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Method for Intelligent Data Mining to augment project scheduling estimates

IP.com Disclosure Number: IPCOM000171736D
Original Publication Date: 2008-Jun-17
Included in the Prior Art Database: 2008-Jun-17
Document File: 1 page(s) / 21K

Publishing Venue

IBM

Abstract

This article proposes an approach that could improve accuracy of estimating projected completion dates for scheduling software completion. The enhancement is to incorporate intelligent data mining to increase the accuracy of estimating projected completion dates.

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Method for Intelligent Data Mining to augment project scheduling estimates

Schedules are not always accurate which can be as a result of not predicting various obstructions the members of the project may encounter. In order to improve the estimate, information from outside the specific task in hand has to be accounted for. Intelligent data mining that can increase the accuracy of estimating projected completion dates should include:
(1) The organizational position of the developer. The higher the employee is in the organizational chart, the more likely they are to have more interruptions and being pulled away to work on side projects or customer issues.
(2) The total number of software defects as well as the number of high ranked software defects assigned to the developer.
(3) Automatic harvesting of the calendar information for calculating schedules - accounting for meetings, vacations, conferences. The current project software requires this information to be input manually by the people who are creating the schedules. The proposed solution is integrated with the calendar.
(4) People commitments to on-going projects and additional responsibilities, such as members of standards and other groups. These commitments do not show up on calendars but will affect the project schedule. This information is harvested from the individual profile (either from a corporate database or being provided by individuals).

The data mining is used...