An Object-Oriented Database Design for Effective Classification
Keywords:
Classification, Data mining, Dynamic Polymorphism, Inheritance, Object-Oriented DatabasesAbstract
Data mining is a science that has been a rapidly emerging art, harnessed to uncover and exploit novel, valuable, and useful relationships in data. Given the increasing reputation of object-oriented databases, it's very critical to look at statistics mining techniques in advanced database programs. It is critical to ensure that object-oriented programming concepts are high-quality addressed through incorporating them into current databases. Object-oriented programming is used considerably to deal with user-defined data types and complicated data. Decision Theory, Machine Learning, and Classification are well-established statistics mining tasks for large-scale studies in the fields of information and literature. Classification is a well-established fact-mining assignment that has been extensively studied within the fields of data, decision concepts, and machine learning literature. This paper demonstrates the layout of object-oriented databases by incorporating object-oriented programming ideas into current relational databases. Simultaneously, an efficient approach to classification is also presented for the data mining task. Object-oriented programming concepts such as polymorphism and inheritance have been employed in the classification and design of databases. Well-equipped methodologies in self-efficient classification have been achieved with the aid of the design of object-oriented databases. The main advantage of this paper is that it employs simple Structured Query Language (SQL) queries. So that effective data classification does not have to resort to complex techniques. This method has been found to have lower implementation overhead compared to traditional databases by significantly reducing the amount of memory space used for storage.
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