ASSIGNMENT-2 (30%) WIDE AREA NETWORK TECHNOLOGIES Objective: • By completing this assignment, students will be demonstrating their knowledge and skills leant from week 6-12 that include: o IPv4 and IPv6 address planning o Wide Area Network concepts o Wide Area Network technologies o Implement and configure IP routing of medium complexity 1 Business case ABC.COM Home Appliance Company has decided to migrate their network from IPv4 to IPv6 to meet a recent development in Internet of Thing (IoT). Due to the budget, only the company’s HQ and two of its branches A and B will be migrated first. Branch C migration will be carried out next year. You are hired by the company to perform the migration task. The company has been allocated the following IPv6 address block to use AB12:####::/48 to use where #### are made of the last 4 digit of the your student ID. Branch C continues to operate on IPv4 150.50.XY.0 network. (Replace XY by the last 2 digit on your student ID. The company network topology is given bellow:
2 Instruction and marking scheme To complete this assignment, you are recommended to take the steps below: 2.1 Address planning (25 marks) • Work out the IPv6 subnet for the company HQ and each branch from the given IPv6 address block. (10 marks) (Place the address allocation plan here) • Create address assignment table for the company network that contain the address of each router interfaces, PCs and Servers. (15 marks) (Place the address assignment table here) 2.2 IPv4 routing configuration (20 marks) • Configure OSPF for routing between Branch-C and HQ router. Use IPv4 150.50.XY.0/30 for network between the routers. (Place the configuration command here for each router. Then enter to Packet Tracer to implement a functional network)
2.4 IPv4IPv6 Network NAT-PT (20 marks) • Configure HQ router for IPv4IPv6 NAT router so that the new IPv6 HQ network can access the branch C server, which has the IP address 150.50.XX.100. (Place the command here. Then enter to Packet Tracer to implement a functional network) 2.5 Verification (15 marks) • Perform necessary verification to test the performance of the implemented network in packet tracer. All computers should be able to ping to any other computers on different networks and to the port on router. Use necessary screenshots of ping and show commands on computers and routers 3 Submission You have to submit this document with a working packet tracer file by the due date. The submission box is in assignment 2 content section as follows: a- Submit answer for sections 2.1 as word document in the Assignment 2 Task 1 folder b- Submit answers for section 2.2 as word file and the packet tracer file also, in the Assignment 2 Task 2 c- Submit the answers for questions 2.3, 2.4,2.5 as word file and packet tracer file (you submit two files one work and one packet tracer file) in the Assignment 2 Task 3-6 folder.
Modules 1–5 are particularly relevant for this assignment. Assignment 2 relates to the course objectives 1, 2 and 4: 1. demonstrate applied knowledge of people, markets, finances, technology and management in a global context of business intelligence practice (data warehouse design, data mining process, data visualisation and performance management) and resulting organisational change and how these apply to implementation of business intelligence in organisation systems and business processes 2. identify and solve complex organisational problems creatively and practically through the use of business intelligence and critically reflect on how evidence based decision making and sustainable business performance management can effectively address real world problems 4. demonstrate the ability to communicate effectively in a clear and concise manner in written report style for senior management with correct and appropriate acknowledgment of main ideas presented and discussed. Note you must use RapidMiner Studio for Task 2 and you must use Tableau Desktop for Task 3 in this Assignment 2. Failure to do so may result in Task 2 and/or Task 3 not being marked and zero marks awarded. Note carefully University policy on Academic Misconduct such as plagiarism, collusion and cheating. Your Assignment 2 submission is automatically submitted to and checked in Turnitin for academic integrity when you submit your Assignment 2 via the course study Assignment 2 submission link. If any of these occur they will be found and dealt with by the USQ Academic Integrity Procedures. If proven, Academic Misconduct may result in failure of an individual assessment, the entire course or exclusion from a University program or programs. Assignment 2 consists of three main tasks and a number of sub tasks Task 1 Data Analytics and Data Warehousing Concepts (Worth 39 Marks) Drawing on relevant and current literature write a short essay that addresses three sub tasks: Task1.1) Define the concept ‘Predictive Analytics’ and describe an example how an organisation has deployed predictive analytics to improve a business process or service (10 marks 250 words) Task 1.2) Identify and describe five main types of data warehouse architectures including a diagram representation for each type of data warehouse architecture (9 marks 250 words) Task 1.3 Identify and discuss five key factors that would determine the choice of a type of data warehouse architecture (20 marks 500 words) Task 2 Exploratory Data Analysis and Linear Regression Analysis (Worth 36 Marks) Carefully study the Data Dictionary for Melbourne Housing Data Set (See Table 1) and accompanying description of each variable. It is important to understand this data set as it is used for Task 2 and Task 3 in Assignment 2. Each record in the housing.csv data set describes a property that was listed for sale and sold.
