Technological Databases
Technological Databases
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Introduction
The relational database structure came into existence in the 1970s (Griesemer & Kadaru, 2009). They were designed to counter the rising complications relating to flat files and the escalating demand for their application when dealing with the complexity of online data.
Unlike the relational database, data warehouse is a type of database designed to store information. Data warehouses have a centralized system of data repository center where data from different sources id analyzed. After the collection of data is done from different sources it is critically analyzed and a report drafted (Date, 2008). One of the key differences between a data warehouse and a relational database is that the former is a center where data from different sources is collected, stored and with time the data will be used by the business cooperation to draft and report (Wrembel & Koncilia, 2007). On the other hand the relational database is a center developed by the company to store the large quantity of data the firm has collected over a stipulated period of time.
In as much as the data warehouse is used to deal with the storing and drafting of reports, the system is not designed to serve a large number of users. The relational database on the other hand is optimized for online transactions is a database system that is aimed at not only storing large quantities of online data, but also allows the users access the information stored in the system at any time regardless of the location because the data is stored on the internet network.
Operational database is a record made up of system precise reference information as well as event data run by a specialized system called a transaction-update system. The system entails system controlled data i.e. flags, indicators as well as counters (Griesemer & Kadaru, 2009). The decision support system is a system that dictates the manner in which businesses run their decision making activities. The operational database is the opposite of the decision support system because the former is not structured in a manner that the data it compiles can be used in decision making (Wrembel & Koncilia, 2007). Unlike operational databases, decision support systems have non-modifiable data that is essential for the purposes of statistical scrutiny. Secondly, operational databases are utilized in supporting IRS task fillings an element that the decision support system cannot do.
Operational data deals with transactions done daily. Granularity separates decision support from operational data in a number of ways (Date, 2008). Operational data deals with transactions that have a short time frame while the decision support data deals with transactions conducted over a long period of time. The integration of both short and long term transactions enables financial managers to observer their commercial development over short and long term basis.
DSS could be essential in decision making and proper running of organizations. In the business and management world, the executive dashboard software is essential in monitoring the progress of the organization and providing a baseline for decision making (Alapati, 2009). The software that is specifically designed to monitor negative trends helps CEOs identify the best way of proportional allocation of resources and after an analysis it presents data in form of charts, pie charts and graphs in the precise and simplest form possible.
In commercial agricultural production and analyzing the market for sustainable development, programs such as DSSAT4 have been developed with funding from USAID to ensure the users to rapidly asses a number of crop production systems that are sustainable (Griesemer & Kadaru, 2009). The software monitors all the available options and provides the user with a set of options that have been selectively sampled. He/ she chooses the best that fits his/ her needs and requirements.
Canada is a country that much of its land is covered by forests. In the past management of the forest was challenging and costly. The application of DSS has enhanced proper forest management and modelling the forest cover Canada would likely have in the future. DSS synchronizes every aspect of forest management and outlines the tree harvesting, afforestation and reforestation schedules (Wrembel & Koncilia, 2007). Areas vulnerable to logging are also identified.
Data mining is the process of extracting valid information from different point of view. The data is evaluated and summarized to provide meaningful information. Data mining softwares are designed to pinpoint unknown co relations among data with numerous databases (Alapati, 2009). In the commercial world, data mining and data warehouses work together to enhance proper data storage and analysis. Coordination of the two elements can allow firms foresee their company sales and determine the future dynamics and trends. The forecast is done because the two systems draw a relationship among huge quantities of information in the large interactive catalogs.
The use of data mining and warehousing has been critical in decision making incidences. Warehousing enables people in the business field to enhance their analysis and decision making through sampling of the operational layer from the decision layer.
Conclusion
The two virtues can also help commercial firms to model patterns, report data and information that is encrypted with the application of the OLTP database system (Alapati, 2009). The encryption of the data in data warehousing can be interlinked to data mining where an organization, for instance a bank, can monitor the patterns and trends of fake credit card use and therefore determine the true and loyal customers that the financial institution can incorporate in its long term plans and eliminate the fraudsters.
References
Alapati, S. R. (2009). Expert Oracle database 11g administration. Berkeley, Calif.: Apress.
Date, C. J. (2008). The relational database dictionary. Berkeley, CA: Apress ;.
Griesemer, B., & Kadaru, A. (2009). Oracle Warehouse Builder 11g getting started : extract, transform, and load data to build a dynamic, operational data warehouse. Birmingham, UK: Packt Pub..
Wrembel, R., & Koncilia, C. (2007). Data warehouses and OLAP concepts, architectures, and solutions. Hershey: IRM Press.
