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TO: Prof. Ellis
FROM: Norbert Derylo
DATE: Oct 27, 2021
SUBJECT: Expanded Definition of Network
In this paper I will be writing about the definition of the word “network.” I will be writing about how network was originally used and will compare it to how it is used in modern day. I will also write about how network is used in different contexts in modern day society. I will finally write about how network is used in terms of computers and data.
Network was used in the Bible, one of the oldest pieces of christian literature. The word’s definition in that book was “Work (esp. manufactured work) in which threads, wires, etc., are crossed or interlaced in the fashion of a net; frequently applied to light fabric made of threads intersecting in this way” . This definition refers to more physical objects which is very different compared to what network is defined as modernly. One definition from modern times is “a network is nothing more than a set of discrete elements (the vertices), and a set of connections (the edges) that link the elements, typically in a pairwise fashion” . The modern definition of network is more about grouping objects together based on how they interact with each other. The modern definition is more broad and in a way agrees with the old definition. The older definition of network comes from a time in which people would be more focused on trying to survive. Nets are a tool that have a very distinct pattern and such anything with that pattern could be described as a network. Compared to modern day we do not really use nets anymore unless it is a part of your profession. Network has lost its connotation of being a net and now just refers to the pattern. As such using the modern definition of network you can describe a net as a network of threads.
Network is a word that requires context to understand what it means. A network describes how a group of objects interact, and therefore you need to know what those objects are. For example the following quote talks about networks in a corporate environment: “The knowledge network of a firm is a structural representation of its cumulative stock of rules, routines, practices, or documents and as such is the result of collective efforts of past and present employees” . In this context a network is about the knowledge a business has collected. If you take out the word knowledge you can see how all of those objects work together and can be considered a network. A network can also be viewed as a set of objects. This following quote is from a survey about wireless sensor networks: “Energy conservation in a WSN (wireless sensor network) maximizes network lifetime and is addressed through efficient reliable wireless communication, intelligent sensor placement to achieve adequate coverage, security and efficient storage management, and through data aggregation and data compression” . In this case the network is looked at as one single object instead of a set of objects that are connected. This is because in a wireless sensor network the entire network defines what it is. If you take any part of it out it loses its status as a network and instead becomes a collection of equipment. Network is very versatile in its uses and can be used to describe many things, however its definition is very dependent on its context.
In my major of computer information systems there are two major definitions. The most well known use of the word network is through computer networks. This refers to how computers connect to each other. One well known computer network is the internet. Computer networks are bluntly computers that can communicate with each other to transfer data. The other definition for network in my major is data networks. These networks are often limited in access and tend to hold more sensitive data. The difference between these two types of networks is how people interact on them. On computer networks computers and their users interact with each other. On data networks users tend to interact with a set of information and very minimally with other users. In both cases the network is defined by what you are trying to connect with as a user.
 “Network,” in Oxford English Dictionary, 3rd ed. Oxford, UK: Oxford Univ. Press, Mar. 2012, def. 1. [Online]. Available: https://www-oed-com.citytech.ezproxy.cuny.edu/view/Entry/126342?rskey=qXENxr&result=1
 Newman, M. et al. (2006) The structure and dynamics of networks. [Online]. Princeton: Princeton University Press. Available: ProQuest Ebook Central
 Brennecke, J. & Rank, O. (2017) The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study. Research policy. [Online] 46 (4), 768–783. Available: https://doi.org/10.1016/j.respol.2017.02.002.
 Jennifer Yick, Biswanath Mukherjee, Dipak Ghosal, Wireless sensor network survey, Computer Networks, Volume 52, Issue 12, 2008, Pages 2292-2330, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2008.04.002.
TO: Prof. Ellis
FROM: Norbert Derylo
DATE: Oct 6, 2021
SUBJECT: 500-Word Summary of Article About Big Data in Cloud Computing
A major challenge of working with big data is using cloud computing. Cloud computing provides many benefits that can help with big data. The study focuses on the various interactions between cloud computing and big data.
Big data is data that is difficult to store due to its volume and variety. In the article big data is defined as “a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling the high velocity capture, discovery, and/or analysis.” Big data can be described with four metrics: volume, variety, velocity, value. Big data can also be classified by five aspects: data sources, content format, data stores, data staging, data processing. Each classification of data has its own characteristics and complexities.
Cloud computing is the next generation of computing in professional settings. Cloud computing has many advantages over current computing methods. The increased popularity of wireless devices has allowed cloud computing to become extremely useful. Cloud computing and big data work hand-in-hand, cloud computing being the base on which big data thrives and expands. Due to the variety of big data, different forms of cloud computing will not work for all forms of big data.
The two sets of data on the relationship between big data and cloud computing come from scholarly sources and the different vendors of cloud computing. Case studies from different vendors demonstrate the extensive variety of research that uses cloud computing. Swift Key used cloud computing to scale its services for demand. 343 Industries used cloud computing to make their game more enjoyable. redBus used cloud computing to improve customer service for online bus ticketing. Nokia used cloud computing to process petabytes of data from their phone network. Alacer used cloud computing to improve response times to system outages. Scholar studies also used cloud computing for big data projects. Scholars studying DNA have used cloud computing to dramatically increase the speed at which they analyze DNA sequences. A case study showed that cloud computing can acquire and analyze extremely large data sources, for example data from social media. A study of microscopic images used cloud computing to submit data processing jobs to the cloud. A study used cloud computing to show that you can use cloud computing as a backup to massive failures of other computing services.
Data integrity is a data security concern and can be a concern in cloud computing as well. Transforming big data for analysis is a challenge and one of the reasons big data is not as popular as it should be. Data quality can be quite variable and cause concerns with big data. Big data allows for various different sources of data which might not follow the same structure. Privacy is also one of the biggest concerns of cloud storage. Encryption is the most popular way to keep cloud data safe. Data that is encrypted is not scalable and the computation time used for it is not practical for big data. Another possible solution is using algorithms to determine how to give out data to prevent leaks. Although studies have addressed multiple issues with cloud computing, there are few tools that can patch up the problems. Data staging is an issue involving the various different formats big data collects. There have been solutions to improve distributed storage systems, however they dont fix all the problems with optimization and accessibility. The current algorithms from data analysis are too unoptimized for the scalability of big data. Data security still and always will be an ongoing problem in cloud computing and big data.
Hashem, I. A. T. , Yaqoob, I. , Anuar, N. B. , Mokhtar, S. , Gani, A. , & Ullah Khan, S. “The rise of “big data” on cloud computing: Review and open research issues. Information Systems” Information Systems. 2015 Vol. 47, p98–115. https://doi. org/10. 1016/j. is. 2014. 07. 006