Automatic monitoring and statistical analysis of dynamic process metrics to expose meaningful changes

• US 20050138020A1
• Filed: 12/17/2004
• Published: 06/23/2005
• Est. Priority Date: 12/19/2003
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: ProClarity Corp. (Microsoft Corporation)
• Inventors: Lokken, Robert C.
• Granted Patent: US 7,072,899 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

A selection module allows a user to specify at least one measure to be monitored in at least one dimension of a dimensional hierarchy. A control limit calculator extracts, for each specified measure and for each specified dimension, a time series from a multidimensional database for the specified measure in the specified dimension and automatically calculates one or more control limits for the specified measure in the specified dimension based on the extracted time series using a Statistical Process Control (SPC) technique. Thereafter, a monitoring module monitors newly acquired data including each specified measure in each specified dimension for an out-of-limits condition based on one or more automatically-calculated control limits. An alert module triggers an alert in response to an out-of-limits condition being detected.

Claims

1. A computer-implemented method comprising:

allowing a user to specify at least one measure to be monitored in at least one dimension of a dimensional hierarchy;

for each specified measure and for each specified dimension;

extracting a time series from a multidimensional database for the specified measure in the specified dimension;

automatically calculating one or more control limits for the specified measure in the specified dimension based on the extracted time series using a Statistical Process Control (SPC) technique; and

storing each automatically-calculated control limit.

3. The method of claim 1, further comprising:

allowing the user to specify a detection cycle, wherein monitoring further comprises periodically obtaining current data for each specified measure in each specified dimension according to the specified detection cycle.

4. The method of claim 1, further comprising:

allowing the user to specify an alert delivery method, wherein triggering further comprises delivering an alert to the user via the specified alert delivery method.

5. The method of claim 4, wherein delivering an alert comprises delivering the alert via an e-mail service.

6. The method of claim 4, wherein delivering an alert comprises delivering the alert via a Short Message Service (SMS).

7. The method of claim 4, wherein delivering an alert comprises delivering the alert via a paging service.

8. The method of claim 1, further comprising:

automatically recalculating at least one control limit after eliminating one or more outliers from existing data.

10. The method of claim 1, wherein automatically calculating one or more control limits comprises automatically calculating a control limit that is a function of an average and a standard deviation of the time series.

11. The method of claim 1, wherein automatically calculating one or more control limits comprises automatically calculating an upper control limit (UCL) according to the formula:

$\mathrm{UCL}=\stackrel{\_}{X}+Z<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\sigma <br><br>}$ $\mathrm{where}$ $\stackrel{\_}{X}=\frac{\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>X_i}{n}$ $\mathrm{\sigma <br><br>}=\sqrt{\frac{\sum <br><br>{\left(X_i-\stackrel{\_}{X}\right)}^2}{n-1}}$

X_i is a measure in the time series,

n is the number of measures in the time series, and

Z is tolerance factor.

12. The method of claim 1, wherein automatically calculating one or more control limits comprises automatically calculating a lower control limit (UCL) according to the formula:

$\mathrm{LCL}=\stackrel{\_}{X}-Z<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\sigma <br><br>}$ $\mathrm{where}$ $\stackrel{\_}{X}=\frac{\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>X_i}{n}$ $\mathrm{\sigma <br><br>}=\sqrt{\frac{\sum <br><br>{\left(X_i-\stackrel{\_}{X}\right)}^2}{n-1}}$

X_i is a measure in the time series,

n is the number of measures in the time series, and

Z is a tolerance factor.

2. The method of claim 2, further comprising:

monitoring newly acquired data including each specified measure in each specified dimension for an out-of-limits condition based on one or more stored control limits; and

triggering an alert in response to an out-of-limits condition being detected.

9. The method of claim 2, further comprising:

automatically recalculating at least one control limit based on newly acquired data.

13. A computer program product comprising a computer-readable medium, the computer-readable medium including:

program code for allowing a user to specify at least one measure to be monitored in at least one dimension of a dimensional hierarchy;

program code for extracting, for each specified measure and for each specified dimension, a time series from a multidimensional database for the specified measure in the specified dimension and automatically calculating, one or more control limits for the specified measure in the specified dimension based on the extracted time series using a Statistical Process Control (SPC) technique;

program code for monitoring newly acquired data including each specified measure in each specified dimension for an out-of-limits condition based on one or more automatically-calculated control limits; and

program code for triggering an alert in response to an out-of-limits condition being detected.

14. The computer program product of claim 13, wherein the computer-readable medium further comprises:

program code for allowing the user to specify a detection cycle, wherein monitoring further comprises periodically obtaining current data for each specified measure in each specified dimension according to the specified detection cycle.

15. The computer program product of claim 13, wherein the computer-readable medium further comprises:

program code for allowing the user to specify an alert delivery method, wherein the program code for triggering an alert further comprises program code for delivering an alert to the user via the specified alert delivery method.

