Relational Data Visualisation Assignment help
Relational Data Visualisation
Assignment Details For this assignment, you are required to identify and develop one (or more) visualisation(s) for relational data sets (such as networks, graphs and trees as presented in lecture notes in week 3 and week 4) using existing tools, software or your own development using available libraries. You might use the sample data sets at the tutorials as well as the provided visualisation techniques. Alternatively, you are encouraged to search and use other visualisation tools and/or datasets in literature. Based the visualisation(s), you can explore to find insight, patterns, ir(regularity) and interesting property from the visualisation.
You are also required to write a report (approximate 1500 words but no limit to) on the following aspects:
- Brief technical details of the used visualisation method(s),
- Discussion on the advantage and disadvantage of the visualisation method(s) in comparison with other methods in literature. Can the visualisation method(s) be used effectively for large relational data sets and why?
- Discussion on the analysis results and findings on the data sets,
- Discussion on other aspects, literature review of related work and your critical thinking on the visualisation(s). Note: images (as figures) are essential and should be included in the report to illustrate the visualisations, results and findings.
Marking criteria for the assignment includes
- Development of visualisations for relational data (50%). You might use existing tools (e.g. tree visualisations, Gephi, Cytoscape, etc.), existing software library (e.g. D3.js) or write your own program in R. The marking will be based on how well the visualisation method presenting the relational data. Interaction should also be included in the visualisation.
- A report on the technical description of the visualisation, analysis results and other aspects (50%)
Deliverables Students must individually complete the visualisation(s) and the report. The report should be typed and submitted online as a Word or PDF file. A high standard of professional English and neat logical structure (including consistent and complete referencing style) is expected. The data and source code (if have) should also be submitted online too.