Cloud Computing and Data Management

The assignment delves into the concepts of cloud computing service models (SaaS, PaaS, IaaS) and their implications for infrastructure management. It discusses data mining techniques for extracting knowledge from data, highlighting the use of algorithms for pattern recognition and prediction. Furthermore, it examines adaptive resource configuration strategies employed in cloud environments to optimize performance and cost efficiency. The document emphasizes the significance of understanding these concepts in effectively managing contemporary IT infrastructures.

Running head: I.T INFRASTRUCTURE MANAGEMENT P.G
I.T infrastructure management P.G
Name of the Student
Name of the University
Author’s note

1I.T INFRASTRUCTURE MANAGEMENT P.G
Table of Contents
1. Definition and explanation of the following terminologies...................................................2
• Just-in-Time Delivery..........................................................................................................2
• E-Commerce........................................................................................................................2
• SaaS.....................................................................................................................................2
• Strategic planning................................................................................................................2
• Supply Chain systems..........................................................................................................3
• DSS......................................................................................................................................3
• Cloud infrastructure.............................................................................................................3
• Web 2...................................................................................................................................3
• Extranet................................................................................................................................3
• Big data analysis..................................................................................................................4
2. Major elements of Data Mining with examples.....................................................................4
3. Importance of ‘’Reserve Stock Level’’ function of ERP critical for the systems’
performance................................................................................................................................4
4.1. Business risks that have been faced by Liberty Wines.......................................................5
4.2. Competitive advantage gained by Librerty Wines’ by adopting the IT infrastructure
model..........................................................................................................................................6
4.3. Server virtualization benefit to Liberty Wines and the environment..................................6
5.1. Issues which are limiting FinCEN’s capability to fight against the financial crime...........6
5.2. IT upgrades to mitigate problems FinCEN is facing..........................................................7
5.3. Factors on which financial intelligence depend..................................................................7
5.4. Necessity to identify patterns for ensuring national security..............................................7
5.5. Explanation of the act and responsibility of data analytics in crime detection...................7
References..................................................................................................................................8
1. Definition and explanation of the following terminologies

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• Just-in-Time Delivery
Just-in-Time Delivery is a sort of inventory management procedure which involves
components, goods and manpower to be processed exactly at the time of need without any
delay (Ouma, Njeru & Dennis 2013).
• E-Commerce
E-commerce involves the purchasing and selling of products or goods and the
transaction of money online over the Internet. There are six types of e-commerce are-
Consumer-to-Administration (C2A), Consumer-to-Business (C2B), Business-to-Consumer
(B2C), Consumer-to-Consumer (C2C), Business-to-Administration (B2A), Business-to-
Business (B2B) (Flanagin et al., 2014).
• SaaS
Software as a Service (SaaS) is a software distribution strategy model via which third
party cloud vendor hosts applications which are cloud based to the users over the network
(Kavis, 2014).
• Strategic planning
Strategic planning involves the management activities that define planning, designing,
directions in accordance with the design. and lastly following steps to effectively execute the
plan (Stadtler, 2015).
• Supply Chain systems
Supply chain system is a procedure which involves the transformation of the natural
resources as well as the raw materials into a complete product and transacted to the
consumers (Stadtler, H. 2015).

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• DSS
A decision support system is a computer application program that accumulates
business data and helps to analyse those data and later delivers it to the users so that they can
take effective decisions.
• Cloud infrastructure
Cloud infrastructure involves the transmittal of the computing services- software,
server, database, networking over the Internet, the cloud vendors offer this service to the
users or the business organisations where they deploy cloud apps (Maurer, Brandic &
Sakellariou, 2013).
• Web 2
Web2 is an advanced web technology and its primary objective is to provide security,
creativity, advanced web-based communities greatly focusing on the social media web sites
like Facebook, Myspace and the video sharing sites like YouTube (Alexopoulos, Loukis &
Charalabidis, 2014).
• Extranet
An extranet is a private Internet network connection that grants access to the external
authorized users, that allow the users to communicate with the business organisations in a
more secure way (Rekhter et al., 2016).
• Big data analysis
Big data analytics help to show lights on the data accumulated by the social media
sites, sensors, digital images and records, the primary objective is to analyse those data to
know the behaviours and patterns of the users' demands, wish list and the current trends
(Raghupathi & Raghupathi, 2014).

