Anomaly detection in data perspectives
First Claim
1. An automatic data perspective anomaly detection system:
- a data tube component that receives at least one data perspective and processes the at least one data perspective into at least one data tube; and
an anomaly detection component that receives and processes the at least one data tube utilizing a curve fitting process to determine any data anomalies.
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Accused Products
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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Citations
20 Claims
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1. An automatic data perspective anomaly detection system:
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a data tube component that receives at least one data perspective and processes the at least one data perspective into at least one data tube; and
an anomaly detection component that receives and processes the at least one data tube utilizing a curve fitting process to determine any data anomalies. - View Dependent Claims (2, 3, 4, 5)
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6. An automatic data perspective anomaly detection component comprising:
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a data tube component for generating a plurality of data tubes;
an anomaly detection component comprising a curve fitting function component that receives a data tube from the data tube component to determine a suitable curve fitting function to represent data from the data tube;
a data deviation score component that utilizes the curve fitting function to;
derive predicted values for the data from the data tube;
compare the predicted values to actual values for data in the data tubes; and
determine deviation scores based on an amount of deviation from the predicted value; and
an anomaly determination component that receives the deviation scores and utilizes a threshold input to detect anomalous data that surpasses a threshold value. - View Dependent Claims (7, 8, 9, 10, 11, 12, 13)
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14. A method of facilitating automatic data perspective anomaly detection comprising:
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receiving tube data representative of a slice of data from a data perspective where only one dimension varies; and
applying a curve fitting function to the data to automatically detect anomalies in the tube data. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification