COMPLEX EXPONENTIAL SMOOTHING FOR IDENTIFYING PATTERNS IN BUSINESS DATA

• US 20080140468A1
• Filed: 12/06/2006
• Published: 06/12/2008
• Est. Priority Date: 12/06/2006
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Selby, David A., Ramsey, Mark S., Todd, Stephen J.
• Application Number:
  US11/567,329
• Publication Number:
  US 20080140468A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 20/403 Solvency checks
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03

Abstract

A system, method and program product for detecting patterns. A system is provided that includes: a monitor for capturing event values from an entity; a running value calculation system that calculates a new running value based on a previous running value using complex exponential smoothing, wherein both the new running value and previous running value are complex numbers; and an analysis system for recognizing patterns by analyzing the new running value.

Claims

1. A system for detecting patterns, comprising:

a monitor for capturing event values from an entity;

a running value calculation system for calculating a new running value based on a previous running value using complex exponential smoothing, wherein both the new running value and previous running value are complex numbers; and

an analysis system for recognizing patterns by analyzing the new running value.

2. The system of claim 1, wherein event values comprise business event data selected from the group consisting of:

financial transactions, network transaction, and operational transactions.

3. The system of claim 1, wherein for event values captured at regular time periods, a new running value RV_N for a captured event value v is calculated using:

RV_N=KC*RV_P+(1−

K)*v,

where;

K=0.5**(1/(W*N)),

C=cos(2*pi/W)+i*sin(2*pi/W),

RV_P is the previous running value,

W is a selected wavelength, and

N is a selected wavelength number.

4. The system of claim 1, wherein for event values captured at irregular time periods, a new running value RV_N for a captured event value v is calculated using:

RV_N=KC**T*RV_P+(1−

K**T)*v,

where;

K=0.5**(1/(W*N)),

C=cos(2*pi/W)+i*sin(2*pi/W),

RV_P is the previous running value,

W is a selected wavelength, and

N is a selected wavelength number.

5. The system of claim 1, further comprising a user interface for managing and configuring a set of monitors.

6. The system of claim 1, further comprising a dynamic reconfiguration system for automatically reconfiguring the monitor based on an analysis of the pattern analysis system.

7. The system of claim 1, further comprising a plurality of monitors in which an a running value for each monitor is tracked in an array of the form RV[e, W, N], where e is an entity, W is a selected wavelength, and N is a selected wavelength number.

8. A computer program product stored on a computer readable medium for detecting patterns, comprising:

program code configured for capturing event values from an entity;

program code configured for calculating a new running value based on a previous running value using complex exponential smoothing; and

program code configured for recognizing patterns by analyzing at least one of a strength and a phase the new running value.

9. The program product of claim 8, wherein event values comprise business event data selected from the group consisting of:

financial transactions, network transaction, and operational transactions.

10. The program product of claim 8, wherein for event values captured at regular time periods, a new running value RV_N for a captured event value v is calculated using:

RV_N=KC*RV_P+(1−

K)*v,

where;

K=0.5**(1/(W*N)),

C=cos(2*pi/W)+i*sin(2*pi/W),

RV_P is the previous running value,

W is a selected wavelength, and

N is a selected wavelength number.

11. The program product of claim 8, wherein for event values captured at irregular time periods, a new running value RV_N for a captured event value v is calculated using:

RV_N=KC**T*RV_P+(1−

K**T)*v,

where;

K=0.5**(1/(W*N)),

C=cos(2*pi/W)+i*sin(2*pi/W),

RV_P is the previous running value,

W is a selected wavelength, and

N is a selected wavelength number.

12. The program product of claim 8, further comprising program code configured for providing a user interface for managing and configuring a set of monitors, wherein each monitor is configured to capture event values from an entity.

13. The program product of claim 12, further comprising a dynamic reconfiguration system for automatically reconfiguring a monitor based on an analysis.

14. The program product of claim 12, wherein a running value for each monitor is tracked in an array of the form RV[e, W, N], where e is an entity, W is a selected wavelength, and N is a selected wavelength number.

15. A method of detecting patterns in business event data, comprising:

selecting a wavelength and wavelength number;

capturing an event value;

calculating a new running value based on the event value, wavelength, wavelength number and a previous running value using complex exponential smoothing; and

analyzing the new running value to determine an existence of a pattern.

16. The method of claim 15, wherein the event value comprises business event data selected from the group consisting of:

financial transactions, network transaction, and operational transactions.

17. The method of claim 15, wherein for event values captured at regular time periods, a new running value RV_N for a captured event value v is calculated using:

RV_N=KC*RV_P+(1−

K)*v,

where;

K=0.5**(1/(W*N)),

C=cos(2*pi/W)+i*sin(2*pi/W),

RV_P is the previous running value,

W is a selected wavelength, and

N is a selected wavelength number.

18. The method of claim 15, wherein for event values captured at irregular time periods, a new running value RV_N for a captured event value v is calculated using:

RV_N=KC**T*RV_P+(1−

K**T)*v,

where;

K=0.5**(1/(W*N)),

C=cos(2*pi/W)+i*sin(2*pi/W),

RV_P is the previous running value,

W is a selected wavelength, and

N is a selected wavelength number.

19. The method of claim 15, further comprising providing a user interface for managing and configuring a set of monitors, wherein each monitor is configured to capture event values from an entity.

20. The method of claim 19, further comprising automatically reconfiguring a monitor based on an analysis of the new running value.

21. A method for deploying pattern detection system, comprising:

providing a computer infrastructure being operable to;

capture event values from an entity;

calculate a new running value based on a previous running value using complex exponential smoothing; and

search for patterns by analyzing at least one of a strength and a phase of the new running value.