Group 10

1. Review of Article
Name of Student-Amandeep Singh,
Manjinder Singh, Narotham Reddy
Student Id-45232, 44823, 45173
Name of University-VIT

2. ArticleTitle
Data Mining languages got
business Intelligence
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3. Introduction
 Data mining-
Data mining is the process of extracting data,
analyzing it from many dimensions or perspectives and
producing a summary of the information in a useful form.
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4. Data mining used in Business
Intelligence
 Data mining is used in Business Intelligence because:
 It helps to extract, load transaction as well as transform the data
that are in the data warehouse.
 It also helps to store as well as manage the data used in
multidimensional database system.
 Data mining helps in providing data access for business analysts
as well as IT professionals.
 This also helps in analyzing the data that are generated by
application software.
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5. Aim of article
 The business Intelligence acts as a strategic factor for a
business, providing insider information to respond to
business problems:
 entering new markets
 financial control
 cost optimization
 production planning
 analysis of customer profiles
profitability… That is how data mining is used to generate
Business Intelligence.
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6. Research Method
 In the article the main concept of the IDW (Inductive Data
Warehouse) and thus introduces a new language QMBE
(Query-Models-By-Example). This language is interactive
and iterative in nature. IDW mainly helps to store the data
as well as contains the model of data mining that are
presented in the database tables.
 The language of QMBE is mainly extensible as well as
flexible as because there are new models that are made
accessible with the business users. It is important to use a
new database for all the tables
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7. Problems identified
 The problems that were identified in the article is the
problems of handling a business that includes large amount
of data and the way to identify the problem.
 Another problem that is stated in the article is finding out
the capability of tools that are needed in data mining.
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8. Conclusion
 The viability of the users helps to provide possibility to
explore the data models that are used in business
intelligence.
 The potential value, concept of Inductive data warehouse,
and a new modeling language are included in the article.
 The study also has a limitation that the system that is
explained in the article is not automated.
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9. References
 Azahara, 2019, How Data mining is used to generate Business
Intelligence, Available from: http://www.blog-
geographica.com/2016/11/15/how-data-mining-is-used-to-
generate-business-intelligence/. [15/11/2016].
 Azevedo, A.I.R.L., 2012. Data mining languages for business
intelligence (Doctoral dissertation).
 Massaro, A., Vitti, V., Lisco, P., Galiano, A. and Savino, N., 2019. A
business intelligence platform Implemented in a big data system
embedding data mining: a case of study. International Journal of
Data Mining & Knowledge Management Process (IJDKP), 9(1),
pp.1-20.
 Šehidić, A. and Junuz, E., 2016. Quality Assurance in Higher
Education Using Business Intelligence Technology. International
Journal of Education and Practice, 4(2), pp.71-83. 9