Essay Sample on Role of SQL in Data Mining and Statistical Analysis

Introduction

SQL is the abbreviation for Structured Query Language which is a programming language that is meant for database operations such as query, insert info, delete info, or update and modifies statistical data in one way or another. The role of SQL in data mining is most instrumental in supporting database-related processes. The first SQL program was developed in the 1970s by IBM and commercially released under Oracle (Rasmussen & Yager, 1997). However, with the continued development of data management programs, SQL is now mostly released voluntarily with specifications modified towards the needs of the vendor and are even government monitored and regulated by the American National Standards Institute. SQL and data mining can be related through the function of data extraction from large data sets whereby SQL forms the tool for categorizing reorganizing and rearranging such data to derive important information.

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Data mining processes are usually comprised of search-based activities that are often guided by some data partition and optimization process. SQL data analysis is central to the main data mining function which involves algorithmic instructions that are usually focused on certain approaches for manipulating tables within databases, analyzing the patterns present in large data sets, and extracting information (Aggarwal et al., 2012). Information extracted through the SQL instructions derives such patterns with information that enables predictive analysis of statistical data. As such, SQL is instrumental in defining the nature of statistical information and can be used to derive information from large data sets that would be otherwise tedious to analyze. Data mining based on SQL involves the development of a data mining structure, usually based on an SQL command that is first tested on a provisional data set for viability then applied for the actual data set in question.

Principally, SQL is the ultimate tool for data preparation from which such data can be used for various analytic activities such as predictive analysis. For instance, in the binary partition of data, SQL can be instrumental in a variety of contexts. One of such contexts includes a binary partition in the use of continuous attributes such as 'BETWEEN,' 'WHERE' and 'SELECT COUNT FROM...' for large data sets stored in relational databases (Son, 1999). Relational databases are information stores that include tables, which can be identified by their attributes. As such, SQL instructions/commands work to derive information by relating the data sets finding consistencies and gaps in the data and information. As such, SQL is a tool that is best for optimizing data and refining information for particular manipulative action (Aggarwal et al., 2012). Such data is usually in forms that are manageable for statistical analyses and can provide insights into aspects of data mining such as consumer information, profitability trends, or sale projections.

Conclusion

Conclusively, SQL and data mining can be related through the function of data extraction from large data sets whereby SQL forms the tool for categorizing reorganizing, and rearranging such data to derive important information. SQL, which is the abbreviation for Structured Query Language is a programming language applicable for data mining purposes. The program mainly works for the preparation of data for predictive analytics. Some SQL functions allow for a full rearranging of data and implementation of the predictive analysis. As such, the program is quite instrumental in data mining which usually involves the establishment of relations between large data sets which are usually stored in relatable databases. These databases consist of tabulations of data from which information can be derived much easier through the action of installed programs such as SQL that help with the extraction of relational aspects of the data sets that avails essential information from a raw set of numbers and concepts. As such, SQL goes a long way in facilitating data mining predictive functions about consumer information, market trends, or sales projections.

References

Son, N. H. (1999, July). Efficient SQL-querying method for data mining in large databases. In Proceedings of the 16th international joint conference on Artificial intelligence (pp. 806-811).Aggarwal, N., Kumar, A., Khatter, H., & Aggarwal, V. (2012). Analysis the effect of data mining techniques on database. Advances in Engineering Software, 47(1), 164-169.

Rasmussen, D., & Yager, R. R. (1997). Summary SQL-a fuzzy tool for data mining. Intelligent Data Analysis, 1(1), 49-58.