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Intelligent Databases:The facilitators of advanced Personalization
How would you expect the storekeeper at your nearest store, where you are a frequent visitor, to offer you personalized services?

Well, he would know your favorite items and preferred brands, won’t he? So, he organizes them to be in your easy reach. Perhaps he may himself suggest a new product, which was introduced recently, that falls within your interest area. Now this is not very imaginative isn’t it? It required quite a few visits for you to get this personalized service. What about someone just like you who is coming to this store for the first time?

Websites that use cookies to offer personalized services to their visitors do the same thing. Cookies only help you identify a particular user and then you try to personalize your site for that visitor. Cookies do not give you any information about the behavior for e.g. what pages did the visitor visit before clicking this page and what pages did he visit after this page; what did he shop for etc.

To make it more clear, now consider your storekeeper friend. Based on his data about you, he can go a step further and watch your buying pattern. He also tries to understand all the youth in a similar age group to you and then tries to say that all youth in the age group 21- 29 who come to my store prefer these type of products. Their buying peaks in summer and therefore it makes sense to run promotions targeting youth in summer.

One can broadly state that this is an example of collating all information about youth who visit a store and analyzing their behavior. This is exactly what intelligent databases permit you to do. Hit counters and cookies give you data, which does not translate to information. If you want information on which you like to take some action then you will have to consider data warehousing, web housing and data mining. Let us now try to understand what these are.

Data Warehousing, Web Housing, Data Mining
Web housing is essential for dotcoms to analyze the click stream data and understand customer behavior. WEB housing is collecting data from log files. A very important thing to note here is that Web housing does not spy. It just takes the data in your Web Site Log files.

Now, 90% of web sites do not do anything with log files. Here we are saying that if you want your site to do better, you need to understand how users behave on your site and we are only analyzing the data in the log files. Log files has simple text info of all the incidents happening on your site, Example will be to get to a web site and take a log file. Like for example if you use IIS server on Windows; under system32 directory you will see a log file directory. If you open a log file it will typically give you the timestamp of a hit; the page accessed; the IP address of the request etc.

WEB housing is an emerging concept in e-commerce; currently not many companies are in it; some companies are now implementing WEB housing technology in their Commerce products.

What I can share with you is that Microsoft is planning in its next version of Commerce Server; web housing capabilities; by which the log files of the commerce site will be directly sent for datawarehousing and you can analyze the data and determine the date and sell out information. Amazon to is still to use WEB housing. But Barnes and nobles.com is going to be up with WEB housing by this December. Being a major competitor I expect Amazon to also get into it in a major way. Web housing makes sense only after some decent traffic in your site.

Data mining is mining through your data using sophisticated algorithms to find some pattern or seasonal predictability. Data warehousing is the analysis of your data in your RDBMS or DBMS. This is extremely useful for ad-hoc reporting, getting reports on the fly without user / MIS help.

Data warehousing and Data mining are related topics. With Data Warehousing, you take data, extract it, cleanse it and then put the data in a data mart / data warehouse so that it can now be analyzed. Data mining is to do with the ready data - analysis of this data, determining the patterns / seasonality / trends hidden in this data.

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