Data Optimization is a process that prepares the logical schema from the data view schema. It is the counterpart of data de-optimization. Data optimization is an important aspect in database management in particular and in data warehouse management in general. Data optimizations is most commonly known to be a non-specific technique used by several applications in fetching data from a data sources so that the data could used in data view tools and applications such as those used in statistical reporting.
A logical schema is also a non-physical dependent method of defining a data model of a specific domain in terms of a particular data management technology without being specific to a particular database management vendor. In more simple terms, the logical schema refers to the semantics describing a particular data manipulation technology and these descriptions could be in terms of tables, columns, XML tags and object oriented classes.
Data views are tools for creating effective reports based on accurate queries. To have a data view, the database management system need to retrieve the desired data and display the expected output. Since the database, especially those databases dealing with high volumes such as those used in data warehouses, need to retrieve large bulks of data, getting a data view may be a slow and complex process. Employing data optimization can reduce the complexity of the process while trying to optimize the needed resources by reducing physical processing needs.
In some database applications, the database management system itself is loaded with features to make querying data views easy by directly executing the query and immediately generating views. Some database applications have its own flexible language for mediating between peer schemas extending from known integration formalisms to more complex architecture.
Data optimization can be achieved by data mapping, an essential aspect in data integration. This process of data optimization includes data transformation or data mediation between a data source and its destination, and in this case, the data sources could refer to the logical schema and the destination the data view schema. Data mapping as a means of data optimization could translate data between various kinds of data types and presentation formats into a unified format used in different reporting tools.
Some software applications offer a graphical user interface (GUI) based tool used in designing and generating XML based queries and for data views. Since data can come from a variety of sources of from a heterogeneous data source, running queries with this tool can be an effective means of generating a data view. Using graphical data view can free a data consumer from having to focus on the intricate nature of query languages as they tool can provide pictorial and drag and drop mapping approach.
Being free from all the intricacies associated with query languages means that one can focus more on the information design and conceptual synthesis information which could come from many different disparate sources. Since high level tools need to shield end users from the back end intricacies, it needs to manage the data from the back end efficiently.
Having a graphical tool may have its benefits but its downside is that the graphics could add load to the computer memory. So, graphical tools need so much data optimization in order to balance the load toll from the graphical components.
There are several modules available designed for data optimization. These modules can be easily "plugged" to existing software and the integration may be seamless. Having these pluggable data optimization modules can definitely make database related applications focus more on the development of graphical reporting tool for non technical data consumers.