Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
Data cube approximation and histograms via wavelets
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SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A comparison of selectivity estimators for range queries on metric attributes
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
SIGMOD '99 Proceedings of the 1999 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
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
VLDB '88 Proceedings of the 14th 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
Selectivity estimators for multidimensional range queries over real attributes
The VLDB Journal — The International Journal on Very Large Data Bases
On the complexity of division and set joins in the relational algebra
Journal of Computer and System Sciences
Generic database cost models for hierarchical memory systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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This paper tackles the issue of estimating database query space and time complexities. Initially, queries without joins are considered and classified into five categories in accordance with complexity (type and number of clauses) in a progressive manner. The storage space and execution time complexity measures for each category are then derived after translating the queries into their algebraic representations and then deriving possible relations that accounts for the different factors (i.e., clauses found in the statement). Joins were then considered and similar complexity expressions were derived. Some experiments were carried out against a database of four tables that were populated using a data generation tool, and involved monitoring the execution time with the aid of a performance monitoring software, so as to give insights into the 'join' costs. It is shown that the obtained trends exhibit general agreement with the theoretical expressions for both space and time complexity.