this assignment needs to be done professiopnallyITECH7407 Tasks 2 and 3 Page 1 of 6 ITECH7407 REAL...

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this assignment needs to be done professiopnally

ITECH7407 Tasks 2 and 3 Page 1 of 6 ITECH7407 REAL TIME ANALYTICS Semester 2019/17 TEAM ASSIGNMENTS A. TASKS, DUE DATES AND WEIGHTINGS Assessment Attribute Task 2: Research Report Presentation (Team) Task 3: Research Report (Team) Due Date Week 8 (During tutorials/lab) 15 Sept 2019, 5pm Learning outcomes assessed K4, K5, S1, A1, A2, V1, V2 K1, K2, K3, K4, S1, A1, A2, V1, V2 Weighting 10% 15% Details of the intended learning outcomes assessed can be found in the course description. B. INSTRUCTIONS Under the leadership of the lecturer or/and tutor, students are expected to work on this two-part project or assignment (i.e. Task 2 and Task 3) in teams of 3 to 5 members. Each team shall choose one of the topics listed in Section C of this document. Both the team presentation and the team research report shall be based on the same topic. The following are the deliverables in this project: 1. Each team is expected to choose one of the topics listed in the next section, research thoroughly on it and write a research report of about 3000 words. 2. Each report should reference a minimum of 15 peer-reviewed journal articles and conference papers. 3. The report should be well researched and written in accordance with APA referencing style. 4. Each team should clearly discuss different aspects of the chosen topic and how these aspects collectively enhance the theoretical and practical knowledge. 5. Final deliverable consists of a presentation and a research report following the schedule above. 6. Presentation of your findings in this project would be of about 20-minute ITECH7407 Tasks 2 and 3 Page 2 of 6 duration. 7. Declaration of contributions: Each student must speak during the presentation part of the assignment. Also, each team must clearly state on the second page of the submission (after the cover page) the contributions of each team member in the project in a tabular form according to the format: Student ID Student Name Contrib utions to Project Lecturers/tutors reserve the right to reduce a student’s mark if their contribution to their team project is deemed insubstantial. 8. You are reminded to read the “Plagiarism” section of the course description. Your submission should be a synthesis of ideas from a variety of sources expressed in your own words. 9. All reports must use the APA referencing style. Federation University has published a style guide to help students correctly reference and cite information they use in assignments (American Psychological Association or APA) citation style, which can be found at http://www.ballarat.edu.au/aasp/student/learning_support/generalguide/ print/ch06s04.shtmlor Australian citation style. 10. Both power point slides for the team presentation and the team report must be submitted on Moodle by the respective due dates. Reports should use Arial Font with size 12pt and double spaced. Your report should include a list of references used in the essay and a bibliography of the wider reading you have done to familiarize yourself on the topic. 11. A passing grade will be awarded to assignments adequately addressing all assessment criteria. Higher grades require better quality and more effort. For example, a minimum is set on the wider reading required. A student reading vastly more than this minimum will be better prepared to discuss the issues in depth and consequently their report is likely to be of a higher quality. So before submitting, please read through the assessment criteria very carefully. http://www.ballarat.edu.au/aasp/student/learning_support/generalguide/print/ch06s04 http://www.ballarat.edu.au/aasp/student/learning_support/generalguide/print/ch06s04 ITECH7407 Tasks 2 and 3 Page 3 of 6 C. ASSIGNMENTS TOPICS Each team is expected to choose ONLY one of the following topics for both parts of the assignment: 1. A relevant industry application of real-time analytics 2. Challenges and potential solutions in real-time big data analytics 3. Real-time Big Data privacy 4. Real-time Big data governance 5. Strategic issues stemming from business intelligence, business analytics and big data 6. Change management issues stemming from business intelligence, business analytics and big data D. REFERENCES AND RELATED WORK Each team is expected to source and cite appropriately at least 15 journal and conference papers related to their chosen topic. Below are some references which can be of help: 1. Tseng, F-H., Hsueh, J.-H., Tseng, C.-W., Yang, Y.-T., Chao, H.-C., & Chou, L.- D. (2018). Congestion Prediction With Big Data for Real-Time Highway Traffic. IEEE Access, 2018(6), 57311 – 57323. 