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lly a read-only dedicated database system created by integrating data from multiple databases and other information sources. A data warehouse is separate from the organization's transactional databases.
　　Data Warehouses and Online Analytic Processing (OLAP) technologies are in full swing in industry as major business intelligence technologies. Many business intelligence applications currently run at companies not only demand more capacity, but also new methods, models, techniques or architectures to satisfy these new needs.
　　Some of the hot topics in Data Warehouses and OLAP include: a methodology covering design steps during a DW lifecycle, active Data Warehouses, distributed Data Warehouses, advanced OLAP for business intelligence, web warehouses, new visualization methods, Data Warehouses for new applications (such as XML documents), stream data, spatial or GIS data, or biomedical data. Moreover, there are other aspects developed in other software areas such as security or quality, which still remain uncovered by current design methods or technologies for Data Warehouses.
　　The site selection method symbolizes a single business decision with numerous business functions carry out by a typical construction firm. Instead of developing a full scale data warehouse, a data mart is developed for the purpose of this research. Existing data models used to design traditional OLTP systems are not appropriate to design a data mart. The reason is that these data models do not support analytical processing and also not efficient to answer complex queries requiring aggregation of data. Special database schemas are available for modeling data in a data mart.
　　There are a large number of obvious advantages involved with using a data warehouse. As the name suggests, a data warehouse is a computerized warehouse in which information is stored. The organization that owns this information can analyze it in order to find historical patterns or connections that can allow them to make important business decisions. In this article I will go over some of the advantages and disadvantages that are connected to data warehouses.
　　One of the best advantages to using a data warehouse is that users will be able to access a large amount of information. This information can be used to solve a large number of problems, and it can also be used to increase the profits of a company. Not only are users able to have access to a large amount of information, but this data is also consistent. It is relevant and organized in an efficient manner. While it will assist a company in increasing its profits, the cost of computing will greatly be reduced. One powerful feature of data warehouses is that data from different locations can be combined in one location.
　　There are a number of reasons why this is important. When data is taken from multiple sources and placed in a centralized location, an organization can analyze it in a way that may allow them to come up with different solutions than they would if they looked at the data separately. Data mining is connected to data warehouses, and neural networks or computer algorithms are responsible. When data is analyzed from multiple sources, patterns and connections can be discovered which would not be found otherwise. Another advantage of data warehouses is that they can create a structure which will allow changes within the stored data to be transferred back to operational systems.
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