Materializing Baseline Views for Deviation Detection Exploratory OLAP
Résumé
Alert-raising and deviation detection in OLAP and explora-
tory search concern calling the attention of the user to variations and
non-uniform data distributions, or directing the user to the most in-
teresting exploration of the data. In this paper, we are interested in
the ability of a data warehouse to monitor continuously new data and
to update accordingly a particular type of materialized views recording
statistics, called baselines. It should be possible to detect deviations at
various levels of aggregation, and baselines should be fully integrated
into the database. We propose Multi-level Baseline Materialized Views
(BMV), including the mechanisms for construction, refreshing and de-
tecting deviation. We also propose an incremental approach and formula
for refreshing baselines eciently. An experimental setup proves the con-
cept and shows its eciency.