SYSTEM AND METHOD FOR CALIBRATING AND EXTRAPOLATING COMPLEXITY METRICS OF INFORMATION TECHNOLOGY MANAGEMENT

• US 20070282644A1
• Filed: 06/05/2006
• Published: 12/06/2007
• Est. Priority Date: 06/05/2006
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Diao, Yixin, Filepp, Robert, Kearney, Robert D., Keller, Alexander
• Granted Patent: US 8,001,068 B2
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/06395 Quality analysis or management
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

The invention broadly and generally provides a method for calibrating the relationship between management-inherent complexity metrics deriving from the management structure and human perceived complexity of information technology management comprising: (a) obtaining a set of management-inherent complexity metrics; (b) obtaining a set of human-perceived complexity metrics; (c) constructing a control model identifying a set of dominant indicators selected from the aforesaid set of management-inherent complexity metrics; (d) establishing a value model mapping from the aforesaid set of dominant indicators to the aforesaid set of human-perceived complexity metrics.

Claims

1. A method for calibrating the relationship between management-inherent complexity metrics deriving from the management structure and human perceived complexity of information technology management comprising:

(a) obtaining a set of management-inherent complexity metrics;

(b) obtaining a set of human-perceived complexity metrics;

(c) constructing a control model identifying a set of dominant indicators selected from said set of management-inherent complexity metrics;

(d) establishing a value model mapping from said set of dominant indicators to said set of human-perceived complexity metrics.

2. A method as set forth in claim 1, further comprising obtaining and validating said control model and said value model for quality assessment.

4. A method as set forth in claim 1, wherein said set of management-inherent complexity metrics comprise at least one of:

(a) execution complexity metrics;

(b) parameter complexity metrics; and

(c) memory complexity metrics.

5. A method as set forth in claim 1, wherein said value model is constructed using a statistical approach.

6. A method as set forth in claim 1, wherein said value model is constructed using linear regression.

7. A method as set forth in claim 1, wherein said value model is constructed using machine learning.

8. A method as set forth in claim 7, wherein said value model is constructed using an artificial neural network;

9. A method as set forth in claim 8, wherein said artificial neural network is a radial basis function.

10. A method as set forth in claim 1, wherein said step of obtaining a set of management inherent complexity metrics comprises at least one of:

(a) obtaining management-inherent complexity metrics from a complexity analysis; and

(b) acquiring human-perceived complexity metrics through controlled user studies.

11. A method as set forth in claim 1, wherein said step of constructing a control model comprises at least one of:

(a) obtaining a subset of management-inherent complexity metrics as a set of dominant indicators under study;

(b) constructing a value model from said set of dominant indicators and said set of human-perceived complexity metrics based on a set of information technology management data; and

(c) evaluating the quality of said value model based on a different set of information technology management data.

12. A method as set forth in claim 11, further comprising obtaining a different subset of management-inherent complexity metrics from said set of dominant indicators under study.

13. A method as set forth in claim 12, further comprising repeating said step of obtaining a different subset of management-inherent complexity metrics as said set of dominant indicators under study until no better set of dominant indicators is found.

3. A method as set forth in 2, further comprising repeating said step of obtaining and validating said control model and said value model for quality assessment.

14. A method for extrapolating from management-inherent complexity metrics to human-perceived complexity of information technology management, said method comprising:

(a) collecting a set of management-inherent complexity metrics;

(b) obtaining a value model;

(c) predicting human-perceived complexity based on said set of management inherent complexity metrics and said value model.

15. A program storage device readable by a digital processing apparatus and having a program of instructions which are tangibly embodied on the storage device and which are executable by the processing apparatus to perform a method for calibrating the relationship between management-inherent complexity metrics deriving from the management structure and human perceived complexity of information technology management, said method comprising:

(a) obtaining a set of management-inherent complexity metrics;

(b) obtaining a set of human-perceived complexity metrics;

(c) constructing a control model identifying a set of dominant indicators selected from said set of management-inherent complexity metrics;

(d) establishing a value model mapping from said set of dominant indicators to said set of human-perceived complexity metrics.