Explaining Differences in Multidimensional Aggregates
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
iDiff: Informative Summarization of Differences in Multidimensional Aggregates
Data Mining and Knowledge Discovery
User-cognizant multidimensional analysis
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining approximate top-k subspace anomalies in multi-dimensional time-series data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Mining significant change patterns in multidimensional spaces
International Journal of Business Intelligence and Data Mining
Mining convergent and divergent sequences in multidimensional data
International Journal of Business Intelligence and Data Mining
Mining top-K multidimensional gradients
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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The goal of the i3(eye cube) project is to enhance multidimensional database products with a suite of advanced operators to automate data analysis tasks that are currently handled through manual exploration. Most OLAP products are rather simplistic and rely heavily on the user's intuition to manually drive the discovery process. Such ad hoc user-driven exploration gets tedious and error-prone as data dimensionality and size increases. We first investigated how and why analysts currently explore the data cube and then automated them using advanced operators that can be invoked interactively like existing simple operators.Our proposed suite of extensions appear in the form of a toolkit attached with a OLAP product. At this demo we will present three such operators: DIFF, RELAX and INFORM with illustrations from real-life datasets.