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Advanced Database Management Systems Including Python Matplotlib for Data Mining
Prof.R. Panneerselvam with his rich experience of teaching and research at Anna University main campus and Pondicherry University main campus for four decades, in specific teaching this subject of Databse Management Systems, had published two well established text books, viz. Database Management Systems through PHI Learning, and Databases and Python MySQL, MongoDB, OOP and Tkinter, through Amazon, in addition to publishing 15 other well established text books in PHI Learning, Cengage and Sage book publishers. Now, as an extension, the professor feels happy in publishing this text, ‘Advanced Management Systems Including Matplotlib for Data Mining’ to serve B.E./B.Tech. (CSE), B.E./B.Tech (IT), M.E./M.Tech. (CSE, IT), M.B.A/ M.Sc. (Data Science/ Data Analytics) and MBA students. This text is useful to practitioners in IT industry as well.
The text begins with introduction to database management systems to revisit the essential topics to have continuity with Advanced Database Management Systems. This chapter includes information system, database system, terminologies of file, association between the fields of a file, associations between files, and data models. The chapter 2 presents normalization and database design, which introduces the concept of normalization first and then it is followed by a case study to design the conceptual data model of a reality.
It is followed by a chapter on distributed database management systems, which includes types of data processing applications, distinction between centralized databases and distributed databases, database partition and a case study on vertical partition. Next, chapter 4 discusses client/ server systems. This includes client/ server concept, examples of client/ server systems, three tier client/ server architecture, types of client, client/ server protocols and design of client/ server system.
It is followed by a chapter on data warehousing, which presents data warehouse architecture, data warehouse aided decision support system and data warehouse models. Chapter 6 is on data mining, which is an integral part of data warehousing. This chapter covers stages of data mining, types of services of data mining, search algorithms, viz. simulated annealing algorithm, genetic algorithm, ant colony optimization algorithm and artificial intelligence, and mathematical techniques such as multiple regression, discriminant analysis and cluster analysis.
The chapter 7 on Python matplotlib is presented as a data mining tool. This consists of environment setup for matplotlib, matplotlib simple plot, matplotlib axis.plot(), mathematical distributions and models, subplots and scatter plots, and charts in matplotlib.pyplot, machine learning, linear and multiple regressions using matplotlib.pyplot, scaling of data using central limit theorem through Pandas, and train and test. The next chapter 8 discusses object-oriented database management system (OODBMS), which includes its terminologies and application areas. At the end, a chapter on cloud databases includes definition and classification of cloud database, types of management database and cloud architecture,
This text is written in easy-to-read style. Each and every chapter contains numerous examples and in select chapters fitting case studies are presented. The chapter 7 has numerous Python programs.
I express my deep gratitude to all my academic colleagues and teachers of other universities and colleges for having suggested me to come out with this text. Further, I take this opportunity to thank Amazon, which provided a platform to publish this book.
Any constructive suggestion for further improvement of the book is most welcome.
R. Panneerselvam, M.E., Ph.D.
The text begins with introduction to database management systems to revisit the essential topics to have continuity with Advanced Database Management Systems. This chapter includes information system, database system, terminologies of file, association between the fields of a file, associations between files, and data models. The chapter 2 presents normalization and database design, which introduces the concept of normalization first and then it is followed by a case study to design the conceptual data model of a reality.
It is followed by a chapter on distributed database management systems, which includes types of data processing applications, distinction between centralized databases and distributed databases, database partition and a case study on vertical partition. Next, chapter 4 discusses client/ server systems. This includes client/ server concept, examples of client/ server systems, three tier client/ server architecture, types of client, client/ server protocols and design of client/ server system.
It is followed by a chapter on data warehousing, which presents data warehouse architecture, data warehouse aided decision support system and data warehouse models. Chapter 6 is on data mining, which is an integral part of data warehousing. This chapter covers stages of data mining, types of services of data mining, search algorithms, viz. simulated annealing algorithm, genetic algorithm, ant colony optimization algorithm and artificial intelligence, and mathematical techniques such as multiple regression, discriminant analysis and cluster analysis.
The chapter 7 on Python matplotlib is presented as a data mining tool. This consists of environment setup for matplotlib, matplotlib simple plot, matplotlib axis.plot(), mathematical distributions and models, subplots and scatter plots, and charts in matplotlib.pyplot, machine learning, linear and multiple regressions using matplotlib.pyplot, scaling of data using central limit theorem through Pandas, and train and test. The next chapter 8 discusses object-oriented database management system (OODBMS), which includes its terminologies and application areas. At the end, a chapter on cloud databases includes definition and classification of cloud database, types of management database and cloud architecture,
This text is written in easy-to-read style. Each and every chapter contains numerous examples and in select chapters fitting case studies are presented. The chapter 7 has numerous Python programs.
I express my deep gratitude to all my academic colleagues and teachers of other universities and colleges for having suggested me to come out with this text. Further, I take this opportunity to thank Amazon, which provided a platform to publish this book.
Any constructive suggestion for further improvement of the book is most welcome.
R. Panneerselvam, M.E., Ph.D.
298 pages, Kindle Edition
Published September 23, 2022
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