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You are home at night and have switched on the TV after dinner and one of the many CSI or Law & Order shows is on. The ATM camera has captured a snapshot of the “bad guy” and it is now thrown into ...
You are home at night and have switched on the TV after dinner and one of the many CSI or Law & Order shows is on. The ATM camera has captured a snapshot of the “bad guy” and it is now thrown into the photo recognition software and a potential suspect’s picture and profile appears on the screen. The underlying software uses a set of mathematical techniques to create a set of eigenpictures that make this otherwise complex pattern recognition possible. Similarly eigenvoices are created in voice recognition systems.
My most recent paper explores one possible way to identify “patterns” in complex projects utilizing the same techniques.
Large scale projects, especially capital construction projects, are notoriously difficult to manage. Project managers, or other stakeholders, require techniques to quickly assess a current state of their project and to better anticipate likely performance trajectories. Ideally, project managers would be able to quickly compare their project against historical projects or known best practices in order to benefit from the wealth of prior experience that exists. Unfortunately, there are very few techniques currently available to project managers that allow them to recognize the project’s state as being similar to circumstances related to other projects.
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