I've attached 2 documents.1. Instruction document - please read through the instruction mentioned in the "Assignment 2 - Research Report" section.2. Second document is the research paper based on this paper please make the research report.
Thanks!
MITS5509 Intelligent Systems for Analytics Assignment 1 and 2 Presentation and Research Report MITS5509 Assignment 1 and 2 Copyright © 2015-2019 VIT, All Rights Reserved. 2 NOTE: This Document is used in conjunction with Assessment 1 [Presentation & participation] and Assessment 2 [Research Report] in the Teaching and Assessment Plan document [MITS5509_Teaching and Assessment Plan.doc] Objective(s) This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to improve student presentation skills and to give students experience in researching a topic and writing a report relevant to the Unit of Study subject matter. INSTRUCTIONS These instructions apply to both Assignment 1 - Presentation and Assignment 2 Research Report. Assignment 1 - Presentation and Participation - 10% (Sessions 5-12) Individual Assignment For this component you will be required to do a 5-10 minute presentation on a recent academic paper on a topic related to Intelligent Systems for Analytics or Intelligent Systems. Some possible topic areas include but are not limited to: • Intelligent Systems for Data Warehouse systems • Evolving Intelligent Systems: Methods, Learning, & Applications • Distance Metric Learning in Intelligent Systems • Intelligent Systems for Socially Aware Computing • Data Mining techniques with IS • frameworks for integrating AI and data mining • Expert System • Structure of knowledge Engineering • IS and Support Vector Machines • IS and Neural Network Architectures • Heuristic Search Methods • Genetic Algorithms and Developing GA Applications The paper you select must be directly relevant to one of the above topics or another topic and be related to Intelligent Systems for Analytics. The paper must be approved by your lecturer and be related to what we are studying this semester in Intelligent Systems for Analytics. The paper MITS5509 Assignment 1 and 2 Copyright © 2015-2019 VIT, All Rights Reserved. 3 can be from any academic conference or other relevant Journal or online sources such as Google Scholar, Academic department repositories, or a significant commercial company involved in research such as IBM etc. All students must select a different paper. Thus, the paper must be approved by your lecturer before proceeding. In case two students are wanting to present on the same paper, the first who emails the lecturer with their choice will be allocated that paper. Please note that popular magazine or web-site articles are not academic papers. A grade of 10% of the Units mark will be awarded for your presentation and your participation in other student presentations. You are to prepare a set of powerpoint slides for your presentation. If you do not participate in at least 70% of other student’s presentations you will forfeit a significant proportion of the marks for this component. Note: if class numbers are large the presentations may be organized into groups, but students will still all need to select their own individual paper for assignment 2. In the case where presentations are arranged in groups each group can decide which students paper will be used for the presentation. The presentations will occur in sessions 5-12 on the academic calendar for the semester and the order of presentations will be by arrangement, but these will be evenly spread over those sessions. What to Submit. There is no submission for this component, you are allocated marks based on your presentation and participation Assignment 2 - Research Report - 10% (Due Session 8) Individual Assignment For this component you will write a report or critique on the paper you chose from Assignment 1, the Presentation and Participation component above. Your report should be limited to approx. 1500 words (not including references). Use 1.5 spacing with a 12 point Times New Roman font. Though your paper will largely be based on the chosen article, you should use other sources to support your discussion or the chosen papers premises. Citation of sources is mandatory and must be in the IEEE style. Your report or critique must include: MITS5509 Assignment 1 and 2 Copyright © 2015-2019 VIT, All Rights Reserved. 4 Title Page: The title of the assessment, the name of the paper you are reporting on and its authors, and your name and student ID. Introduction: Identification of the paper you are critiquing/ reviewing, a statement of the purpose for your report and a brief outline of how you will discuss the selected article (one or two paragraphs). Body of Report: Describe the intention and content of the article. If it is a research report, discuss the research method (survey, case study, observation, experiment, or other method) and findings. Comment on problems or issues highlighted by the authors. Report on results discussed and discuss the conclusions of the article and how they are relevant to the topics of this Unit of Study. Conclusion: A summary of the points you have made in the body of the paper. The conclusion should not introduce any ‘new’ material that was not discussed in the body of the paper. (One or two paragraphs) References: A list of sources used in your text. They