Improving Admission Control Policies in Database Management Systems, Using Data Mining Techniques

Author(s):  
Ali Farahmand Nejad ◽  
Sadegh Kharazmi ◽  
Shahabedin Bayati
Author(s):  
Dionysios Politis

In this chapter data-mining techniques are presented that can be used to create data-profiles of individuals from anonymous data that can be found freely and abundantly in open environments, such as the Internet. Although such information takes in most cases the form of an approximation and not of a factual and solid representation of concrete personal data, nevertheless it takes advantage of the vast increase in the amount of data recorded by database management systems as well as by a number of archiving applications and repositories of multimedia files.


2003 ◽  
Vol 3 (1) ◽  
Author(s):  
S. G. Maritz

Managing data as a resource is an important function of information management. Accurate and relevant data is the source of valuable information. By managing data efficiently, sound management decisions can be made. The traditional file environment is not appropriate for managing data; database management systems are the most popular choice for managing data effectively. Some of the broader trends in data management include outsourcing, data reuse, data re-engineering, archiving, data warehousing and data mining.


Author(s):  
John N. Bernal ◽  
Johanna P. Rodriguez ◽  
Jorge Portella

Databases are by far the most valuable asset of companies. Since the need was seen not only to count but also to have some type of record of elements such as crops, animals, money, properties and that this record could be consulted and modified according to the situation, that is where the first database was born. , and after that, these databases cannot be disorganized, they also need to be managed and administered under established standards that facilitate their understanding and management not only by their creators but by the other people who subsequently administer them. Databases and database management systems have an interesting evolutionary history that deserves to be analyzed and this is the objective of this document, where it is sought to understand. Along with databases and their management systems, data mining or Data mining arises that in order not to extend ourselves so much, it is the job of finding common patterns in various data sources and in what way they can be used to predict situations or results of various circumstances; We also focus on the other topic that we will present, Oracle data mining, which roughly is to merge data mining with Oracle, which makes it a powerful tool for obtaining information and predicting results based on statistics.In this article we will study and analyze the ideas, concepts and basic examples that make up SGBD and Data Mining and, we will try to go deeper into this topic, the use of decision techniques such as advanced statistical algorithms. We also present a fictitious example of the application of these techniques: predicting which products can be sold based on their relationship with others. we will give a brief explanation of association rules, data mining cycle and the types of learning and the evolution that data mining has had.


Author(s):  
Sujata Mulik

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 


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