Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Scaling mining algorithms to large databases
Communications of the ACM - Evolving data mining into solutions for insights
Optimal Histograms with Quality Guarantees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Histogram-Based Approximation of Set-Valued Query-Answers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Approximate Query Processing: Taming the TeraBytes
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
Approximate query processing using wavelets
The VLDB Journal — The International Journal on Very Large Data Bases
Wavelet synopses for general error metrics
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2004
The history of histograms (abridged)
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Hierarchical synopses with optimal error guarantees
ACM Transactions on Database Systems (TODS)
Fast and effective histogram construction
Proceedings of the 18th ACM conference on Information and knowledge management
Optimality and scalability in lattice histogram construction
Proceedings of the VLDB Endowment
Efficient approximations of conjunctive queries
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
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The need for approximations of information has become very critical in the recent past. From traditional query optimization to newer functionality like user feedback and knowledge discovery, data management systems require quick delivery of approximate data in order to serve their goals. There are several techniques that have been proposed to solve the problem, each with its own strengths and weaknesses. In this paper, we take a look at some of the most important data approximation problems and attempt to put them in a common framework and identify their similarities and differences. We then hint on some open and challenging problems that we believe are worth investigating.