Month: October 2011

Principles of Modeling: the Concreteness Principle

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In an earlier post, I talked about the Jargon Principle, one of three principles I learned in 1994 and that have not only helped make me a better modeler, but that I have found to be very valuable in many other situations as well. Today, I will cover the second of those principles. The Concreteness Principle Again, not in the exact wording I learned it but paraphrased, here is “The Concreteness Principle”: “When communicating with the domain expert, the analyst shall avoid all abstract statements and questions and instead use concrete examples as the base of all communications; these examples…

Principles of Modeling: the Jargon Principle

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In one of my previous posts, I discussed whether data modeling is art or science, and I concluded that, unfortunately, the current state of affairs is that it’s closer to art than to science, whereas I would like to see the opposite. And I think that the same applies to process modeling. Back in 1994, I learned about a methodology that managed to transition data modeling from art to science. As a result, I have become much more effective at creating successful data models. The root cause of this methodology is that it is founded on three basic principles that…

Bin packing part 5: Set-based iteration

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One of the most common techniques authors use to keep their readers interested is to leave them with a cliff-hanger. It’s what I did when I finished part 4 of my series on the bin packing problem – never intending to leave you all hanging over a cliff for almost three years, though that is exactly what happened. My apologies to everyone who has been checking my blog on a daily basis all that time, in the idle hope of finally learning that faster method I promised. For those of you had have forgotten what I wrote in the previous…
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