Introduction to algorithms
Optimal histograms for limiting worst-case error propagation in the size of join results
ACM Transactions on Database Systems (TODS)
Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 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
Towards estimation error guarantees for distinct values
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of 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
Distinct Sampling for Highly-Accurate Answers to Distinct Values Queries and Event Reports
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
Sing the truth about ad hoc join costs
The VLDB Journal — The International Journal on Very Large Data Bases
The optimization of queries in relational databases
The optimization of queries in relational databases
Preventing bad plans by bounding the impact of cardinality estimation errors
Proceedings of the VLDB Endowment
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
Efficiently adapting graphical models for selectivity estimation
The VLDB Journal — The International Journal on Very Large Data Bases
RFID-data compression for supporting aggregate queries
ACM Transactions on Database Systems (TODS)
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Virtually all histograms store for each bucket the number of distinct values it contains and their average frequency. In this paper, we question this paradigm. We start out by investigating the estimation precision of three commercial database systems which also follow the above paradigm. It turns out that huge errors are quite common. We then introduce new bucket types and investigate their accuracy when building optimal histograms with them. The results are ambiguous. There is no clear winner among the bucket types. At this point, we (1) switch to heterogeneous histograms, where different buckets of the same histogram possibly are of different types, and (2) design more bucket types. The nice consequence of introducing heterogeneous histograms is that we can guarantee decent upper error bounds while at the same time heterogeneous histograms require far less space than homogeneous histograms.