VAProject

IS428 Visual Analytics for Business Intelligence

About

Weekly Session

Assignments

Visual Analytics Project

Course Resources

Putting Concepts, Methods and Principles into Practice

Overview

The purpose of this project is to provide you with first-hand experience on collecting, processing and analysing large business data using real world data. A project may involve developing new methods or implementing visual analytics system to support analytic tasks in specific domains. Alternatively, a project may be in the form of application development by integrating analytical tools within a visual analytics environment. You are encouraged to focus on research topics that are relevant to your field of study. It should address a concrete visual analytics problem and should propose a novel and creative solution.

The project is to be done in a team. You are required to form a project team of 2-3 members by the end of second week of the academic term. Each project team must start thinking about their project ideas from third week onward. You are expected to discuss your project topic and scope of works with the instructor before week 7 of the academic term. A project website must be prepared and uploaded to the course wiki page by the end of week 8.

In week 14, all project teams will give a poster presentation outlining the motivation of the project, design principles, implementation process, analytical methods used and findings of their project. By the end of week 14, students are also required to submit the final artifact and the project report.

Project Milestone

• Formulation of project ideas and create project page on course wiki: Week 3-7
• Discuss project topic, approach and scope of work with course mentor: Week 3-7
• Upload detail project proposal: By the end of week 8, 13th October 2019 (Sunday), time: mid-night 23:59pm.
• Submission of project poster: On week 13: 14th November 2019, by 9.00am
• Visual Analytics Townhall Presentation: On week 14: 22th November 2019, 11:30am-4:00pm
• Submission of artifacts and updating the final wiki page: On week 14, 24th November 2019 (Sunday), by 23:59pm

Project Deliverables

Project Proposal

As a first step, you should create a project proposal (in the form of a wiki page) that includes the names of the members in your group and a short (Not more than 350 words) description of the visual analytics issues or problems you would like to address.

Detail Project Proposal

A good way to assess the strengths and weaknesses of your project proposal is to share your ideas in to your classmates for feedback. Thus, each group will be expected to prepared a comprehensive proposal on the wiki page by the end of week 8. The project proposal should include, but not limited to the following material:

• description of the problem and motivation, explaining why it is worth addressing.
• describe the data and provide a list of data used.
• a background survey of related work and a list of references. Include the 2-3 most relevant pieces of prior work in your presentation.
• post your full list of references to your wiki page.
• a list of the key technical challenges your group expects to face and a description (or storyboard) of the approach you plan to use to address the challenge.
• a list of milestones breaking the project into smaller chunks and a description of what each person in the group will work on.

Note: Be sure to include a Comments directive at the bottom of the detail proposal page so that your classmates can share feedback.

Poster

The project poster should provide an overview of your project. It should include, but not limited to the following information:

• Issues and problems - A clear statement of the issues or/and problems your project addresses.

• Motivation - An explanation of why the issues and/or problems are interesting and what make them difficult to solve.

• Approach - A description of the techniques or algorithms you used to solve the problem.

• Results - Screenshots and a working demo of the system you built.

• Future Work - An explanation of how the work could be extended.

The dimensions for the poster must conform to the International Standards Organization (ISO) poster size format (A1). The dimensions for A1 format are 594mm × 841mm, or approximately 23.4" × 33.1". Either landscape or portrait orientation is acceptable. It has to be in jpeg format. Please ensure that the poster is in high resolution, at least 300dpi.

The course instructor will be responsible for printing your poster. You are required to upload your posters to the wiki page of your project and the eLearn Dropbox by the deadline given above.

Note: The poster will be considered a final deliverable, so don't forget to apply good visual design and data visualisation principles and best practice to your poster.

Final Deliverables

The final deliverables will include:

• artifact, an implementation of your system (source code and executable)
• a user guide.
• a research paper (not more than 6 pages)
  • File:Research Paper Template.doc
  • Sample research paper [1]

The final deliverables must be uploaded into the Dropbox of e-Learn (e.g. LMS). It must in as single zip file format.

Visual Analytics Townhall Presentation

We will organise a townhall presentation of the final projects. The presentation will be in the form of a poster session and live demo. You are required to bring a laptop with a working demo of your system. You should set up the laptop near your poster and use it to explain your project.

• Venue: SIS Basement Concourse

• Time: 11:30am-4:00pm (All team should come down to set-up the poster by 11:00pm)

During the presentation session, visitors and course instructor will view the various posters that are put up and pose questions to find out more details of the project. Be prepared to give a short 5-10 minute oral explanation and demo of what you did.

Grading

The visual analytics project will account for 40% of your final grade in the course. The distribution of marks for each stage of the project are as follows:

• Project wiki page 5%
• Poster 5%
• Townhall presentation 5%
• Research paper 10%
• Artifacts (including User Guide) 15%

The course instructor will consider strongly the novelty of the idea (If it has never been done before, you will get lots of credit!), how it addresses the problem at hand, the methodology you employ in doing the research, and your technical skill in implementing the idea.

In small group projects, each person will be graded individually. A good group project is a system consisting of a collection of well defined subsystems. Each subsystem should be the responsibility of one person and be clearly identified as their project. A good criteria for whether you should work in a group is whether the system as a whole is greater than the sum of its parts!

Grading criteria for poster

The poster will be graded based on the following criteria:

• Clear communication of key aspects of solution
• Clear communication of design approaches
• Clear communication of arguments for proposed solution
• Craft quality of the solution

Grading criteria for townhall presentation

The townhall presentation will be judged based on:

• Clarity and organization of the oral presentation
• Relevance and clarity of presentation material (poster and live demo,etc)
• Quality of argument used to justify why the solution is worthy of consideration
• Quality, originality and relevance of design solution

Sample Projects

Note that the following examples are for references purposes. You are urge to use your own creativity and innovation to design the application

• IS428 Year 2012-13 Term 1
• IS428 Year 2013-14 Term 1
• IS428 Year 2014-15 Term 1
• IS428 Year 2015-16 Term 1
• IS428 Year 2016-17 Term 1
• IS428 Year 2017-18 Term 1
• IS428 Year 2018-19 Term 1

Looking for Project Idea and Data?

Singapore

• Data.gov [2]
• Smart Nation Open Data Portal [3]
• Department of Statistics (DoS), Singapore [4]
• Ministry of Manpower (MOM) [5]
• Singapore Tourism Board [6]

Other Source

• Open Challenges at Visualizing Data [7]
• UN Data [8]
• World Bank Open Data [9]
• Open Government [10]
• Kaggle - Datasets [11]