METHOD AND SYSTEM FOR MODEL-DRIVEN APPROACHES TO GENERIC PROJECT ESTIMATION MODELS FOR PACKAGED SOFTWARE APPLICATIONS

• US 20080313008A1
• Filed: 06/13/2007
• Published: 12/18/2008
• Est. Priority Date: 06/13/2007
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Lee, Juhnyoung, Sigl, Gerhard, Mohan, Rakesh, Shivpuriya, Vikas J., Rosinski, Thomas D.
• Application Number:
  US11/762,122
• Publication Number:
  US 20080313008A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06313 Resource planning in a project environment
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A project estimation system with a model-driven approach to the generation of generic project estimation models for packaged software applications, 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; an estimation knowledge base layer configured to hold, obtain and derive the series of estimation and implementation models; and wherein the system for a model-driven approach to the generation of generic project estimation models for 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 a model-driven approach to the generation of generic project estimation models for packaged software applications, the method comprising:

connecting to a series of knowledge and information sources;

collecting knowledge, data, and information from the series of sources;

culling and analyzing the collected knowledge, data, and information;

validating the culled and analyzed knowledge, data, and information; and

forming one or more generic estimation models based on the validated knowledge, data, and information.

2. The method of claim 1, wherein the connecting to and collecting from a series of knowledge and information sources is facilitated with one or more estimation knowledge discoverer and collectors;

wherein the collected knowledge, data, and information is placed in one or more knowledge repositories;

wherein culling, analyzing, and validating is facilitated with one or more model refiners in signal communication with the one or more estimation knowledge discoverer and collectors via the one or more knowledge repositories;

wherein the portions of the knowledge, data, and information that are validated are learned by the one or more model refiners;

wherein the one or more model refiners are in signal communication with one or more estimation knowledge bases in a project estimation system; and

wherein the one or more model refiners convey the newly learned Knowledge, data, and information as parameters to one or more estimation models held in the one or more estimation knowledge bases to form the one or more generic estimation models.

3. The method of claim 2, wherein the series of knowledge and information sources comprise:

empirical data from the most recent project plan generated by the project estimation system, existing empirical data based on previous project plans generated by the project estimation system and other previous project plans held in the one or more knowledge repositories, and one or more external sources;

wherein data mining is employed to collect knowledge, data, and information from the one or more external sources; and

wherein crawlers are utilized to conduct the data mining.

4. The method of claim 2, wherein the estimation knowledge discoverer and collector comprise a packaged application crawler and a model discoverer;

wherein the packaged application crawler searches and finds one or more external installations of packaged software applications;

wherein the packaged application crawler collects relevant information on model parameters of the one or more external installations of packaged software applications; and

wherein the model discoverer analyzes the information collected by the packaged application crawler and derives models and estimation parameters for packaged software implantations.

5. The method of claim 4, wherein the model discoverer derives one or more estimation system models in a machine interpretable modeling language including, but not limited to, the unified modeling language (UML);

and

wherein the one or more UML models are used in a model-driven architecture (MDA).

6. The method of claim 5, wherein one or more model transformers import the one or more UML models and estimation parameters derived from the one or more external installations of packaged software applications, and transforms the one or more UML models into a single universal model for project estimation;

wherein the one or more model transformers serve as an automated generator of estimation tools; and

wherein the one or more model transformers generate basic code for a platform independent estimation system for packaged application projects.

7. The method of claim 6, wherein the one or more model transformers utilize one or more model-driven transformation packages including, but not limited to, an eclipse-modeling framework (EMF) to import the UML models and estimation parameter values.

8. A method for a model-driven approach to the generation of generic project estimation models for packaged software applications, the method comprising:

connecting to a series of knowledge and information sources;

collecting knowledge, data, and information from the series of sources;

culling and analyzing the collected knowledge, data, and information;

validating the culled and analyzed knowledge, data, and information; and

forming one or more generic estimation models based on the validated knowledge, data, and information;

wherein the connecting to and collecting from a series of knowledge and information sources is facilitated with one or more estimation knowledge discoverer and collectors;

wherein the collected knowledge, data, and information is placed in one or more knowledge repositories;

wherein culling, analyzing, and validating is facilitated with one or more model refiners in signal communication with the one or more estimation knowledge discoverer and collectors via the one or more knowledge repositories;

wherein the portions of the knowledge, data, and information that are validated are learned by the one or more model refiners;

wherein the one or more model refiners are in signal communication with one or more estimation knowledge bases in a project estimation system;

wherein the one or more model refiners convey the newly learned knowledge, data, and information as parameters to one or more estimation models held in the one or more estimation knowledge bases to form the one or more generic estimation models.

