ITECH 1100: Analyzing the Impact of Data Mining on the Film Sector

This report examines the application of data mining within the film industry, focusing on technology, design, implementation, and key areas of focus. It highlights the use of technology for creating interaction with the environment, design considerations related to components interacting within the film industry (potentially through sensor-based data collection), and the implementation process involving sensor deployment, equipment integration for patient safety, and timely patient monitoring. The report emphasizes real-time data access, professional data manipulation, and easy alteration processes. References to academic papers on data mining are included to support the analysis.

DATA MINING IN FLIM
INDUSTRY

Technology
The technology which is related to the concept can be directly be related
to the aspect of creating a means of interaction with the environment.
The technology can be related to the interaction of different components
and creating a means of providing
Interaction with the environment.

Design
The design in this concept can be considered to be linked directly to the
various components which interact with the hospital industry. The
interaction is directly achieved by means of sensors which are attached
to the patient.
The main advantage which is seen in this concept is that the interaction
would be in real time without time lagging concept attached to it.

Implementation
The implementation of the technology is done mainly by:
 Deployment of sensors
 Integration of different equipment which is related to the safe guard
of the patient
 Monitoring of the patient in a timely manner which would be
implemented within the concept of the technology.

Focus
The main focus of the technology is directed towards the real tracking of
the patient. The main aspect which is related to the concept is:
 Access to the data in real time
 Manipulation of the data by professionals
 Alteration process very easy.

Reference
 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.