Online aggregation with tight error bounds in dynamic environments

Abstract OLAP is a category of database technology that allows analysts to gain insight into the aggregation of data by enabling them to gain access to a variety of different views of the information contained in a database. It is very important to provide analysts with guaranteed error bounds for approximate results to aggregation queries in enterprise applications such as decision support systems. We propose a general method of providing tight error bounds for approximate results to OLAP range-sum queries. We perform an extensive experiment on diverse data sets and examine the effectiveness of the proposed method for various data cube dimensions and query sizes.

[1]  Hanan Samet,et al.  The Quadtree and Related Hierarchical Data Structures , 1984, CSUR.

[2]  Divyakant Agrawal,et al.  Flexible Data Cubes for Online Aggregation , 2001, ICDT.

[3]  Ju-Hong Lee,et al.  Dynamic Update Cube for Range-sum Queries , 2001, VLDB.

[4]  Sharad Mehrotra,et al.  Progressive approximate aggregate queries with a multi-resolution tree structure , 2001, SIGMOD '01.

[5]  Jeffrey Scott Vitter,et al.  Approximate computation of multidimensional aggregates of sparse data using wavelets , 1999, SIGMOD '99.

[6]  Marios Hadjieleftheriou,et al.  R-Trees - A Dynamic Index Structure for Spatial Searching , 2008, ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems.

[7]  Divyakant Agrawal,et al.  pCube: Update-efficient online aggregation with progressive feedback and error bounds , 2000, Proceedings. 12th International Conference on Scientific and Statistica Database Management.

[8]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[9]  Viswanath Poosala,et al.  Fast approximate answers to aggregate queries on a data cube , 1999, Proceedings. Eleventh International Conference on Scientific and Statistical Database Management.

[10]  Sridhar Ramaswamy,et al.  Join synopses for approximate query answering , 1999, SIGMOD '99.

[11]  Paul S. Bradley,et al.  Compressed data cubes for OLAP aggregate query approximation on continuous dimensions , 1999, KDD '99.

[12]  Terence R. Smith,et al.  Relative prefix sums: an efficient approach for querying dynamic OLAP data cubes , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[13]  Hans-Jörg Schek,et al.  A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces , 1998, VLDB.

[14]  Nimrod Megiddo,et al.  Range queries in OLAP data cubes , 1997, SIGMOD '97.