Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Analytical profile estimation in database systems
Information Systems
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
Introduction to inference for Bayesian networks
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Join synopses for approximate query answering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
The Aqua approximate query answering system
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Compressed data cubes for OLAP aggregate query approximation on continuous dimensions
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate Query Answering Using Data Warehouse Striping
Journal of Intelligent Information Systems - Special issue on data warehousing and knowledge discovery
Approximate query processing using wavelets
The VLDB Journal — The International Journal on Very Large Data Bases
Approximate Query Answering with Frequent Sets and Maximum Entropy
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Fundamentals of Database Systems (5th Edition)
Fundamentals of Database Systems (5th Edition)
Selectivity estimation of range queries based on data density approximation via cosine series
Data & Knowledge Engineering
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A probabilistic model for data cube compression and query approximation
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
TuG synopses for approximate query answering
ACM Transactions on Database Systems (TODS)
AQUAGP: approximate QUery answers using genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
| Hi-index | 0.00 |
The methodologies used in approximate query processing are able to provide fast responses to queries that require high computational time in the decision making process. However, the approximate answers are affected with a small quantity of error. For this reason, it is important to provide also an accuracy of the approximate value, that is, a confidence degree of the approximation. In this paper, we present a probabilistic model that can be used in order to provide the accuracy measure in a methodology based on polynomial approximation. This probabilistic model is a Bayesian network able to estimate the relative error of the approximate answers.