Equi-depth multidimensional histograms
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Estimating the size of generalized transitive closures
VLDB '89 Proceedings of the 15th international conference on Very large data bases
On the expected size of recursive Datalog queries
PODS '91 Proceedings of the tenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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
Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Rapid bushy join-order optimization with Cartesian products
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Implications of certain assumptions in database performance evauation
ACM Transactions on Database Systems (TODS)
Independence is good: dependency-based histogram synopses for high-dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Exploiting statistics on query expressions for optimization
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Dynamic multidimensional histograms
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
The VLDB Journal — The International Journal on Very Large Data Bases - Prototypes of deductive database systems
Measuring the Complexity of Join Enumeration in Query Optimization
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
Universality of Serial Histograms
VLDB '93 Proceedings of the 19th 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
Join Enumeration in a Memory-Constrained Environment
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Database Systems: An Application Oriented Approach, Complete Version (2nd Edition)
Database Systems: An Application Oriented Approach, Complete Version (2nd Edition)
Graph-based synopses for relational selectivity estimation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Optimal top-down join enumeration
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
The history of histograms (abridged)
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Dynamic programming strikes back
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Adding magic to an optimising datalog compiler
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Deriving predicate statistics in datalog
Proceedings of the 12th international ACM SIGPLAN symposium on Principles and practice of declarative programming
Non-termination analysis and cost-based query optimization of logic programs
RR'12 Proceedings of the 6th international conference on Web Reasoning and Rule Systems
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Database query optimizers rely on data statistics in selecting query execution plans and rule-based systems can greatly benefit from such optimizations as well. To this end, one first needs to collect data statistics for base and propagate them to derived predicates. However, there are two difficulties: dependencies among arguments and recursion. Earlier we developed an algorithm, called SDP, for estimating Datalog query sizes efficiently by estimating statistical dependency for both base and derived predicates [16]. Base predicate statistics were summarized as dependency matrices, while the statistics for derived predicate were estimated by abstract evaluation of rules over the dependency matrices. This previous work had several limitations. First, it only considered Datalog predicates. Second, only predicates of arity at most 2 were allowed--a very serious limitation of the approach. The present paper extends SDP to general rules and n-ary predicates. It also handles negation and mutual recursions as well as other operations. We also report on our experiments with SDP.