How to Refresh Data

Refresh : Data Refreshing is the process of propagating the updates from the source to the corresponding data on the data warehouse.It is performed on a regular known schedule ,so that the data warehouse is updated periodically.It depends on business needs and must be known by clients.

The two important issues with data refreshing are.
1.When to refresh and
2.How to refresh.

Usually,a refresh operation is performed daily or weekly .The refresh interval is defined by the warehouse administrator,depending on user needs. Refresh techniques may depend on characteristics and capabilities of the data base servers.The time interval which is set for data refreshing should be done carefully,because a very short interval will typically result in inefficient use of network bandwith and processor resources,On the other hand,a long interval may result in outdated data.Sometimes,the interval between refreshing is too small that it increases the load on servers.

Data refreshing is done in order to synchronize data at source and data warehouse.So that the final data is correct and consistent.

Construction and mining of Object Cubes

In an object-oriented databases,classes of objects rather than individual objects are generalized and multidimensional analysis are applied on them.Generalization of large set of objects becomes difficult because these objects share attributes and methods of other classes.

In order to perform a class-based generalization on a large set of objects,the extended version of the attribute -oriented induction method is used.

In case of large data sets,the generalization can be seen as an application of class-based data mining process performed on different attributes in sequence.This generalization can be obtained efficiently by analyzing each attribute,generalizing them into a simple-valued data and then constructing a multidimensional data cube called an object cube.Data Mining and multidimensional analysis can be performed on these object cubes in a similar way as that of relational data cubes.However,it is very difficult to generalize the data set into a single-valued data.For example,consider an attribute keyboard which consists of a set of keywords.These keywords are used to define the features of the book.Because of which,it is not sensible to generate these set of key words into single-valued keyword.

Fundamental of Data Mining and significance in database technology

Data Mining : Data mining is a process of extracting knowledge from massive volume of data.It refers to a way of finding significant and useful information from an organization's data base.The knowledge which is extracted can include pattern types , association rules and different trends.Data mining is not confined to a particular organization.instead it has techniques explore the knowledge hidden in any data.The different techniques used for digging out data are artificial intelligence, statistical and mathematical techniques and pattern recognition techniques.

Organizations that makes use of data mining techniques are benefited in their corresponding business area by identifying the significant trends and anomalies that were not possible to be detected by a human analyst.The association shows important knowledge about the database and entities present in the database.The purpose of data mining is to discover relation that connects different database entities.

The following are the reasons for using data mining.
1.Knowledge discovery.
2.Data visualization.
3.Data correction.

1.Knowledge Discovery : The objective of knowledge discovery process is to identify the invisible correalation ,patterns,trends available in the database.

2.Data Visualization : The objective of data visualization is to "harmonize" large volume of data so as to find a sensible way of displaying data.

3.Data Correction:This process is used to identify and correct incomplete, erroneous,inconsistent data.