Data Mining in Film and Television Industry

This article discusses the concept of data mining in the film and television industry, its benefits, ethical aspects, and potential disruptions. It emphasizes the importance of generating meaningful data and securing it properly. The article also includes references for further reading.

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Data mining in film and television industry
Name of the Student
Name of the University
Author Note

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Table of Contents
Data mining in film and television industry....................................................................................1
Data mining in film and television industry....................................................................................3
Brainstorming..................................................................................................................................3
Ethical aspect...................................................................................................................................4
Disruption........................................................................................................................................4
References........................................................................................................................................6

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Data mining in film and television industry
Data mining can be considered as a process which turns raw data into data which has
some sort of meaning attached to the concept. In most of the cases it is seen that with the help of
software data can be directly be manipulated which directly helps the business organization to
learn more about organizations and build strategy of business. It can be stated that it is a very
meaningful concept which alters the business scenario
Linking the concept of the data mining in the prospective of film industry it can be stated that it
helps in the concept of the creation of manipulation of the data which are feed into the system.
The main role of the system is to generate meaning into the concept. It can be used to extract
data in a meaningful manner.
Brainstorming
Point 1: The point of emphasis which is related to the concept can be on the field of the
access of the generation of the data. Sometimes it can be stated that data which is generated are
of no meaning which abrupt the proper working within the system. Sometimes it can be very
confusing in order to generate a data which is meaningful.
Point 2: The data which is generated can be directly used in the concept of the recreation
of the idea in the concept of the film. Sometime it can be stated that there are a vast number of
business prospective which can be applied to the concept. Sometimes it can be stated the concept
can be very much beneficial in an ideal way which directly alters the framework of the business.
Point 3: In most of the scenario it is seen that the data in the system is not so secured. As
a result of which the data would be easily accessible by others by means of altering the sequence

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input. In majority times it can be seen that the data would be used by different purposes
depending upon the need and the desires of its output.
Point 4: The data can be stated to be one of the most important factors in the path of the
functionality of the system. It can be stated that the security of the data should be the main
concern area and should be provided with appropriate access permission. The people who would
be engaged into the concept should be only authorized person who would be directly
Ethical aspect
The main ethical aspect which is related to the concept can be on the field of the access
which is generated in the data of the system. It can be stated that in any system the data are very
much important which play a very vital role which alters the normal functionality of the system.
In most of the cases it is seen that the system is directly altered due to the access permission of
the data which can be used in the concept of the generation of the data which would be involved
into the concept of the generation of films. In most of the cases it is seen that the data which
would be involved into the processing of the sequence should be very much meaningful which
would be helping the system to learn from the data and function accordingly.
Disruption
Robert Rauch, CHA, is CEO of RAR Hospitality, a leading cordiality administration firm
with 20 inns under administration. Mr. Rauch has more than 35 years of friendship-related
administration involvement in all aspects of the business. Generally perceived as the "lodging
master," Mr. Rauch keeps up a blog where he elucidates upon experiences and patterns in the
friendliness business for different factors of filming related to the data. Alongside the blog, he
additionally distributes Hospitality Insights, a month to month electronic bulletin. Mr. Rauch

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held almost every situation in the lodging business including General Manager of full-benefit
Four Diamond inns for Hilton and Embassy Suites in Phoenix. With a four-year college
education in Hotel Administration from Western International University and a graduate degree
in Tourism Administration from Arizona State University, Rauch has filled in as president and
on the leading body of various tourism associations. Mr Rauch is a Faculty Associate at Arizona
State University and California State University San Marcos. He is broadly cited on TV, radio
and in various distributions including the Wall Street Journal and Los Angeles Times. He has
built up a few driving brand inns, some of which the firm still claims and oversees and opened a
Fairfield Inn and Suites by Marriott in San Diego this past summer and a Doubletree by Hilton in
Phoenix this month (Ivanov, S. H., Webster, C., & Berezina, K. (2017).

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References
Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: an introduction to data
mining. John Wiley & Sons.
Lu, H., Setiono, R., & Liu, H. (2017). Neurorule: A connectionist approach to data
mining. arXiv preprint arXiv:1701.01358.
Roiger, R. J. (2017). Data mining: a tutorial-based primer. CRC Press.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions
on knowledge and data engineering, 26(1), 97-107.