Database Applications: Data Concepts and Business Strategies Review

This assignment provides a comprehensive overview of database applications, detailing key concepts and technologies. It begins with an introduction to data warehouses, explaining their role as central repositories for data analysis and reporting, and their integration with operational systems. The document then explores data marts, which are subsets of data warehouses tailored for specific business needs, improving end-user response times. Data mining is presented as a process for discovering patterns in large datasets, utilizing methods from statistics, database systems, and machine learning. The assignment also covers business intelligence, outlining its strategies and technologies for data analysis, including data mining, reporting, and online analytical processing (OLAP). OLAP is described as a method for multidimensional analytical queries. Finally, the assignment introduces object-relational database management systems, highlighting their support for object-oriented database models and their ability to extend data models with custom methods and data types. References to relevant academic sources are included to support the information presented.

Running head: DATABASE APPLICATIONS DIRECTIONS
DATABASE APPLICATIONS DIRECTIONS
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1DATABASE APPLICATIONS DIRECTIONS
Data Warehouse
It is system that is used to report and analysis of data. It could be considered business
intelligence’s core component. Data warehouse is integrated data’s central repositories from
several disparate sources. Data warehouse stores historical and current data at one place which
are used to create analytical reports throughout enterprise for the workers (Visscher, et al., 2017).
Data stored within data warehouse could be uploaded form operational systems. Data might pass
by operational data store. It might also need data cleansing.
Data Mart
Data mart is access pattern or structure specific with environments of data warehouse,
which are used for retrieving data of client. It is data warehouse’s subset and usually is oriented
to specific business team or line. These are built by the organizations as information is in
unorganized form inside the database which makes this readily accessible. Response time of end
user could be improved by using data marts by allowing the users in having access to specific
data type which are required by them.
Data Mining
Data mining is process to discover pattern within huge data sets, which involved methods
at intersection of statistics, database systems and machine learning. It is interdisciplinary subfield
for statistics and computer science with overall goal to extract the information from set of data as
well as transforms the information in understandable structure (Tan, Steinbach & Kumar, 2016).

2DATABASE APPLICATIONS DIRECTIONS
It is analysis step for knowledge discovery within processes of database. Data mining’s task is
automatic analysis of large amount of data.
Business Intelligence
It comprises technologies and strategies that are used by the organizations for business
information’s data analysis. Technologies of business intelligence give current, predictive and
historical views for business operations. Business intelligence’s common functions consist of
data mining, process mining, reporting, text mining, business performance management, online
analytical processing, perspective analytics and benchmarking. Technologies of business
intelligence could handle huge quantity of unstructured and structured data for identifying,
creating and developing new strategic opportunities for business. They aim in allowing for easy
interpretation for such big data.
Online Analytical Processing
It is approach for answering queries of multi-dimensional analytical (MDA) within
computing. Online Analytical Processing (OLAP) is a part for wider category for business
intelligence that encompasses also relational databases, data mining and report writing. OLAP’s
typical applications consist of business reporting, management reporting marketing forecasting
and budgeting and financial reporting (Chou, et al., 2018). Tools of OLAP enable the users for
analyzing multidimensional data from several perspectives. Three analytical operations are there
for OLAP: dicing, slicing and drill-down.

3DATABASE APPLICATIONS DIRECTIONS
Object-relational Database Management System
It is database management system which is same with relational database. However, with
object-oriented model of database inheritance, classes and objects are supported in schemas of
database as well as in query language. This supports data model’s extension with custom
methods and data types (Medina, et al., 2017). Object-relational database could be said as for
providing middle ground among object-oriented databases and relational databases. The data is
stored within database and could be manipulated by using queries within query language.

4DATABASE APPLICATIONS DIRECTIONS
References
Chou, C. H., Hayakawa, M., Kitazawa, A., & Sheu, P. (2018). GOLAP: Graph-Based Online
Analytical Processing. International Journal of Semantic Computing, 12(04), 595-608.
Medina, J. M., Barranco, C. D., Pons, O., & Sanchez, D. (2017, July). Building and evaluation of
indexes for possibilistic queries on a fuzzy object-relational database management
system. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-
6). IEEE.
Tan, P. N., Steinbach, M., & Kumar, V. (2016). Introduction to data mining. Pearson Education
India.
Visscher, S. L., Naessens, J. M., Yawn, B. P., Reinalda, M. S., Anderson, S. S., & Borah, B. J.
(2017). Developing a standardized healthcare cost data warehouse. BMC health services
research, 17(1), 396.