Recursive estimation and time-series analysis: an introduction
Recursive estimation and time-series analysis: an introduction
Adaptive selectivity estimation using query feedback
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
The data warehouse toolkit: practical techniques for building dimensional data warehouses
The data warehouse toolkit: practical techniques for building dimensional data warehouses
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
OLAP, relational, and multidimensional database systems
ACM SIGMOD Record
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Quasi-cubes: exploiting approximations in multidimensional databases
ACM SIGMOD Record
Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Join synopses for approximate query answering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Ripple joins for online aggregation
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
Using approximations to scale exploratory data analysis in datacubes
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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Data warehouses are becoming a powerful tool to analyze enterprise data. A critical demand imposed by the users of data warehouses is that the time to get an answer (latency) after posing a query is to be as short as possible. It is arguable that a quick, albeit approximate, answer that can be refined over time is much better than a perfect answer for which a user has to wait a long time. In this paper we addressed the issue of online support for data warehouse queries, meaning the ability to reduce the latency of the answer at the expense of having an approximate answer that can be refined as the user is looking at it. Previous work has address the online support by using sampling techniques. We argue that a better way is to preclassify the cells of the data cube into error bins and bring the target data for a query in "waves," i.e., by fetching the data in those bins one after the other. The cells are classified into bins by means of the usage of a data model (e.g., linear regression, log-linear models) that allows the system to obtain an approximate value for each of the data cube cells. The difference between the estimated value and the true value is the estimation error, and its magnitude determines to which bin the cell belongs. The estimated value given by the model serves to give a very quick, yet approximate answer, that will be refined online by bringing cells from the error bins. Experiments show that this technique is a good way to support online aggregation.