Data Mining Report: Exploring Database Systems and Applications

This report provides an overview of data mining and its applications within the context of database systems. It begins by defining databases and database management systems, with examples including the National Climatic Data Center and Earthdata. The report then details the setup and functionality of Earthdata, including its use of AWS services and prerequisites for data migration. It explores potential data errors like transposition and overflow inaccuracies, and discusses how data modeling can flag these issues. Finally, the report highlights the benefits of data mining in business management, such as generating quality leads, organizing data, increasing revenue, cutting costs, and aiding in business decision planning, while also preventing system abuse.

Running head: DATA MINING 1
DATA MINING
Name:
Institutional Affiliation:

DATA MINING 2
A database is an electronic framework that permits information to be effectively updated,
controlled and accessed (Groomer & Murthy 2018). A database is generally utilized by
organizations as a technique of retrieving, managing and storing data. A database
management system is used in the present day to control databases.
National Climatic Data Centre and Earthdata are examples of some of the public databases
that are currently available. The National Climatic Data Center is an important database for
observing environmental and climate information for the United States. It is a development of
the National Atmospheric and oceanic Administration, a division of the United States
Department of business (Yang, Liao & Lou 2016). The National Climatic Data Centre keeps
a large sequence of information, from the recent weather patterns and trends to very old
atmosphere information.
Earthdata is a network application created by NASA EOSDIS to facilitate information
revelation, visualization, comparison, search, discovery, and access across EOSDIS' Earth
Science information possessions (Guo 2017). It fabricates upon several public-facing
provisions offered by EOSDIS, incorporating the EOSDIS User Registration System (URS)
verification, the Common Metadata Repository (CMR) for information access and discovery
the Global Imagery Browse Services (GIBS) for imaging, and various OPeNDAP provisions
facilitated by information suppliers.
The logic and scenario of Earthdata
 It uses Webpack 4.24.o and Node v10.15.1 in the generation of static assets.
 SQS, S3, and Lambda are some of the AWS services used by the serverless
application.

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 The prerequisites include the following: Node, PostgreSQL and NPN
Primary setup
Installation of the package- once npn is locally installed, the dependencies are downloaded
by performing the command underneath the directory of the project root.
Arrangement- Earthdata utilizes a json configuration for the storage of safe files for local
development.
Migration of data- Since resources are not accessed by the public in non-developed
environments, the database migrations run within Lambda
Application building- built in the dist/static directory.
Running of the application- execute the command underneath the directory of the project
root.
When data input doesn’t meet logical prerequisites, the following are likely to occur:
 Transposition inaccuracy
 Overflow errors
 Misreading inaccuracy

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Data mining is the procedure of analyzing vast information to discover concealed
correlations and new unknown patterns (Roiger 2017). The following are ways through
which data modeling can help in flagging data that doesn’t meet human logic guidelines in
business management:
 Data mining of enterprises helps in the generation of quality leads through enabling us
to predict future or undefined values.
 Data modeling also makes one's data more organized by making use of user-friendly
models and patterns. Information mining devices will assist you with producing more income
by informational assets creation, utilized both by marketing and sales divisions. They can
study the conduct of your customers, position and make solid marketing strategies.
 Furthermore, it helps in cutting costs, business decision planning and getting detailed
business intel.
 They can likewise assist you with identifying peculiarities inside your models and
mechanisms of preventing your system from being abused by third people. With each one of
those highlights onboard, you don't need to execute actualize complex calculations from the
ground up.

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REFERENCES
Groomer, S. M., & Murthy, U. S. (2018). Continuous auditing of database applications: An
embedded audit module approach. Continuous Auditing, 105-124.
Guo, H. (2017). Big Earth data: A new frontier in Earth and information sciences. Big Earth
Data, 1(1-2), 4-20.
Roiger, R. J. (2017). Data mining: a tutorial-based primer. Chapman and Hall/CRC.
Yang, Y., Liao, H., & Lou, S. (2016). Increase in winter haze over eastern China in recent
decades: Roles of variations in meteorological parameters and anthropogenic emissions.
Journal of Geophysical Research: Atmospheres, 121(21), 13-050.