The Use of Temporal Database in the Data Management System - Article Example

The Use of Temporal Database in the Data Management System Organizations tend to store large amounts of information in their database, which may have highly confidential information. Generally, the system to store this large amount of data into the system involves the use of different models. One such model includes that of a temporal database, along with relational database, object-oriented database and a spatial database (Patel, 2003). This particular paper addresses the use of temporal database in the Data Management System (DBMS). A temporal database is a means of holding temporal data of a temporal version of a Structured Query Language. It contains built in time aspects. To be more clear, the time aspect of the temporal data works on the transaction time and the valid time together forming bi-temporal data. Valid time ensures that the fact is correct with respect to what is happening in the world. Transition time on the other hand is the time taken while the fact is being stored in the database. However, the main essence of the temporal database remains to address the information, which particularly relates to the time aspect that may include past, present and the future. One example of the kind of data included in temporal database includes the history of the stock market (the different prices of different stocks during a certain period), and the movement of employees within an organization. In this way, temporal database allows the heads of the organization to understand the different trends (Patel, 2003). There are different forms of temporal databases. The two bi-temporal databases, transition time and valid time help to distinguish between the different kinds of temporal database. A historical database is one, which stores data keeping valid time in mind. On the contrary, a rollback database stores data looking into the transaction time. Database management systems known as DBMS are known for only storing a single state of the real world and this is the most recent state. Therefore, due to their short time, it is known as a snapshot database. Another question that is worth considering is the reasons for the requirement of a temporal database. The reasons are quite simple. . The relational data model has become popular in the last 20 years due to its simplicity and the tried and test mathematical foundation it possesses. Generally, the treatment of the changes in data is quite similar to the way ordinary data is treated. Most applications do require a temporal database. The main objective of this is to help identify a proper data type for time and to stop object description from being fragmented. It should be able to function with an old database that does not have a temporal data. Additionally, a temporal database helps to handle temporal data. Changes in data, pertaining to time are recorded, which makes the application more convenient and user friendly. It can handle static data and the traditional database algebra is functional in a temporal database. Additionally, it can control time, the new query algebra works like the traditional database algebra. It remains of extreme significance to address the issue of the storing of data in a temporal database. Storage of information occurs in a perfectly defined format. Generally, this is done with the help of tables. As long as the virtual data stores are given a time slot, they can easily be used in the temporal database. Entities need to be time-stamped with different times (Leondes, 2002). Moreover, automatic query writing provided by the different applications that offer temporal database allows for easy storage. Data generally tends to be stored in large data storage warehouses, just in the same way that a conventional file cabinet tends to store data. However, data is stored in a ‘virtual’ world, with temporal database. After the information has been stored, it remains essential to take out the output from the particular type of data. After all, temporal database tend to be used in large business organizations, which require information to get an edge over competitors. Generally, information is required in milliseconds, which cannot be obtained from a relational database, because it is quite inefficient. The different applications use various ways of getting the output. However, most simply, different applications create various tables for different queries. Therefore, the output is derived from a virtual data store, with the help of tables. The system has a set of pre-determined algorithms for processing data and then eventually producing a desired output. However, the database will tend to store large amounts of data, in the form of transactions. Many of these transactions will not even be required. It is of utmost importance that only meaningful data is retrieved from the large warehouse. Moreover, the data stored has occurred at different, varying lengths of time. The process that deals with this is known as data mining. Temporal Mining tends to attach significance to the time, at which transactions tend to take place. This type of mining clusters data, according to the time and then develops certain association rules. The temporal association rules tend to be stored in binary digits and in a series of formulas, which may take into account the Transaction ID, the Customer ID, the items brought and the valid time range for the transaction, if it is pertaining to a supermarket. Generally, for temporal mining, each transaction is scanned once for all types of mining methods (Balasubramanian, Duraiswamy, 2009). One of the methods of data mining is with Easy Miner, software developed by UMIST that uses a wide spectrum of data mining functions, to enable greater functionality. These different functions enhance the user-friendliness of this application (Mendeley, n.d) There are different database theories, which cover a range of topics related to the theoretical approaches of database management system. There are two famous people, who have contributed to the particular study of temporal database. This includes Inmon and Kimball. They both have put forward the ideas of building the data warehouse. However, there have differences in their approaches. Kimball used data warehousing as a constituency of data marts. Kimball wanted to use temporal database system for the working of a huge organization, separated into departments. The data warehouse caters to the dimension of the data marts. Uniformity is achieved throughout the organization, based on a departmental level, according to this approach. However, Inmon’s idea to create a data warehouse had the idea that the areas would be catered on a subject to subject basis. Online stores can be handy way to employ the use of temporal database. In a way, Inmon presented the idea that the data warehouse is made up from different data marts. Inmon believed that the different areas of various subjects can be added to this if the need occurs. A Point-of-Sales data can always be included if the management feels it will add to the efficiency of the entire system. These remains in stark contrast to the idea of Kimball, who stated the different data marts, take the data from the data warehouse. However, both ideas are right in their own way. However, one would of course wonder, whether the warehouse would be formed after the data is received collectively, or it is stored simultaneously as soon as it would be stored (Keydata, n.d) Therefore, the temporal database system is a highly efficient way of handling different types of information, gathered at different time intervals. A supermarket can use the temporal database management system to look at the time, which has the highest sales. It could then assess that a particular time records the highest number of sales. This will help in its organizational policies, for example, having more helpers around at that time, and using two checkouts instead of two. The information gathered is quick and can be achieved within milliseconds. References Balasubramanian, C., & Duraiswamy, D. (n.d.). A Temporal Mining Method for an Interval Encoded Temporal Database with Prioritized Items. IJRTE Academy Publisher. Retrieved January 12, 2012, from ijrte.academypublisher.com/vol02/no0 Bill Inmon vs. Ralph Kimball. (n.d.). 1Keydata - Home of Free Online Tutorials. Retrieved January 12, 2012, from http://www.1keydata.com/datawarehousing/inmon-kimball.html Data mining in temporal databases | Mendeley. (n.d.). Free reference manager and PDF organizer | Mendeley. Retrieved January 12, 2012, from http://www.mendeley.com/research/data-mining-temporal-databases/ Leondes, C. T. (n.d.). Database and data communication network systems: techniques and applications - Cornelius T. Leondes - Google Books. Google Books. Retrieved January 12, 2012, from http://books.google.com.pk/books?id=oP8SwbO0lT8C&pg=PA842&lpg=PA842&dq=How+are+virtual+data+stores+used+in+a+temporal+database&source=bl&ots=3TcVPW-8d5&sig=in_7Drh4mIn8q86HEu_l_JVPZM Patel, J. (2003, June 18). Temporal Database System. Google Docs. Retrieved January 12, 2012, from https://docs.google.com/viewer?a=v&q=cache:i4xQSmrPKWYJ:www.doc.ic.ac.uk/~pjm/teaching/student_projects/jaymin_patel.pdf+Temporal+database&hl=en&gl=pk&pid=bl&srcid=ADGEESikqVI1dc2Qa5HyBHLMhldy80jjatDLc3oOAKOc_Cmw9eXXeYTBkxOufUM4lL9Z-3IMRtxeHL9l_3dOHHChF-iG4oiPKghDlwvsWsIXECSjuw4xU2kQMG8gX Read More