Data wharehousing Essay Example | Topics and Well Written Essays - 2500 words

Data wharehousing - Essay ExampleThis is a belief of OLAP mainly used when its being related to info exploit. This is possible through pre-aggregation, an panorama of relational databases in that they not only facilitate the creation of tables, nevertheless also can manipulate them together with the data they contain. Pre-aggregation in connection with OLAP essentially explains how factual information, in this sense data that has been collected can be used to come up with probability estimations used for distribution (Kozielski & Wrembel, 2009). Data mining is a concept that has been confused with OLAP for a while. These two terms, though different have been used synonymously to refer to the other. However, these are two similar but different terms when viewed critically. Data mining is a data knowledge discovery mechanism that aims at identifying, from a pool of data, snips of useful and important data that may have been ignored, classifies the data in relation to the whole set and associates it to its class. This data analysis concept focuses mainly on dividing the existing data into minuscular manageable sets, as regards their relationship. On the other hand, OLAP focuses on addition of more data to a pool that is already in existence. This is made possible since OLAP feeds data a multi-dimensional approach, creating summaries in different dimensions that are then added up to the original data, making it more comprehensive A general exposition associated with OLAP is that which describes it as packet put in place to create a platform in which the user interacts easily with a complex database accessed online and is able to prompt the database for a utility which n return provides a report in a form understandable to him/her. However, there are many other definitions attached to this concept. Its tremendous that some people even used the full name of the acronym as a definition, but this is entirely questionable since it does not give gist of wha t the concept is or what it refers to. The most commonly used definitions are listed below (Becker, 2002). First, there is what we can call the popular definition. Its a precise and interesting definition of OLAP as set of many spreadsheets in a package. This is just but a typical meaning, mainly used to people who have little or no knowledge in information technology. Its true OLAP is used in spreadsheets to enhance the view of data, but its certainly not a set or group of spreadsheets. Secondly, OLAP has been popularly defined as a report given or presented with some extra information attached to it. This is adopted from the sort OLAP works, by presenting data from different dimensions with diverse interpretations. In this case, OLAP allows the database users to scan through different perspectives of an issue. Lastly, there is the technical definition attached to OLAP. It describes on-line analytical bear upon as enhanced, friendly browsing of similar, multidimensional data. Whe n a user prompts a query to the software, it may take up to less than a minute to give an output, hence its attribution as a fast software. It also does this in levels with a set of new data for each level. MegaSave uses data marts, tools used in presenting data and facts in multi-dimensions. The best definition, therefore, for such a scenario is the last one. This is so because so far,