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2. Major elements of Data Mining with examples
Data mining key elements’ examples are
i. Utilising data mining’s seasonal approach technique: This data mining technique
helps to make an annual budget plan or an annual revenue plan for a particular company
based on the latest prevalent trends.
ii. Utilising data mining logistic approach: The Company can take efficient
marketing techniques and strategies by reducing the costs of the products and sell in large
quantity in the market.
iii. Utilising data mining cluster analysis technique: The Company can divide their
customers based on their demands or wish list and latest trends (Witten et al., 2016).
iv. Utilising data mining market basket analysis: The company with the help of
market basket analysis can predict the latest trends and which items should be appeared
together and what will the customers wish to buy (Larose, 2014).
3. Importance of ‘’Reserve Stock Level’’ function of ERP critical for the systems’
performance
Reserve Stock Level helps in the finding the profit of the business organisations in
each department, basically, no profit is gained until the goods are sold to the external
stakeholder. The Reserve Stock Level helps in adjusting the profit elements within the stock
and thus the actual profit can be predicted which has been earned by the companies
(Lakdawala & Schaffer, 2016). The opening stock provides the calculation of the profit
elements earned last year whereas the closing stock lessens its cost thereby converting it to
credit side from the debit side. This closing stock gives the analysis of the latest year. Thus
Reserve Stock Level plays an important to know the profit elements.

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4.1. Business risks that have been faced by Liberty Wines
Liberty Wines faced the following risks-
i. Pre-shipment analysis- The excessive heat or cold can be threat to wines, during
packing or shipping the wine remains in good quality but at the time of delivery or after the
delivery the temperature and the climate change can affect the wine and can damage the
quality, it is very difficult to predict actually when the wine gets damaged, if gets damaged
then to what extent, this is the risk which Liberty Wine faces and this causes severe loss to
the company (Goetz, 2013). There are certain parameters like dissolved oxygen, colour,
phenolics, sulphur dioxide which is used to detect the wine damage due to climate change.
ii. Analysis of wine integrity- The ingredients of the wine should be properly mixed
or integrated otherwise the wine quality can be severely damaged, in another case, it may
happen Liberty Wines loses its authenticity. The reputation of the company can severely
suffer in this scenario. This is another risk.
iii. Analysis of wine stability- Another risk is the wine spoilage. The customers
purchase wines tasting the wine whether it contains, if any the spoilage microbes or not, the
chemical metabolites are present or not and lastly the aroma compounds are analysed, all
these screening tests pass then the wine is fine otherwise it is spoiled, so this is the third risk
(Wright, 2014).
4.2. Competitive advantage gained by Librerty Wines’ by adopting the IT
infrastructure model
Liberty Wines simply via lessening the costs of their office premises’ hardware
components, lessening the costs of power and air conditioning, via enhancing the flexibility
and stability, via strong backup can provide the business competitive edge they want to

6I.T INFRASTRUCTURE MANAGEMENT P.G
achieve. The IT infrastructure also assists speeding up the business activities, hence provides
better customer service. The IT infrastructure assists in further agile and effective business
growth (Goetz, 2013). The mentioned capabilities provide the companies with the benefits
over their competitors by providing cost-effective service.
4.3. Server virtualization benefit to Liberty Wines and the environment
The number of servers lowered from ten to four results in diminishing power
consumptions and the air conditioning expenses to about sixty percentage, this also enhances
the bottom lines as well as carbon footprint (Sandoval et al., 2016). The software applications
can execute faster because of the virtualization and this fast execution provides comparatively
better customer satisfaction. As a result of virtualization the expenses of hardware
components can be cut down to around $70,000 and at the time of need, the cloud servers can
be used to deploy the Liberty Wines apps whenever required.
5.1. Issues which are limiting FinCEN’s capability to fight against the financial crime
FinCEN deals with the organized crime related to financing, tax fraud and money
laundering (Sandoval et al., 2016). The problems associated with are- under reporting of
individual's income, over-reporting of the individual income's deductions and underlying
business activities of the individuals due to lack of digital transaction record of data.
5.2. IT upgrades to mitigate problems FinCEN is facing
Under-reporting of personal incomes can be solved by the digital data recording
techniques incorporated with the sales machine and cash registers, the over-reporting of the
deductions can be verified or detected by the automatized reporting of transaction and
electronic invoicing (Calvery & Director, 2015). The underlying business activities can be
predicted by digital vigilance, so upgrading to IT can be beneficial to FinCEN.