16. The computer program product of claim 15, wherein the program code for delivering an alert comprises program code for delivering the alert via an e-mail service.

17. The computer program product of claim 15, wherein the program code for delivering an alert comprises program code for delivering the alert via a Short Message Service (SMS).

18. The computer program product of claim 15, wherein the program code for delivering an alert comprises program code for delivering the alert via a paging service.

19. The computer program product of claim 13, wherein the computer-readable medium further comprises:

program code for automatically recalculating at least one control limit after eliminating one or more outliers from existing data.

20. The computer program product of claim 13, wherein the computer-readable medium further comprises:

program code for automatically recalculating at least one control limit based on newly acquired data.

21. The computer program product of claim 13, wherein the program code for automatically calculating one or more control limits comprises program code for automatically calculating a control limit that is a function of an average and a standard deviation of the time series.

22. The computer program product of claim 13, wherein the program code for automatically calculating one or more control limits comprises program code for automatically calculating an upper control limit (UCL) according to the formula:

$\mathrm{UCL}=\stackrel{\_}{X}+Z<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\sigma <br><br>}$ $\mathrm{where}$ $\stackrel{\_}{X}=\frac{\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>X_i}{n}$ $\mathrm{\sigma <br><br>}=\sqrt{\frac{\sum <br><br>{\left(X_i-\stackrel{\_}{X}\right)}^2}{n-1}}$

X_i is a measure in the time series,

n is the number of measures in the time series, and

Z is tolerance factor.

23. The computer program product of claim 13, wherein the program code for automatically calculating one or more control limits comprises program code for automatically calculating a lower control limit (UCL) according to the formula:

$\mathrm{LCL}=\stackrel{\_}{X}-Z<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\sigma <br><br>}$ $\mathrm{where}$ $\stackrel{\_}{X}=\frac{\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>X_i}{n}$ $\mathrm{\sigma <br><br>}=\sqrt{\frac{\sum <br><br>{\left(X_i-\stackrel{\_}{X}\right)}^2}{n-1}}$

X_i is a measure in the time series,

n is the number of measures in the time series, and

Z is a tolerance factor.

24. A system comprising:

a selection module to allow a user to specify at least one measure to be monitored in at least one dimension of a dimensional hierarchy;

a control limit calculator to extract, for each specified measure and for each specified dimension, a time series from a multidimensional database for the specified measure in the specified dimension and automatically calculate one or more control limits for the specified measure in the specified dimension based on the extracted time series using a Statistical Process Control (SPC) technique;

a monitoring module to monitor, for newly acquired data, each specified measure in each specified dimension for an out-of-limits condition based on one or more automatically-calculated control limits; and

an alert module to trigger an alert in response to an out-of-limits condition being detected.

25. The system of claim 24, wherein the selection module is to allow the user to specify a detection cycle, and wherein the monitoring module is to periodically obtain current data for each specified measure in each specified dimension according to the specified detection cycle.

26. The system of claim 24, wherein the selection module is to allow the user to specify an alert delivery service, and wherein the alert module is to deliver an alert to the user via the specified delivery service.

27. The system of claim 26, wherein the specified delivery service comprises e-mail service.

28. The system of claim 26, wherein the specified delivery service comprises a Short Message Service (SMS).

29. The system of claim 26, wherein the specified delivery service comprises a paging service.

30. The system of claim 24, wherein the control limit calculator is to automatically recalculate at least one control limit after eliminating one or more outliers from existing data.

31. The system of claim 24, wherein the control limit calculator is to automatically recalculate at least one control limit based on newly acquired data.

32. The system of claim 24, wherein the control limit calculator is to automatically calculate a control limit that is a function of an average and a standard deviation of the time series.

33. The system of claim 24, wherein the control limit calculator is to automatically calculate an upper control limit (UCL) according to the formula:

$\mathrm{UCL}=\stackrel{\_}{X}+Z<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\sigma <br><br>}$ $\mathrm{where}$ $\stackrel{\_}{X}=\frac{\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>X_i}{n}$ $\mathrm{\sigma <br><br>}=\sqrt{\frac{\sum <br><br>{\left(X_i-\stackrel{\_}{X}\right)}^2}{n-1}}$

X_i is a measure in the time series,

n is the number of measures in the time series, and

Z is tolerance factor.

34. The system of claim 24, wherein the control limit calculator is to automatically calculate a lower control limit (UCL) according to the formula:

$\mathrm{LCL}=\stackrel{\_}{X}-Z<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\sigma <br><br>}$ $\mathrm{where}$ $\stackrel{\_}{X}=\frac{\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>X_i}{n}$ $\mathrm{\sigma <br><br>}=\sqrt{\frac{\sum <br><br>{\left(X_i-\stackrel{\_}{X}\right)}^2}{n-1}}$

X_i is a measure in the time series,

n is the number of measures in the time series, and

Z is a tolerance factor.