Final Project

Final Project Requirements

May 4, 2010

in Final Project

Each group will turn in a single zip file containing:

  1. A Visualization
  2. Any Artifacts
  3. The writeup
  4. Presentation materials

Each person will turn in a zip file (seperate from the group one) containing:

  1. A Bibliography
  2. Paper Summaries
  3. A Post-Mortem

Each of these is detailed below. One you have created the zip file, send email to the instructor and TA telling us how big it is, and we’ll figure out the best way for you to deliver it. (do not send us big files by email!)

Everything must be turned in before the beginning of the presentation session. So make the delivery arrangements well before that!

Things to be done by each group

Each group should prepare each of these together. I would prefer documents as PDFs except as noted. Everything should be put together in a single zip file.

A Visualization

Turn in some pictures of what you did, applied to some specific data (this is a requirement in the project description). If you made an interactive tool, you should make some pictures of what it looks like.

Each picture should have an associated “caption” explaining what the picture shows (as in, what the data is, and what the visualization is showing about the data). It might be best to do this by created a web page that includes the image and the caption (but make sure the zip file is self contained, with local links). Or you can put the pictures into a document in your favorite word processor and make a PDF.

Any “artifacts”

This is mainly the program you wrote, along with the data to run it on (if that is feasible). Please make sure that you provide instructions on how to run your program(s). (windows binaries are preferred in addition to the source code, if appropriate. If you have questions on what to turn in, ask.

You should also include pictures that you made (preferably in a curated gallery with captions explaining what they are).

The Final Writeup

As indicated in the project description, your main writeup should be a 4 page (in the format for IEEE Visualization), self-contained, description of your projects. Define the problem and your solution.

Create a writeup: your writeup must be in the format of a Eurovis poster paper IEEE Visualization poster paper and 4 pages, not 2 pages. The writeup must be submitted as PDF document. The format is described here, including Word and LaTeX templates. Note: you do not need to provide the “Descriptors” or “Keywords.”

The formatting is actually a requirement (so you can learn how to deal with formatting requirements for real venues).

Authorship order (which is significant in some academic venues) is not important for this.

In addition to a PDF, please include the “source code” (e.g. latex or word) for the document.

Presentation

Each group will give a 10 minute presentation either in class or in the presentation session.

Please include your presentation materials in the zip file.

Things to be done by each person

Each person should turn in their own version of each of these as they were supposed to be done independently. Please turn in each one as a PDF, and turn in 1 zip file with everything.

Bibliography

You should provide a complete list of everything you read. The citation should be complete (enough of a bibliographic entry that someone will know what it is, can judge the source, and find it). Please include a web link if available (for example a DOI – which is required by many journals now).

Note: you might not have read all of the papers in the bibliography that you list. Please indicate this (maybe denote them “read” “skimmed” or “not looked at”).

Reading Summaries

You should write a brief summary of the papers that were significant. This won’t be everything in the Bibliography. (but should be at least 5 papers per person, as discussed in the project assignment)

You should summaries the content of the paper (what is the main ideas), as well as what you took away from it (both in general, as well as for the project).

These need not be long – 2-3 paragraphs can do the job well (if you really understand the paper, you can see what the key points are)

Post-Mortem

A “post-mortem” is a chance to reflect on the project after its over. In terms of pedagogy, this is a critical piece of making sure you learn something form the project.

Briefly describe the process – what did you do. This may be different than what is reflected in the final writeup (which is focused on the outcomes).

What did you learn from the process?

What would you have done differently? What would you do the same if you were to do it over?

Self-Evaluation: what grade would you give yourself (and why)?

Group Evaluation: how well did your team work out? How would you evaluate your teammates? (this will not be shared with them)

Project Plan (JW)

April 22, 2010

in Final Project,Student Posts

Description

The project involves comparing climate observations from across North America to downscaled climate predictions for Wisconsin using 15 different climate models.  There are two time spans, 2046-2065 and 2081-2100, for each set of predictions.  The visualization will also take into consideration three different carbon emission scenarios.  The state of Wisconsin will be divided into cells (.1 degree latitude by .1 degree longitude) with an analog displayed somewhere within North America.

Desired outcomes

An interactive map and accompanying geo-visual analytic tool will be produced.  The visualizations will be designed for display online, with the data residing in a database accessible though standard web requests.

Reading List
Shneiderman, B. 1994. Dynamic Queries for Visual Information Seeking. IEEE Softw. 11, 6 (Nov. 1994), 70-77.

Eick, S. G. 1994. Data visualization sliders. In Proceedings of the 7th Annual ACM Symposium on User interface Software and Technology (Marina del Rey, California, United States, November 02 – 04, 1994). UIST ’94. ACM, New York, NY, 119-120.

Nakhimovsky, Y., Miller, A. T., Dimopoulos, T., and Siliski, M. 2010. Behind the scenes of google maps navigation: enabling actionable user feedback at scale. In Proceedings of the 28th of the international Conference Extended Abstracts on Human Factors in Computing Systems (Atlanta, Georgia, USA, April 10 – 15, 2010). CHI EA ’10. ACM, New York, NY, 3763-3768.

