Anomaly detection in data perspectives

Details Anomaly detection in data perspectives Anomaly detection in data perspectives

2007-01-09

2007-01-09

Nghiem, Michael (Department: 2863)

Data processing: database and file management or data structures

Database design

Data structure types

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.

REFERENCES:
patent: 5998925 (1999-12-01), Shimuzu et al.
patent: 6542878 (2003-04-01), Heckerman et al.
patent: 2002/0127529 (2002-09-01), Cassuto et al.
patent: 2004/0034616 (2004-02-01), Witkowski et al.
patent: 2004/0181519 (2004-09-01), Anwar
patent: 2004/0225648 (2004-11-01), Ransom et al.
patent: 2005/0222928 (2005-10-01), Steier et al.
patent: 2005/0283511 (2005-12-01), Fan et al.
Rakesh Agrawal et al., “Intelligent Information Systems”, IBM Almaden Research Center, viewed Feb. 18, 2004, 1 page.
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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