Note: You should conduct some desktop research to identify determinates/drivers of property sale prices in order to fully understand and interpret the key findings of the exploratory data analysis (EDA) and Linear Regression Models for the housing.csv data set for Task 2 and visual presentation of the housing.csv data set in Task 3. Task 2.1) Conduct and report on exploratory data analysis (EDA) of housing.csv data set using RapidMiner Studio data mining tool and RapidMiner Studio operators Provide following for Task 2.1: (i) a screen capture of final EDA process, briefly describe EDA process (ii) summarise key results of exploratory data analysis in Table 2.1 Results of Exploratory Data Analysis for housing.csv. Table 2.1 should include key characteristics of each variable in housing.csv set such as maximum, minimum values, average, standard deviation, most frequent values (mode), missing values and invalid values etc. (iii) Discuss key results of exploratory data analysis presented in Table 2.1 and provide a rationale for selecting top 5 variables for predicting sale price of a property (Price), in particular focusing on the relationships of independent variables with each other and with dependent variable Price drawing on results of EDA analysis and relevant literature on what determinates property prices (20 marks 250 words) Hint: Statistics Tab and Chart Tab in RapidMiner Studio provide a lot of descriptive statistical information and the ability to create useful charts like Barcharts, Scatterplots, Boxplot charts etc for EDA analysis. You might also like to look at running correlations and/or chi square tests as appropriate to determine which variables contribute most to predicting property sale price (Price). Task 2.2) Build and report on Linear Regression model for predicting property sale price (Price) using RapidMiner data mining process and appropriate set of data mining operators and a reduced set of variables from housing.csv data set as determined by your exploratory data analysis in Task 2.1. Provide the following for Task 2.2: (i) A screen capture of Final Linear Regression Model process and briefly describe your Final Linear Regression Model process (ii) Table 2.2 named Results of Final Linear Regression Model for Task 2.2 for housing.csv data set. (iii) Discuss the results of Final Linear Regression Model for housing.csv data set drawing on key outputs (coefficients, standardised coefficients, t-statistics values, p-values and significance levels etc) for predicting property sale price (Price) and relevant supporting literature on interpretation of a Linear Regression Model. (16 marks 150 words) Include all appropriate outputs such as RapidMiner Processes, Graphs and Tables that support key aspects of exploratory data analysis and linear regression model analysis of the housing.csv data set in your Assignment 2 report.
Note: export Processes and Graphs from RapidMiner using File/Print/Export Image option, include in Task 2 section or in Appendix 2 of Assignment 2 report. Task 3 Tableau Desktop View of Housing Data (Worth 15 marks) After connecting to housing.csv data set in Tableau Desktop you consider binning variables such as Price and recoding variables such as Type and Method to create new or more informative categorical variables Task 3.1) Create a Tableau Text Table or Graph view that displays properties by sale price and type of property and other relevant data using the data set housing.csv. Comment on the (1) process of preparing a Text Table or Graph view using Tableau Desktop and (2) key trends and patterns apparent in Tableau view created (8 marks 50 words). Task 3.2) Create a Tableau Text Table or Graph view that displays property sale price and potential impact of distance to CBD on sale price (Price) and other relevant data using data set housing.csv. Comment on the (1) process of preparing a Text Table or Graph view using Tableau Desktop and (2) key trends and patterns apparent in Tableau view created (7 marks 50 words). Note: you need copy the two Text Table / Graph views you have created in Tableau using the Worksheet Menu Copy or Export Image option and include in the Task 3 section where relevant or in Appendix 3 of Assignment 2 report. Report structure, presentation, writing style and referencing (Worth 10 marks) Your Assignment 2 must be presented in report format, written in an appropriate style and supported where required with appropriate in text references using Harvard Referencing Style Your assignment 2 report must be structured as follows: Cover/Title Page for Assignment 2 Table of Contents Body of report – Task 1 main heading with appropriate sub headings Task 1.1, Task 1.2, Task 1.3 etc.. Task 2 … Task 3…. List of References List of Appendices You must submit two files for Assignment 2: 1. Assignment 2 Report for Tasks 1, 2 and 3 in Word document format extension .docx 2. Tableau packaged workbook with extension .twbx that contains required two Text Table / Graph views for Task 3 You must use the following file naming convention: 1. Student_no_Student_name_CIS8008_Ass2.docx 2. Student_no_Student_name_CIS8008_Ass2.twbx You must use Harvard referencing style – Harvard referencing resources Install a bibliography referencing tool – Endnote which integrates with your word processor. http://www.usq.edu.au/library/referencing/endnote-bibliographic-software or alternatively use an online citation tool such as Zetoro or You Cite This For Me USQ Library - how to reference correctly using Harvard referencing system https://www.usq.edu.au/library/referencing/harvard-agps-referencing-g