DETECTING AND DISPLAYING EXCEPTIONS IN TABULAR DATA

• US 20080189238A1
• Filed: 02/02/2007
• Published: 08/07/2008
• Est. Priority Date: 02/02/2007
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Crivat, Ioan Bogdan, MacLennan, C. James, Iyer, Raman S.
• Granted Patent: US 7,797,264 B2
• Time in Patent Office: Days
• US Class Current: 707/2
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures

Abstract

Data expressed as tabular data having columns and rows can be analyzed and data determined to be an exception can be flagged. In addition, reasons for flagging such data as exceptions can be presented to a user to facilitate further analysis and action on the data. A predictive analysis component can utilize a clustering algorithm with predictive capabilities to autonomously analyze the data. Periodic re-analysis of the data can be performed to determine if exceptions have changed based on new or modified data.

Claims

1. A system that facilitates automatic detection of exceptions in tabular data, comprising:

an application that contains tabular data expressed as a plurality of rows that include a plurality of columns; and

a predictive analysis component that receives the tabular data and predicts exceptions in the table data utilizing a clustering algorithm with predictive capabilities.

2. The system of claim 1, further comprising a clustering module that groups the plurality of rows into one or more groups and identifies at least one row from the plurality of rows that is not grouped with at least one other row from the plurality of rows.

3. The system of claim 2, further comprising a predictive module that determines at least one column of the identified at least one row that caused the identified at least one row to not be grouped with the at least one other row.

4. The system of claim 2, the clustering module groups the plurality of rows through utilization of a probabilistic clustering algorithm.

5. The system of claim 1, further comprising a refresh module that periodically invokes the predictive analysis component to reevaluate the predicted exceptions when tabular data is modified, deleted or added.

6. The system of claim 1, further comprising a display component that presents the exceptions to a user.

7. The system of claim 1, the predictive analysis component is executed on a server independent of the location of the tabular data.

8. The system of claim 1, further comprising an interface component that allows a user to interact with the tabular data or the predictive analysis component.

9. The system of claim 1, the tabular data is contained in a spreadsheet application.

10. The system of claim 1, the tabular data is contained in an XML application.

11. A method for exception detection, comprising:

building a predictive clustering model on tabular data;

comparing each row contained in the tabular data to the clustering model;

ascertaining one or more row that is an exception; and

presenting the exception to a user.

12. The method of claim 11, building a predictive clustering model on tabular data further comprising:

detecting groups of rows with similar characteristics; and

detecting rows unlike most common groups of rows.

13. The method of claim 11, ascertaining one or more row that is an exception further comprising:

using a threshold to mark a row as exception; and

comparing a value computed for each row with the threshold.

14. The method of claim 11, ascertaining one or more row that is an exception further comprising marking a row having a largest distance from other rows within the clustering model as the exception.

15. The method of claim 11, further comprising:

periodically updating the clustering model;

determining if a different row is a new exception; and

presenting the new exception to a user.

16. The method of claim of claim 11, building a predictive clustering model on tabular data is performed on a machine independent of the location of the data.

17. A computer executable system that provides autonomous analysis of tabular data, comprising:

means for building a model for tabular data;

means for comparing each row contained in the tabular data against the model;

means for detecting a row as an outlier;

means for identifying at least one column in the row detected an outlier that caused the row to be the outlier.

18. The computer executable system of claim 17, the means for detecting a row as an outlier utilizes a probabilistic clustering algorithm.

19. The computer executable system of claim 17, further comprising means for presenting the outlier and the identified at least one column to a user.

20. The computer executable system of claim 17, further comprising a means for automatically refreshing the model when a change to the tabular data is detected.