB '88 Proceedings of the 14th International Conference on Very Large Data Bases
A Formal Perspective on the View Selection Problem
Proceedings of the 27th International Conference on Very Large Data Bases
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The dawning of the autonomic computing era
IBM Systems Journal
Autonomic Web-Based Simulation
ANSS '05 Proceedings of the 38th annual Symposium on Simulation
Goals and benchmarks for autonomic configuration recommenders
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Making database systems usable
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A framework for enforcing application policies in database systems
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Automatic SQL tuning in oracle 10g
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A survey of autonomic computing—degrees, models, and applications
ACM Computing Surveys (CSUR)
Optimizer plan change management: improved stability and performance in Oracle 11g
Proceedings of the VLDB Endowment
Architecture of a Database System
Foundations and Trends in Databases
Dynamic plan generation for parameterized queries
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
An Ontology-Based Autonomic System for Improving Data Warehouse Performances
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Automation everywhere: autonomics and data management
BNCOD'07 Proceedings of the 24th British national conference on Databases
Online monitoring and visualisation of database structural deterioration
International Journal of Autonomic Computing
Improving architecture-based self-adaptation using preemption
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
A bayesian approach to online performance modeling for database appliances using gaussian models
Proceedings of the 8th ACM international conference on Autonomic computing
New algorithms for join and grouping operations
Computer Science - Research and Development
Subquadratic algorithms for workload-aware haar wavelet synopses
FSTTCS '05 Proceedings of the 25th international conference on Foundations of Software Technology and Theoretical Computer Science
Making self-adaptation an engineering reality
Self-star Properties in Complex Information Systems
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Chimera: a declarative language for streaming network traffic analysis
Security'12 Proceedings of the 21st USENIX conference on Security symposium
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Structured Query Language (SQL) has emerged as an industry standard for querying relational database management systems, largely because a user need only specify what data are wanted, not the details of how to access those data. A query optimizer uses a mathematical model of query execution to determine automatically the best way to access and process any given SQL query. This model is heavily dependent upon the optimizer's estimates for the number of rows that will result at each step of the query execution plan (QEP), especially for complex queries involving many predicates and/or operations. These estimates rely upon statistics on the database and modeling assumptions that may or may not be true for a given database. In this paper, we discuss an autonomic query optimizer that automatically self-validates its model without requiring any user interaction to repair incorrect statistics or cardinality estimates. By monitoring queries as they execute, the autonomic optimizer compares the optimizer's estimates with actual cardinalities at each step in a QEP, and computes adjustments to its estimates that may be used during future optimizations of similar queries. Moreover, the detection of estimation errors can also trigger reoptimization of a query in mid-execution. The autonomic refinement of the optimizer's model can result in a reduction of query execution time by orders of magnitude at negligible additional run-time cost. We discuss various research issues and practical considerations that were addressed during our implementation of a first prototype of LEO, a LEarning Optimizer for DB2脗® (Database 2TM) that learns table access cardinalities and for future queries corrects the estimation error for simple predicates by adjusting the database statistics of DB2.