Pass your certification exam. Faster. Guaranteed.

Data Mining Transcription

Welcome to out data warehousing and data mining module. Data warehousing is combining databases into a single large database in order to analyze the data. The merged data can be used for information retrieval reporting and statistical analysis. Data mining is analyzing large amounts of data to locate data that you previously did not know, data that was previously hidden, or useful information.

Data mining tools produce metadata, and this sorted, aggregated data is often able to show you previously unseen relationships and patterns. You should be familiar with the difference between data warehousing and data mining for the CISSP examination. Remembering that both allow you to combine large amounts of information. But data mining allows you to find information that was previously hidden or unknown.

Data warehousing is the combination of data from several different data bases In order to better retrieve and analyze the information. You're extracting operational data and making it into informational data. A screen scraper is a tool that will pull data from multiple websites and add it to your database.

You should remember this term screen scraper for the CISSP examination. With data warehousing, you are not just mirroring the data, you are normalizing it by reducing redundancies, and inconsistencies. With a data mart, you're gathering and sorting data, in order to meet some defined criteria. This data can be used to meet your immediate business needs or this data can be sold to other individuals who are interested in it.

There are tools that can be used to analyze a database or warehouse, to look for anomalies or trends, and also to identify patterns and relationships between different sets of data. This creates metadata which is data about data, which can help the data become more useful to the user.

It can also be used to test for inference vulnerabilities, where an individual is able to deduce information about an object by collecting information from several different sources. We have an example at the right of your screen where database information from database A, B, C and D were all placed into the data warehouse.

The medi data, or data about the data within the warehouse was extracted and can be presented to the users in a more usable format than if the user just looked at the data warehouse itself. This concludes our data warehousing and data mining module. Thank you for watching.