2. Ranjitha, P. (2015). Streaming Analytics over Real-Time Big Data. Global Journal of Computer Science and Technology: Software & Data Engineering, 15(5), 26-30. 3. He, Y., Yu, F.R, Zhao, N., Yin, H., Yao, H., & Qiu, R.C. (2016). Data Analytics in Mobile Cellular Networks. IEEE Access, 2016(4), 1985–1996. 4. Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: big data and the changing context of strategy. Journal of Information Technology, 30(1), 44-57. 5. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188. 6. Ketter, W., Peters, M., Collins, J., & Gupta, A. (2015). Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics. MIS Quarterly. (forthcoming) 7. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data. The management revolution. Harvard Business Review, 90(10), 61-67. 8. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and https://ieeexplore.ieee.org/author/37086495645 https://ieeexplore.ieee.org/author/37085502598 https://ieeexplore.ieee.org/author/38090201100 https://ieeexplore.ieee.org/author/37273526700 https://ieeexplore.ieee.org/author/37301808400 https://ieeexplore.ieee.org/document/8481486/ https://ieeexplore.ieee.org/document/8481486/ https://ieeexplore.ieee.org/author/37085819980 https://ieeexplore.ieee.org/author/38516823600 https://ieeexplore.ieee.org/author/37896294800 https://ieeexplore.ieee.org/author/37085808336 https://ieeexplore.ieee.org/author/37085510704 https://ieeexplore.ieee.org/document/7429688/ https://ieeexplore.ieee.org/document/7429688/ https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 ITECH7407 Tasks 2 and 3 Page 4 of 6 Byers, A. H. 2011. “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” McKinsey Global Institute. http://www.mckinsey.com/business-functions/business- technology/ourinsights/big-data-the-next-frontier-for-innovation; access on 4 March 2016. 9. Kallinikos, J., & Constantiou, I. D. (2015). Big data revisited: a rejoinder. Journal of Information Technology, 30(1), 70-74. 10. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data. The management revolution. Harvard Business Review, 90(10), 61-67. 11. Kallinikos, J., & Constantiou, I. D. (2015). Big data revisited: a rejoinder. Journal of Information Technology, 30(1), 70-74. 12. Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), 433-441. ITECH7407 Tasks 2 and 3 Page 5 of 6 Assessment Criteria for Team Report – 100 Marks (15% Weighting) Student IDs: Score Very Good Good Satisfactory Unsatisfactory (0) Presentation Information is well Information is Information is somewhat Information is somewhat /Layout organized, well written, organized, well written, organized, proper organized, but proper and proper grammar with proper grammar grammar and grammar and and punctuation are and punctuation. punctuation mostly punctuation not always used throughout. Correct layout used. used. Correct layout used. Some elements of /05 marks Correct layout used. used. layout incorrect. Structure Structure guidelines Structure guidelines Structure guidelines Some elements of Enhanced followed exactly mostly followed. structure omitted /10 marks Introduction Introduces the topic of Introduces the topic of Satisfactorily introduces Introduces the topic of the report in an the report in an the topic of the report. the report, but omits a extremely engaging engaging manner which Gives a general general background of manner which arouses arouses the reader's background. the topic and/or the the reader's interest. interest. Indicates the overall overall "plan" of the Gives a detailed general Gives some general "plan" of the paper. paper. background and background and indicates the overall indicates the overall /10 marks "plan" of the paper. "plan" of the paper. Discussion of All topics discussed in Consistently detailed Most topics are Inadequate discussion Topics depth. Displays deep discussion. Displays adequately discussed. of issues Little/no analysis of issues with sound understanding Displays some demonstrated no irrelevant info. with some analysis of understanding and understanding or issues and no irrelevant analysis of issues. analysis of most issues Information and/or some irrelevant /50 marks information. Conclusion An interesting, well A good summary of the Satisfactory summary of Poor/no summary of the written summary of the main points. the main points. main points. main points. A good final comment A final comment on the A poor final comment on An excellent final on the subject, based subject, but introduced the subject and/or new comment on the on the information new material. material introduced. subject, based on the provided. /15 marks information provided. Referencing Correct referencing Mostly correct Mostly correct Not all material correctly (APA). All quoted referencing (APA). All referencing (APA ) acknowledged. material

