Abstract
Alert-raising and deviation detection in OLAP and explora-tory search concerns calling the user’s attention to variations and non-uniform data distributions, or directing the user to the most interesting 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 to build, refresh and detect deviations. We also propose an incremental approach and formula for refreshing baselines efficiently. An experimental setup proves the concept and shows its efficiency.
This work was done while Pedro Furtado was visiting University of Tours.
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Notes
- 1.
For the sake of readability, baseline expressions are simplified in the examples: the top levels are dropped in the group-by sets.
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Furtado, P., Nadal, S., Peralta, V., Djedaini, M., Labroche, N., Marcel, P. (2015). Materializing Baseline Views for Deviation Detection Exploratory OLAP. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science(), vol 9263. Springer, Cham. https://doi.org/10.1007/978-3-319-22729-0_19
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