METHOD AND SYSTEM FOR SELF-CALIBRATING PROJECT ESTIMATION MODELS FOR PACKAGED SOFTWARE APPLICATIONS

• US 20080313110A1
• Filed: 06/13/2007
• Published: 12/18/2008
• Est. Priority Date: 06/13/2007
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Lee, Juhnyoung, Sigl, Gerhard, Mohan, Rakesh, Kreamer, Jed, Shivpuriya, Vikas J., Rosinski, Thomas D.
• Granted Patent: US 8,055,606 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 10/06312 Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 10/06313 Resource planning in a project environment
• CPC Class Codes: G06Q 10/06314 Calendaring for a resource
• CPC Class Codes: G06Q 10/1093 Calendar-based scheduling for persons or groups
• CPC Class Codes: G06Q 10/1097 Task assignment

Abstract

An estimation system for deriving multi-dimensional project plans for implementing packaged software applications with self-calibration and refinement of project estimation models, the system includes: a view layer configured to act as a user interface for user inputs and system outputs; a model and control layer configured to implement rules based on a series of estimation and implementation models, and to perform self-calibration and refinement of project estimation models for multi-dimensional project plans; an estimation knowledge base layer configured to hold and derive the series of estimation and implementation models; and wherein the system for self-calibration and refinement of project estimation models for multi-dimensional project plans for implementing packaged software applications is carried out over networks comprising: the Internet, intranets, local area networks (LAN), and wireless local area networks (WLAN).

Claims

1. A method for self-calibration and refinement of project estimation models for implementing packaged software applications, the method comprising:

publishing one or more resultant project estimation models with related data from one or more estimation systems to one or more knowledge repositories and to one or more stakeholders;

analyzing the one or more resultant project estimation models with one or more model refiners that are in signal communication with the one or more knowledge repositories; and

wherein the one or more model refiners calibrate by updating, validating, and refining one or more existing project estimation models and their associated parameters stored and provided by the one or more knowledge repositories, based on the one or more model refiner analyses of the resultant project estimation models.

2. The method of claim 1, wherein the related data comprises:

answers to questionnaires, scenarios with generated schedules, staffing projections, costing reports, pricing reports, and benefit reports.

3. The method of claim 2, wherein the generated schedules include, but are not limited to Gantt charts.

4. The method of claim 1, wherein the stakeholders utilize the published resultant project estimation models as guidelines for future implementations of project plans;

and

wherein the stakeholders can utilize the published resultant project estimation models as a training and educational tool.

5. The method of claim 1, wherein the stakeholders comprise practitioners in the field for which the resultant project estimation models were generated, registered subscribers, and research and development teams in the field of interest.

6. The method of claim 1, wherein the published resultant project estimation models are based on one or more existing project estimation models and their associated parameters stored and provided by the one or more knowledge repositories.

7. The method of claim 1, wherein publishing one or more resultant project estimation models with related data is carried out with one or more publication and subscription engines.

8. The method of claim 7, wherein the one or more publication and subscription engines utilize one or more servers to form communication channels and networks between the estimation system and the one or more stakeholders;

and

wherein the communication channels and networks comprise one or more of email systems, collaborative team rooms, Web sites, wiki sites, real simple syndication (RSS), and with notification; and

wherein the stakeholders have the ability to collaborate and communicate with the other stakeholders over the communication channels and networks.

9. The method of claim 8, wherein the communication channels and networks are utilized for connecting to various sources for gathering information for calibrating and refining the one or more existing project estimation models with the one or more model refiners;

and

wherein the various sources comprise;

empirical data, historical data, the Internet, packaged application software from independent software vendors, information from existing installations, and data mining utilizing crawlers.

10. The method of claim 1, wherein the one or more model refiners utilize machine learning comprising:

supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, transduction, and learning to learn.

11. The method of claim 1, wherein the calibration of one or more existing project estimation models and their associated parameters comprises:

the adjustment of phase parameters, duration parameters, and scoring parameters and values.

12. The method of claim 1, wherein the resultant and existing project estimation models comprise:

normative, constructive, and self-correcting models.

13. An estimation system for deriving multi-dimensional project plans for implementing packaged software applications with self-calibration and refinement of project estimation models, the system comprising:

a view layer configured to act as a user interface for user inputs and system outputs;

a model and control layer configured to implement rules based on a series of estimation and implementation models, and to perform self-calibration and refinement of project estimation models for multi-dimensional project plans;

an estimation knowledge base layer configured to hold and derive the series of estimation and implementation models; and

wherein the system for self-calibration and refinement of project estimation models for multi-dimensional project plans for implementing packaged software applications is carried out over networks comprising;

the Internet, intranets, local area networks (LAN), and wireless local area networks (WLAN).

14. The estimation system of claim 13, wherein the self-calibration and refinement of project estimation models for multi-dimensional project plans is carried out with one or more publication and subscription engines;

wherein the one or more publication and subscription engines are in signal communication with one or more knowledge repositories, one or more model refiners, one or more estimation knowledge bases, and one or more estimation knowledge discoverers and collectors within the estimation knowledge base layer;

wherein the one or more publication and subscription engines publish one or more resultant project estimation models with related data from the estimation system to the one or more knowledge repositories and to one or more stakeholders;

wherein the one or more model refiners analyze the one or more resultant project estimation models; and

wherein the one or more model refiners calibrate by updating, validating, and refining one or more existing project estimation models and their associated parameters stored and provided by the one or more knowledge repositories, based on the one or more model refiner analyses of the resultant project estimation models.

15. The system of claim 14, wherein the related data comprises:

answers to questionnaires, scenarios with generated schedules, staffing projections, costing reports, pricing reports, and benefit reports; and

wherein the generated schedules include, but are not limited to Gantt charts.

16. The system of claim 14, wherein the stakeholders comprise practitioners in the field for which the resultant project estimation model was generated, registered subscribers, and research and development teams in the field of interest;

wherein the stakeholders utilize the published resultant project estimation models as guidelines for future implementations of project plans; and

wherein the stakeholders can utilize the published resultant project estimation models as a training and educational tool.

17. The system of claim 14, wherein the model refiner utilizes machine learning comprising:

supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, transduction, and learning to learn.

18. The system of claim 14, wherein the calibration of one or more existing project estimation models and their associated parameters comprises:

the adjustment of phase parameters, duration parameters, and scoring parameters and values; and

wherein the resultant and existing project estimation models comprise;

normative, constructive, and self-correcting models.

19. The system of claim 14, wherein the one or more publication and subscription engines utilize one or more servers to form communication channels and networks between the estimation system and the one or more stakeholders;

and

wherein the communication channels and networks comprise one or more of email systems, collaborative team rooms, Web sites, wiki sites, real simple syndication (RSS), and with notification; and

wherein the stakeholders have the ability to collaborate and communicate with the other stakeholders over the communication channels and networks.

20. The system of claim 19, wherein the communication channels and networks are utilized by the estimation knowledge discoverer and collector for connecting to various sources for gathering information for calibrating and refining the one or more existing project estimation models with the model refiner;

and

wherein the various sources comprise;

empirical data, historical data, the Internet, packaged application software from independent software vendors, information from existing installations, and data mining utilizing crawlers.