This paper deals with the examination of the databases, data mining and data warehouse systems. Databases are present in the computer systems to store information about sales and transactions. Data warehouses can be considered as a single structure for the storage of information other than the operational databases. Data mining is a field of computer science in which information is analyzed to derive useful decisions for a company. There are various tools which help to examine the information. It is very helpful since it is possible to investigate old data of data warehouse for effective decision making procedure in companies.
Databases, Data mining, Data warehouse
Data warehouse is used in the information technology companies to include company information. It comprises of a distinct storage for the group of data that has been purposely built from information which is kept in exclusive internal and external functional databases. The mining of information is the way which helps to divide the informational designs(Moscoso-Zea&Luján-Mora, 2016). Data mining is used in investigation of marketplaces, corporate inquiry,hazard management, and identification of fraud. A data distribution center is the database structure which is planned for methodicalinspection instead of valuebased effort(Coronel & Morris, 2016). A data warehouse is a framework that keeps stocks of information from acompany’soperational database. A database is utilized to collect information that is structured so that it can be effortlessly, handled, accessed and updated (PhridviRaja&GuruRao, 2016). A computer database comprises of collections of data archives, folders and files which consist of information regarding sales transactions or communications with particular consumers.
This topic is of great important for Information Technology systems. The inspiration behind using data warehouse is to carry out the basic process of taking decisions in a company. It makes data accessible easily as numerous reports can be produced from the data warehouse. It usuallycomprisesof logged information which is gained from the valuebased factsthough can similarlyinclude information from dissimilar sources. Data warehouse is continually kept in isolation from value based facts (PhridviRaja&GuruRao, 2016). Data warehouse are not identical to the operational databases since they keep logged information, which makes it simpler for business leaders to break down information through a particular timeline.
Data warehouse sorts the data which is dependent on varioustopics, for example clients, products, or commercial activities. Databases are of great importance since it helps to manage data effectively and permits the users to do numerous tasks with ease (Mahi&Kaur, 2017). Databases are used to store, arrange and handle a large amount of data in single software. On the other hand, a data mining system is useful to forecast the future. It helps in increasing the revenue of the company by assisting in the decision making process. It also helps in fraud detection.
As per PhridviRaja&GuruRao (2016) data mining is one of the portion which is attaining lot of practical importance and is developing at a fast pace with novel approaches, practices and findings in numerous applications which are linked to computer science, medicine, text, stock market prediction, bioinformatics, weather forecast, video and audio processing. Data is the main issue in data mining. With the enormous data on the web produced from numerous devices, Twitter, Internet Relay Chats, Online Bank, Face book and ATM Transactions, the idea of dynamically altering data is becoming an important challenge, which is known as data streams. In this paper, the authors provide the procedure for finding recurrent patterns from data streams with a case study and recognizing the research problems in managing data streams (PhridviRaja&GuruRao, 2016).
As per the analysis of databases, data warehouses and data mining systems it can be found out that all the three systems deals with data and its examination(An, 2018). Databases and data warehouses work for the effective storage and handling of data whereas data mining work for analyzing data and deducing useful information from it. This information can be used in efficient decision making for the organizations.
A data warehouse is database system that is intended for logical examination rather than transactional effort. Data mining is the technique of examining data patterns. Data warehousing is the procedure of combining all appropriate information together (Coronel & Morris, 2016). Data mining is regarded as a technique of mining data from huge data sets.
There are numerous benefits of the data warehouse, data mining and database applications, but there are some limitations in the also which includes the flexibility of data, extra reporting task, possession of data etc. (An, 2018). If there is a huge project, data warehouse needs a enormous budget and time to apply on it. This is a kind of additional expenditure and damage for the business. Databases are expensive and there are changes of database failure.
It can be determined from this paper that the databases, data mining and data warehouse applications are of great importanceas they support in storing the facts and figures properly. Database management systems are significant to companies since they offer a highly well-organizedtechnique for handling numerouskinds of information.Earlier, there were a numerousdifficulties in storing data and hence there were many concernsrelated with it. These problems comprised of data mis-management, time consuming task, information loss, etc. There were numerous conventional techniques which aided a lot in the information storage but well-organized storage was achieved in data warehouse as it can manage bulk data. The necessityfor storing such data arises from the decision making strategies since these strategies utilises these data to examine them and then deducevaluablefacts from it.
Generally, there are numerous benefits of data mining and a data warehouse application then its restrictions. In future work, researchers should focus on reducing the database failures and reduce noisy and incomplete data. It will increase data effectiveness. Also, it should work on the reduction of cost to retrieve data from the data warehouse.
PhridviRaja, M. S. B., &GuruRao, C. V. (2016). Data mining: past present and future-a typical survey on data streams. arXiv preprint arXiv:1605.01429.
Moscoso-Zea, O., &Luján-Mora, S. (2016, September).Datawarehouse design for educational data mining. In 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET) (pp. 1-6). IEEE.
Mahi, S., &Kaur, G. (2017).Introduction to Data Mining and Data warehouse. International Journal of Advanced Research in Computer Science, 8(4).
Coronel, C., & Morris, S. (2016). Database systems: design, implementation, & management. Cengage Learning,1(2).
An, A. I. (2018, November). in Relational Databases. In Advances in Artificial Intelligence-IBERAMIA 2018: 16th Ibero-American Conference on AI, Trujillo, Peru, November 13-16, 2018, Proceedings (Vol. 11238, p. 16). Springer.
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