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5.3. Factors on which financial intelligence depend
The financial intelligence depends on the large chunks of data generated as a result of
data mining techniques and these data are provided by banks and the certain organisations
(Calvery & Director, 2015). The financial intelligence duty is to detect the money laundering
or tax evasion or some illegal money transactions in which an individual is involved.
5.4. Necessity to identify patterns for ensuring national security
The identification of fraud is important as it detects fraud faster and provides
solutions faster for the remedy of threats, the digital technology can assist diminishing the
underlying fraud, data leaks and data theft which are jeopardizing national safety issues
(Sandoval et al., 2016). The patterns will help to prevent the risks that are going to exploit in
mere future.
5.5. Explanation of the act and responsibility of data analytics in crime detection.
FinCEN Fines Western Union Financial Services, Inc. for violating the rules
correlated with money laundering. They failed to comply with the requisites of anti-money
laundering rules, failed to configure an efficient AML program in their premises, also they
did not provide suspicious activity reports in due time (Sandoval et al., 2016). That is why
they will have to pay the penalty applied by FinCEN.
The data analytics method like profiling, behaviour monitoring, pattern recognition
and lastly the network analysis help in detecting the crimes occurring. These methods are
applied by FinCEN to catch the financial fraudsters.

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References
Alexopoulos, C., Loukis, E., & Charalabidis, Y. (2014). A platform for closing the open data
feedback loop based on Web2. 0 functionality. JeDEM-eJournal of eDemocracy and
Open Government, 6(1), 62-68.
Calvery, S., & Director, J. F. (2015). Remarks Delivered at the Predictive Analytics World
for Government Conference. Washington, DC, https://www. fincen.
gov/news_room/speech/pdf/20151013. pdf.
Flanagin, A. J., Metzger, M. J., Pure, R., Markov, A., & Hartsell, E. (2014). Mitigating risk in
ecommerce transactions: perceptions of information credibility and the role of user-
generated ratings in product quality and purchase intention. Electronic Commerce
Research, 14(1), 1-23.
Goetz, L. (2013). The Collective at the House of St Barnabas.
Kavis, M. J. (2014). Architecting the cloud: design decisions for cloud computing service
models (SaaS, PaaS, and IaaS). John Wiley & Sons.
Lakdawala, A., & Schaffer, M. (2016). Federal Reserve Private Information and the Stock
Market.
Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John
Wiley & Sons.
Maurer, M., Brandic, I., & Sakellariou, R. (2013). Adaptive resource configuration for Cloud
infrastructure management. Future Generation Computer Systems, 29(2), 472-487.
Ouma, A. M., Njeru, A. W., & Dennis, J. (2013). Assessment of the influence of Just in Time
(JIT) delivery of materials in managing cost levels in the pharmaceutical industry in

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Kenya. International Journal of Academic Research in Business and Social
Sciences, 3(11), 185.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and
potential. Health information science and systems, 2(1), 3.
Rekhter, Y., Rosen, E., Aggarwal, R., Cai, Y., & Morin, T. (2016). Extranet Multicast in
BGP/IP MPLS VPNs (No. RFC 7900).
Sandoval, I., Sandoval, I., Horn, C., Horn, C., Hall, M., & Hall, M. (2016). Fincen requires
financial institutions to obtain beneficial ownership information for legal entity
customers. Journal of Investment Compliance, 17(4), 34-44.
Stadtler, H. (2015). Supply chain management: An overview. In Supply chain management
and advanced planning (pp. 3-28). Springer Berlin Heidelberg.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.
Wright, A. (2014). ICT540: Enterprise Architect Blog. Management.