Digital Symposium Collection 2000  

 
 
 
 
 
 

 



















Explaining Differences in Multidimensional Aggregates

Sunita Sarawagi

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Return to Aggregation, Approximation & Explanation

Abstract
Our goal is to enhance multidimensional database systems with advanced mining primitives. Current Online Analytical Processing (OLAP) products provide a minimal set of basic aggregate operators like sum and average and a set of basic navigational operators like drill-downs and roll-ups. These operators have to be driven entirely by the analyst's intuition. Such ad hoc exploration gets tedious and error-prone as data dimensionality and size increases. In earlier work we presented one such advanced primitive where we premined OLAP data for exceptions, summarized the exceptions at appropriate levels, and used them to lead the analyst to the interesting regions.

In this paper we present a second enhancement: a single operator that lets the analyst get summarized reasons for drops or increases observed at an aggregated level. This eliminates the need to manually drill-down for such reasons. We develop an information theoretic formulation for expressing the reasons that is compact and easy to interpret. We design a dynamic programming algorithm that requires only one pass of the data improving significantly over our initial greedy algorithm that required multiple passes. In addition, the algorithm uses a small amount of memory independent of the data size. This allows easy integration with existing OLAP products. We illustrate with our prototype on the DB2/UDB ROLAP product with the Excel Pivot-table frontend. Experiments on this prototype using the OLAP data benchmark demonstrate (1) scalability of our algorithm as the size and dimensionality of the cube increases and (2) feasibility of getting interactive answers even with modest hardware resources.


References

Note: References link to DBLP on the Web.

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Surajit Chaudhuri , Umeshwar Dayal : An Overview of Data Warehousing and OLAP Technology. SIGMOD Record 26(1) : 65-74(1997)
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Jiawei Han , Yongjian Fu : Discovery of Multiple-Level Association Rules from Large Databases. VLDB 1995 : 420-431
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[Mic98b]
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Sunita Sarawagi , Rakesh Agrawal , Nimrod Megiddo : Discovery-Driven Exploration of OLAP Data Cubes. EDBT 1998 : 168-182
[Sof]
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Referenced by

  1. H. V. Jagadish : Review - Explaining Differences in Multidimensional Aggregates. ACM SIGMOD Digital Review 1 : (1999)

BIBTEX

@inproceedings{DBLP:conf/vldb/Sarawagi99,
  author    = {Sunita Sarawagi},
   editor    = {Malcolm P. Atkinson and
                Maria E. Orlowska and
                Patrick Valduriez and
                Stanley B. Zdonik and
                Michael L. Brodie},
   title     = {Explaining Differences in Multidimensional Aggregates},
   booktitle = {VLDB'99, Proceedings of 25th International Conference on Very
                Large Data Bases, September 7-10, 1999, Edinburgh, Scotland,
                UK},
   publisher = {Morgan Kaufmann},
   year      = {1999},
   isbn      = {1-55860-615-5},
   pages     = {42-53},
   crossref  = {DBLP:conf/vldb/99},
   bibsource = {DBLP, http://dblp.uni-trier.de} } },


























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