9. The method of claim 8, wherein the series of knowledge and information sources comprise:

empirical data from the most recent project plan generated by the project estimation system, existing empirical data based on previous project plans generated by the project estimation system and other previous project plans held in the one or more knowledge repositories, and one or more external sources;

wherein data mining is employed to collect knowledge, data, and information from the one or more external sources; and

wherein crawlers are utilized to conduct the data mining.

10. The method of claim 8, wherein the estimation knowledge discoverer and collector comprise a packaged application crawler and a model discoverer;

wherein the packaged application crawler searches and finds one or more external installations of packaged software applications;

wherein the packaged application crawler collects relevant information on model parameters of the one or more external installations of packaged software applications;

wherein the model discoverer analyzes the information collected by the packaged application crawler and derives models and estimation parameters for packaged software implantations.

11. The method of claim 10, wherein the model discoverer derives one or more estimation system models in a machine interpretable modeling language including, but not limited to, the unified modeling language (UML);

and

wherein the one or more UML models are used in a model-driven architecture (MDA).

12. The method of claim 11, wherein one or more model transformers import the one or more UML models and estimation parameters derived from the one or more external installations of packaged software applications, and transforms the one or more UML models into a single universal model for project estimation;

wherein the one or more model transformer serves as an automated generator of estimation tools; and

wherein the one or more model transformers generate basic code for a platform independent estimation system for packaged application projects.

13. The method of claim 12, wherein the model transformers utilize one or more model-driven transformation packages including, but not limited to, an eclipse-modeling framework (EMF) to import the UML models and estimation parameter values.

14. A project estimation system with a model-driven approach to the generation of generic project estimation models for packaged software applications, 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;

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

wherein the system for a model-driven approach to the generation of generic project estimation models for packaged software applications is carried out over networks comprising;

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

15. The system of claim 14, wherein the model-driven approach to the generation of generic project estimation models for packaged software application is carried out with one or more estimation knowledge discoverer and collectors in signal communication with one or more knowledge repositories, model refiners, and estimation knowledge bases within in the estimation knowledge base layer;

wherein the one or more estimation knowledge discoverer and collectors connects to a series of knowledge and information sources to collect knowledge, data, and information;

wherein the one or more model refiners culls, analyzes, and validates the collected knowledge, data, and information;

wherein the one or more model refiners learn the portions of the knowledge, data, and information that has been validated; and

wherein the one or more model refiners convey the learned knowledge, data, and information as parameters to one or more estimation models held in the one or more estimation knowledge bases to form the one or more generic estimation models.

16. The system of claim 15, wherein the series of knowledge and information sources comprise:

empirical data from the most recent project plan generated by the project estimation system, existing empirical data based on previous project plans generated by the project estimation system and other previous project plans held in the one or more knowledge repositories, and one or more external sources;

wherein data mining is employed to collect knowledge, data, and information from the one or more external sources; and

wherein crawlers are utilized to conduct the data mining.

17. The system of claim 15, wherein the estimation knowledge discoverer and collector comprise a packaged application crawler and a model discoverer;

wherein the packaged application crawler searches and finds one or more external installations of packaged software applications;

wherein the packaged application crawler collects relevant information on model parameters of the one or more external installations of packaged software applications; and

wherein the model discoverer analyzes the information collected by the packaged application crawler and derives models and estimation parameters for packaged software implantations.

18. The system of claim 17, wherein the model discoverer derives one or more estimation system models in a machine interpretable modeling language including, but not limited to, the unified modeling language (UML);

and

wherein the one or more UML models are used in a model-driven architecture (MDA).

19. The system of claim 18, wherein one or more model transformers import the one or more UML models and estimation parameters derived from the one or more external installations of packaged software applications, and transforms the one or more UML models into a single universal model for project estimation;

wherein the one or more model transformers serve as an automated generator of estimation tools; and

wherein the one or more model transformers generate basic code in graphical user interface form for a platform independent estimation system for packaged application projects.

20. The system of claim 19, wherein the model transformers utilize one or more model-driven transformation packages including, but not limited to, an eclipse-modeling framework (EMF) to import the UML models and estimation parameter values.