ITECH7407- REAL TIME ANALYTICS TEAM ASSIGNMENT WEIGHTING: 25% (15% for the report and 10% for the presentation) TASK and Due Week Group Presentation (Due Date - Week 10 – Allocated Laboratory)...

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ITECH7407- REAL TIME ANALYTICS TEAM ASSIGNMENT WEIGHTING: 25% (15% for the report and 10% for the presentation) TASK and Due Week Group Presentation (Due Date - Week 10 – Allocated Laboratory) Learning Outcomes Assessed: K4, K5, S1, A1, A2, V1, V2 Group Report (Due Date – Week 11 Sunday 11:55pm) Learning Outcomes Assessed: K1, K2, K3, K4, S1, A1, A2, V1, V2 DETAILS Write a research report of about 3000 words focusing on one of the following topics · Big Data privacy · Big data governance · Strategic issues stemming from BI&A and big data · Change management issues stemming from BI&A and big data Some of the important references are: 1. 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. 2. 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. 3. 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) 4. 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. 5. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and 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. 6. Kallinikos, J., & Constantiou, I. D. (2015). Big data revisited: a rejoinder. Journal of Information Technology, 30(1), 70-74. 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. Kallinikos, J., & Constantiou, I. D. (2015). Big data revisited: a rejoinder. Journal of Information Technology, 30(1), 70-74. 9. 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. REQUIRED Form a group of between 4 members. The following are the deliverables in this project: 1. Each group is expected to choose one of the above topics and write a research report of about 3000 words. 2. Each report should referenced 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 should clearly discussed different aspect of the chosen topic and how these aspects collectively enhance the theoretical and practical knowledge 5. Final deliverable consists of a presentation (10%) in week 10 and a research report (15%) in week 11. 6. Presentation of your findings in this project would be of about 20-minute duration. 7. Every student is expected to submit a short report to their lecturer stating their contribution to their team. Lecturers reserve the right to reduce a student’s mark if their contribution to their team is deemed insubstantial. 8. Each team is expected to present their report findings as part of the “Team Report Presentation” assessment task during week 10. You are reminded to read the “Plagiarism” section of the course description. Your essay 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. University Referencing/Citation Style Guide: The University has published a style guide to help students correctly reference and cite information they use in assignments (American Psychological Association (APA) citation style, http://www.ballarat.edu.au/aasp/student/learning_support/generalguide/print/ch06s04.shtmlor Australian citation style 10. Reports are to be presented in hard copy in size 12 Arial Font and doublespaced. 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. Report Submission: Hard-copy to tutors/lecturers assignment box in week11.Double- sided printing for the hard-copy is encouraged in order to save paper. Declaration should be done to declare how much (percentage) each of the team has contributed to the report. 12. 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. 1 Team Report – Marks 100 Weighting: 15 % for Team Report Student IDs: Assessment Criteria: 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 in quotes and quoted material in Some problems with Some problems with the acknowledged. All Quotes & acknowledged. quoted material and reference list. paraphrased material All paraphrased material paraphrased material acknowledged. acknowledged. Some problems with the Correctly set out Mostly correct setting reference list. /10 marks reference list. out reference list. SubTotal-/100 marks Total out of 15 Team Report Presentation – Marks 100 Weighting: 10% for Team Presentation Students are expected to create and present a 10-15 minute overview of the findings from their Team Report. Student IDs: Assessment Criteria: Criteria Marks Introduction /10 Content /40 Conclusion /10 Presentation Style e.g. clarity, engagement /20 Team participation /10 Timing (10-15 minutes) /10 SubTotal-2 /100 General Comments: Team assignment Total = SubTotal-1 + SubTotal-2.
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Answered Same DayMay 14, 2021ITECH7407

Answer To: ITECH7407- REAL TIME ANALYTICS TEAM ASSIGNMENT WEIGHTING: 25% (15% for the report and 10% for the...

