Anomaly detection in data perspectives

Details Anomaly detection in data perspectives Anomaly detection in data perspectives

2006-06-20

2006-06-20

Barlow, John (Department: 2863)

Data processing: database and file management or data structures

Database design

Data structure types

C702S179000

Reexamination Certificate

active

07065534

ABSTRACT:
The present invention leverages curve fitting data techniques to provide automatic detection of data anomalies in a “data tube” from a data perspective, allowing, for example, detection of data anomalies such as on-screen, drill down, and drill across data anomalies in, for example, pivot tables and/or OLAP cubes. It determines if data substantially deviates from a predicted value established by a curve fitting process such as, for example, a piece-wise linear function applied to the data tube. A threshold value can also be employed by the present invention to facilitate in determining a degree of deviation necessary before a data value is considered anomalous. The threshold value can be supplied dynamically and/or statically by a system and/or a user via a user interface. Additionally, the present invention provides an indication to a user of the type and location of a detected anomaly from a top level data perspective.

REFERENCES:
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Sunita Sarawagi, et al., Discovery-Driven Exploration of OLAP Cubes, Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology, 1998, pp. 168-182, Springer-Verlag, London, UK.
Rakesh Agrawal et al., “Intelligent Information Systems”, IBM Almadem Research Center, viewed Feb. 18, 2004. 1 page.

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