itech7407-team-assignment-task-2-research-report-presentation-zc1yyuub.pdf

Ashmita · Accepted Answer

PowerPoint Presentation
REAL TIME BIG DATA PRIVACY
PRESENTATION OF FINDINGS
iNTRODUCTION
In the last few decades, the world has experienced a major transformation in the field of science and technology.
No wonder, that Big Data, as a technological term, is almost new to this generation of tech savvy youth; however, the need of the companies to collect and store large size of data has been quite old.
In the early years of 2000s, the technological concept of Big Data came into prominence holding the hand of the eminent industry analyst named Doug Laney.
Introduction
In the last few decades, the world has experienced a major transformation in the field of science and technology. The rigorous research and development work carried out by the proficient scientists, worldwide has empowered humanity to take a step closer to the finest living condition. Notably, Wamba et al. (2017) have pointed out that the corporate world, today, manages and relentlessly deals with hefty information for different analytical purposes those are conducive for the development of the business firms. The wide spectrum of technological tools adopted by the top notch business organisations serves as a significant aid to manage the data and accordingly, utilise them for sheer business reasons. No wonder, that Big Data as a technological term is almost new to this generation of tech savvy youth; however, the need of the companies to collect and store large size of data has been quite old. In the early years of 2000s, the technological concept of Big Data came into prominence holding the hand of the eminent industry analyst named Doug Laney. To put it simply, Big Data offers the users, who are primarily business analysts, the following aspects that enabled it to successfully gain its footing. In this report, privacy issues concerning the use of Big Data in real time business transactions will be elucidated. 
2
Main Body
Big Data definition with appropriate examples
Big Data, as the term suggests, denotes a massive size of information, structured as well as unstructured, that come across major challenges during processing for its large volume using the traditional software and databases.
It is interesting to note that few of the major instances of Big Data include that of 
Social media site such as Facebook 
New York Stock Exchange
Jet engines and others.
Big Data definition with appropriate examples
Big Data, as the term suggests, denotes a massive size of information, structured as well as unstructured, that come across major challenges during processing for its large volume using the traditional software and databases. The business organisations gather information for analysis to derive a paradigm of client or customer behaviour. However, the noticeable factor is that with the progression of time, the data keep on growing at an exponential rate. It is interesting to note that few of the major instances of Big Data include that of social media site such as Facebook, New York Stock Exchange, Jet engines and others. Going by the statistical reports, almost 500 terabytes or more data get channelized on a daily basis into the Facebook database. Sun, Song, Jara and Bie (2016) have mentioned that the New York Stock Exchange is involved in generating daily a volume of nearly one terabyte of trade information. Lastly, it is expected that the Jet Engine has the capability to generate a data volume of around 10 terabytes within half an hour of the flight timing. Interestingly, as thousands of flights is into the operation each day, therefore, the total volume of data goes as high as the Petabytes. 
3
Classification of Big Data 
Big Data can be categorized into three forms such as 
Unstructured
Structured 
Semi-structured.
A suitable example of the structured data is the table of the employees of a particular organization present in its database.
The Google search result is categorised as an unstructured data
Finally, Wang, Kung and Byrd (2018) have explained the semi-structured data is characterised by both the data types, where the semi-structured appears in the form of structured data; however, it remains undefined in relation to the DBMS.
Classification of Big Data 
Big Data can be categorized into three forms such as unstructured, structured and semi-structured. The structured data can basically be stored and be easily accessed along with getting processed in a fixed format. In the opinion of Fernández, del Río, Chawla and Herrera (2017), with the constant evolution of technology, computer science has successfully managed to come up with better techniques and technological tools to deal with such data. A suitable example of the structured data is the table of the employees of a particular organization present in its database. On the contrary, unstructured data are the information that does not possess a particular known structure. Besides, the unstructured data being massive in size, it also comes across a number of problems while processing for extracting the value out of the data. In the recent times, the business enterprises have witnessed a common problem where they are unable to extract the value out of the date because they remain in their unstructured form. 
The Google search result is categorised as an unstructured data. Finally, Wang, Kung and Byrd (2018) have explained the semi-structured data is characterised by both the data types, where the semi-structured appears in the form of structured data; however, it remains undefined in relation to the DBMS. The data present in the XML file is an appropriate instance of semi-structure data. Business enterprises, worldwide, collect large volume of information from diversifying sources such as social media, sensor, business transactions or others. The business organisations in the previous decades have struggled to manage the information but now with technological tools such as Big Data and Hadoop, the pressure on the firms has eased.
4
Different Aspects of Big Data
Big Data has gradually gained its momentum across the technological domain of businesses by offering the users with better volume for large data storage along with four other beneficial aspects.
The variety of data can be classified into the following formats such as structured data, unstructured data in the form of email, text documents, audio, video, financial transaction, stock data and other numeric data.
Complexity of the data arises due to its inflow from wide range of sources. 
Finally, Bello-Orgaz, Jung and Camacho (2016) have referred variability to the inconsistent nature of the displayed data that appear at times. 
These fundamental characteristics of Big Data significantly contribute towards improving the quality of customer service, decision making ability and operational productivity. 
Different Aspects of Big Data 
Big Data has gradually gained its momentum across the technological domain of businesses by offering the users with better volume for large data storage along with four other beneficial aspects. The business enterprises, every day, deals with significantly large volume of data derived from social media channels or other transactions. Tene and Polonetsky (2011) have stated the data storage and its processing have become seamless in the recent times, with the application of the Big Data. In the real time business transaction, with a remarkable speed, which is offered never before, the data streaming takes place within stipulated time limit. The variety of data can be classified into the following formats such as structured data, unstructured data in the form of email, text documents, audio, video, financial transaction, stock data and other numeric data. Mai (2016) has opined that this particular variety belonging to the unstructured data bring with itself certain storage, data analysis related issues along with mining problem. 
Complexity of the data arises due to its inflow from wide range of sources. Consequently, the process of linkage, cleansing, matching and transforming data become challenging across the entire system. It is, indeed, a mandate to develop a connection and correlate the relationships, data linkages and hierarchies. Finally, Bello-Orgaz, Jung and Camacho (2016) have referred variability to the inconsistent nature of the displayed data that appear at times. It is likely to hinder the processing, managing and handling of the data. These fundamental characteristics of Big Data significantly contribute towards improving the quality of customer service, decision making ability and operational productivity.

ITECH7407 Tasks 2 and 3 Page 1 of 6 ITECH7407 REAL TIME ANALYTICS Semester 2019/17 TEAM ASSIGNMENTS A. TASKS, DUE DATES AND WEIGHTINGS Assessment Attribute Task 2: Research Report Presentation (Team)...

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