should be listed alphabetically by (first) author’s family name. Follow the IEEE style. The footer must include your name, student ID, and page number. Note: reports submitted on papers which are not approved or not the approved paper registered for the student will not be graded and attract a zero (0) grade. What to Submit Submit your report to the Moodle drop-box for Assignment 2. Note that this will be a turn-it-in drop box and as such you will be provided with a similarity score. This will be taken into account when grading the assignment. Note that incidents of plagiarism will be penalized. If your similarity score is high you can re-submit your report, but re- submissions are only allowed up to the due date. If you submit your assignment after the due date and time re-submissions will not be allowed. Please Note: All work is due by the due date and time. Late submissions will be penalized MITS5509 Assignment 1 and 2 Copyright © 2015-2019 VIT, All Rights Reserved. 5 at the rate of 10% per day including weekends. Microsoft Word - camera Integrating Artificial Intelligence into Data Warehousing and Data Mining Nelson Sizwe. Madonsela, Paulin. Mbecke, Charles Mbohwa Abstract— Knowledge engineering is key for enhancing organizational capabilities to gain a competitive edge and adapt and respond to an unpredictable market environment. Such knowledge can be generated from collected data which is often considered complex. Organizations are collecting vast amounts of data to transform them into real-time information in order to attain successful decision-making support systems. It is more than likely that such processes can be challenging; yet such knowledge must be extracted from thoughtfully designed and implemented data warehousing, and mined to obtain the required information. This paper explores appropriate techniques, technologies and trends to facilitate the integration of artificial intelligence into data warehousing and data mining. It provides an insightful overview of data warehousing and data mining, and it highlights the techniques and the limitations of analyzing and interpreting enormous data. Index Terms— artificial intelligence, data warehousing, data mining, knowledge discovery, business intelligence. I. INTRODUCTION Information in the 21st century has become the main source of gaining competitive edge. [4, p.1] are of the opinion that there is “nothing new under the sun”, since there are “lots of old things we don’t know”. Manuscript received 21 March 2015; revised 03 April 2015 Nelson Sizwe Madonsela is a Masters student at the University of Johannesburg (UJ), Faculty of Engineering and the Built Environment (FEBE). He works for the Department of Justice and Correctional Services as an IT trainer and the Department of Higher Education as a Part-time Lecturer. Tel: +27721345265, Email: nel_madon@yahoo.com Prof Paulin Mbecke is currently a postdoctoral research fellow in the Department of Public Administration & Management, School of Economic & Management Sciences at the University of South Africa and a visiting Associate Professor in the Faculties of Political, Administrative and Social Sciences (Department of Community Development) and Economic and Management Sciences at the University du Moyen Lualaba (UML) in the DRC. Email: pmbecke@gmail.com Prof Charles Mbohwa is the Vice-Dean: Postgraduate Studies, Research and Innovation at the University of Johannesburg (UJ), Faculty of Engineering and the Built Environment (FEBE). He serves as a Professor in the Department of Quality and Operations Management. Email: cmbohwa@uj.ac.za Firms are collecting vast amounts of data daily and developing advanced data warehouse systems to secure the data. Their intention is transforming it into vital information, or knowledge, for developing decision support systems (DSS). According to [10] there are still challenges with regard to the techniques of analyzing and interpreting the exact meaning of enormous data, in fact, to integrate artificial intelligence (AI) into data warehousing. It is much more likely that critical information is often overlooked or not tapped into from these vast amounts of data, while organizations invest huge amounts of financial resources on collecting, storing and securing the same data. [5, p. 27] emphasize that: “It is crucial for business to acquire a better understanding of the commercial context of their organization, such as their customers, the market, supply and resources, and competitors. Business intelligence (BI) technologies provide historical, current and predictive views of business operations. Examples include reporting, online analytical processing, business performance management, competitive intelligence, benchmarking, and predictive analytics.” To take a case in point, there is clear evidence that, it is imperative to establish such a broad and in-depth understanding of the incorporation of concepts like data warehousing, data mining, AI and BI, to name but few. [10] maintains that is difficult to incorporate AI into data warehousing or BI. Even so, many researchers strongly believe that for business to gain competitive advantage, the usage of data warehousing is key [13], [10], [9]. However, [11] stresses that there are constraints such as the understanding of the required information, organizational culture and funding.