20 More Reasons You Need Topic Maps

Well, Ed Lindsey did call his column 20 Commom Data Errors and Variation but when you see the PNG of the 20 errors, here, you will agree my title works better (for topic maps anyway).

Not only that, but Ed’s opening paragraphs work for identifying a subject by more than one attribute (although this is “subject” in the police sense of the word):

A good friend of mine’s husband is a sergeant on the Chicago police force. Recenlty a crime was committed and a witness insisted that the perpetrator was a woman with blond hair about five nine weighing 160 pounds. She was wearing a gray pinstriped business suit with an Armani scarf and carrying a Gucci handbag.

So what does this sergeant have to do? Start looking at the women of Chicago. He only needs the women. Actually, he would start with women with blond hair (but judging from my daughter’s constant change of hair color he might skip that attribute). So he might start with women in a certain height range and in a certain weight group. He would bring those women in to the station for questioning.

As it turns out, when they finally arrested the woman at her son’s soccer game, she had brown hair, was 5’5″ tall and weighed 120 pounds. She was wearing an Oklahoma University sweatshirt, jeans and sneakers. When the original witness saw her she said yes that’s the same woman. Iit turns out she was wearing four inch heels and the pantsuit made her look bigger.

So what can we learn from this episode that has to do with matching? Well the first thing we need to understand is that each of the attributes of the witness can be used in matching the suspect and then immediately we must also recognize that not all the attributes that the witness gave the sergeant were extremely accurate. So later on when we start talking about matching, will use the term fuzzy matching. This means that when you look at an address, there could be a number of different types of errors in the address from one system that are not identical to an address in another system. Figure 1 shows a number of the common errors that can happen.

So, there you have it: 20 more reasons to use topic maps, a lesson on identifying a subject and proof that yes, a pinstripped pantsuit can make you look bigger.

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