Kuldeep answered on May 18 2021
157 Votes
Big Data
Big Data
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Introduction
In the era of the big data, we required to think in a different way regarding privacy. We required to turn our thinking from the definition of the privacy (the features of privacy) to the privacy model (how confidentiality works). In addition, along with privacy monitoring model and the existing paradigm of the capture paradigm, we also need to bear in mind a new model - the data model proposed in this paper, which uses predictive study of the collected data to derive new personal data. These three privacy models complement each other; they are not contending for privacy. This expanded method will allow our thinking to transcend present concerns by analysing the data that has been collected and ensuring that individuals agree to collect privacy issues through the development of new information about possible personal behaviours this new information may infringe on privacy but are not required agree (Chen, Chiang & Storey, 2012).
Big Data privacy
In today's world, information is readily available, which has raised privacy concerns in many different areas, including the healthcare industry. The information in the industry is more personal and sensitive, and any privacy leaks become a matter of life and death. It relies heavily on medical data from numerous sources that traditional database management systems (DBMSs) can no longer handle. Therefore, the utilization of the big data has become a major benefit of the healthcare, manufacturing, mining and many other industries. The study focuses on the strengths of the big data confidentiality and some of the major challenges. This study provides recommendations for encouraging big data paths and clarifies current privacy issues and current methods for maintaining patient confidentiality. Big data is such a large as well as complex data set that traditional data processing applications have difficulty handling them. The difficulties faced by traditional data processing apps in processing big data such as capture, analysis, sharing, storage, and transmission. Big data uses different algorithms and techniques to predict general trends rather than focusing on the precise relationship between individual pieces of data. It focuses on the amount of data than on quality, and it also details relevance rather than causality, that is, what is not the cause. Due to advances in advanced data collection methods, storage and interpretation; advances in technology have made big data possible. Data collection from several fields has exploded moreover its storage expenses and costs have fallen, thus demonstrating the reasons for retaining data instead of discarding it (Constantiou & Kallinikos, 2015).
Analysing data to support the decision-making, discovering trends and opening up new business prospects is not new. There is also no conflict between certain kinds of these processing and privacy as well as data protection philosophies. Big data collection is easily lost by individuals based on many different and unexpected source controls Because in many cases he/she doesn't even know how to process or fail to track how data flows from one the system to another. This presents noteworthy engineering challenges for how to timely and effectively inform users as well as who is responsible for this task, particularly when dealing with interactions that require many participants. The scalability of the storage permits for potentially infinite space, that means that the data may be collected constantly until new insights can be formed from the insights gained from it. For instance, mobile application providers collect private data to offer users along with information regarding their health status or health (e.g., eating habits, heart condition, etc.). These data may be beneficial to insurance corporations or/and other providers (for example, food consultants, sports centres, etc.) that might be targeted to a particular user. Another important element of data inference and re-identification of big data is a likelihood of combining data sets from several different sources in order to get more data. This can also trigger privacy risks, particularly if the source of the link is different and there may be patterns associated with a single person. For example, it is feasible to conclude information associated to an individual or a group by combining numerous so-called non-personal information. If this possibility of inference is high, unexpected personal data processing may occur. Big data management can solve problems related to information retrieval. Advances in big data management play a vital role in providing effective information in effective decision-making related to key areas of the business (Sinanc, 2015). BD environment is characterized by the ability to process several PBs of data, allocate and manage redundant data for storage, utilize processing related to parallel tasks, improve data processing capabilities, efficiently insert and retrieve information, and centrally manage data. In this article, we will focus on privacy and security issues related to the big data management on the web, as well as possible solutions for handling big data (Custers & Uršič, 2016).
The foundation of big data
Big data technology is different from previous data mining and processing technologies. It allows a larger amount of data to be processed at one time. This technique differs from previous methods in three ways, such as the conversion and transfer rate of the data, the type of data being processed, in addition, the amount of data to be processed. The technology works in an advanced way so it can connect and accept data from different sources and formats. Data analysis helps organizations make inferences, draw conclusions, and predict market behavior. Cloud computing has also proven to be an important platform for storing, processing and collecting data. It's much easier to mine the data available on the cloud because it's connected to the internet. The success of big data can be attributed to Apache Hadoop software. The software platform helps collect big data and store it in a cluster, making it easier to process and analyze. It can be extended because it can access thousands of servers and their threads from one system (Diesner, 2015).
Despite the advances and capabilities of data mining technology, many technologists are still concerned about the security, privacy, and applications of these big data. A bunch of data contains many types of personal data that can be adversely affected if it is disclosed. Unauthorized access and undesired purchases are performed when these types of data are obtained. In this process, the privacy of individuals who violated without his consent required serious data security issues. To solve this problem, a new technology conforming to data mining, Privacy Protection Data Mining has been developed. The technology aims...
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