Anomaly detection in data perspectives

Data processing: database and file management or data structures – Database design – Data structure types

Reexamination Certificate

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Reexamination Certificate

active

11299539

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.

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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.

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