SIGMOD '82 Proceedings of the 1982 ACM SIGMOD international conference on Management of data
Statistical Databases: Characteristics, Problems, and some Solutions
VLDB '82 Proceedings of the 8th International Conference on Very Large Data Bases
Computer based management information systems embodying answer accuracy as a user parameter
Computer based management information systems embodying answer accuracy as a user parameter
Rule-based statistical calculations on a database abstract
Rule-based statistical calculations on a database abstract
Directions for AI in the eighties
ACM SIGART Bulletin
Rule/based statistical calculations on a "Database abstract"
SSDBM'81 Proceedings of the 1st LBL Workshop on Statistical database management
Some experiments in evaluation of an expert system for statistical estimation on databases
SSDBM'83 Proceedings of the 2nd international workshop on Proceedings of the Second International Workshop on Statistical Database Management
The effect of join selectives on optimal nesting order
ACM SIGMOD Record
Estimating the size of generalized transitive closures
VLDB '89 Proceedings of the 15th international conference on Very large data bases
Query size estimation by adaptive sampling (extended abstract)
PODS '90 Proceedings of the ninth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Object management in POSTGRES using procedures
OODS '86 Proceedings on the 1986 international workshop on Object-oriented database systems
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Some experiments in evaluation of an expert system for statistical estimation on databases
SSDBM'83 Proceedings of the 2nd international workshop on Proceedings of the Second International Workshop on Statistical Database Management
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The size of data sets subjected to statistical analysis is increasing as computer technology develops. Quick estimates of statistics rather than exact values are becoming increasingly important to analysts. We propose a new technique for estimating statistics on a database, a "top-down" alternative to the "bottom-up" method of sampling. This approach precomputes a set of general-purpose statistics on the database, a "database abstract", and then uses a large set of inference rules to make bounded estimates of other, arbitrary statistics requested by users. The inference rules form a new example of an artificial-intelligence "expert system". There are several important advantages of this approach over sampling methods.