Timetable
Week 1: Gather all of the data from the Jack Williams lab and create a relational database for the different carbon emission scenarios, climate models and time spans.
Week 2: Create an interactive map capable of defining each cell within Wisconsin.  The map should have the same basic navigational capabilities as Google maps, Yahoo or Bing.
Week 3: Begin integrating the climate data with the maps of Wisconsin and North America.  Every cell on either map should have the capability of displaying the relevant data for a given location.
Week 4: Build a geo-visual analytic tool that shows the original Wisconsin data point and the distance to all of the related points for North America.  I’ll have a better idea what this will look like after working with the data.

Description
The final visualization will contain two maps side-by-side.  The map on the left will allow the user to navigate the state of Wisconsin in order to select a cell for comparison.  Once a cell has been selected, the map on the right will show all of the results in relation to the Wisconsin cell.  Lines will be draw between the original cell and all of the results, with the ability to mouse-over each line to display the distance and bearing.  Drop down menus will allow the user to select different scenarios, models and time spans for unique database queries.  I’ll add the ability to switch base tiles so that a user can evaluate the landscape using aerial photos, road networks or a hybrid of both.  A tool for analyzing the relationship between the original cell and each result will reside below the set of maps.

Description

The project involves comparing climate observations from across North America to downscaled climate predictions for Wisconsin using 15 different climate models. There are two time spans, 2046-2065 and 2081-2100, for each set of predictions. The visualization will also take into consideration three different carbon emission scenarios. The state of Wisconsin will be divided into cells (.1 degree latitude by .1 degree longitude) with an analog displayed somewhere within North America

Desired outcomes

An interactive map and accompanying geo-visual analytic tool will be produced. The visualizations will be designed for display online, with the data residing in a database accessible though standard web requests.

Reading List

Shneiderman, B. 1994. Dynamic Queries for Visual Information Seeking. IEEE Softw. 11, 6 (Nov. 1994), 70-77.

Eick, S. G. 1994. Data visualization sliders. In Proceedings of the 7th Annual ACM Symposium on User interface Software and Technology (Marina del Rey, California, United States, November 02 – 04, 1994). UIST ’94. ACM, New York, NY, 119-120.

Nakhimovsky, Y., Miller, A. T., Dimopoulos, T., and Siliski, M. 2010. Behind the scenes of google maps navigation: enabling actionable user feedback at scale. In Proceedings of the 28th of the international Conference Extended Abstracts on Human Factors in Computing Systems (Atlanta, Georgia, USA, April 10 – 15, 2010). CHI EA ’10. ACM, New York, NY, 3763-3768.

Timetable

Week 1: Gather all of the data from the Jack Williams lab and create a relational database for the different carbon emission scenarios, climate models and time spans.

Week 2: Create an interactive map capable of defining each cell within Wisconsin. The map should have the same basic navigational capabilities as Google maps, Yahoo or Bing.

Week 3: Begin integrating the climate data with the maps of Wisconsin and North America. Every cell on either map should have the capability of displaying the relevant data for a given location.

Week 4: Build a geo-visual analytic tool that shows the original Wisconsin data point and the distance to all of the related points for North America. I’ll have a better idea what this will look like after working with the data.

Description

The final visualization will contain two maps side-by-side. The map on the left will allow the user to navigate the state of Wisconsin in order to select a cell for comparison. Once a cell has been selected, the map on the right will show all of the results in relation to the Wisconsin cell. Lines will be draw between the original cell and all of the results, with the ability to mouse-over each line to display the distance and bearing. Drop down menus will allow the user to select different scenarios, models and time spans for unique database queries. I’ll add the ability to switch base tiles so that a user can evaluate the landscape using aerial photos, road networks or a hybrid of both. A tool for analyzing the relationship between the original cell and each result will reside below the set of maps.

Project Plan

April 17, 2010

in Final Project,Student Posts

Topic:

I want to explore the use of textons, textures, color blending, and their combination as a means to clearly show relationships in a general Euler diagram.

Desired outcomes:

I want to develop a technique, or a set of techniques,  that can be used to style a Euler diagram so that the relationship of any subregion to the rest of the diagram is obvious and require little cognitive resources to understand. A part of this goal will be to create visually pleasing illustrations, and things such as color harmonies will be taken into account.

Initial reading list:

Cohen-Or, D., Sorkine, O., Gal, R., Leyvand, T., and Xu, Y. 2006. Color harmonization. ACM Trans. Graph. 25, 3 (Jul. 2006), 624-630. DOI= http://doi.acm.org/10.1145/1141911.1141933

Hagh-Shenas, H., Interrante, V., Healey, C., and Kim, S. 2006. Weaving versus blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color. In Proceedings of the 3rd Symposium on Applied Perception in Graphics and Visualization (Boston, Massachusetts, July 28 – 29, 2006). APGV ’06, vol. 153. ACM, New York, NY, 164-164. DOI= http://doi.acm.org/10.1145/1140491.1140541

Papers on texture synthesis from: http://graphics.cs.cmu.edu/people/efros/research/synthesis.html

Time Table:

  • Week 1
    • Compile and begin reading the reading list
    • Visualize color harmonies in several color spaces
  • Week 2
    • Finish reading list
    • Complete harmony visualization
    • Formulate optimization framework for finding colors
    • Decide on an approach for texture synthesis
  • Week 3
    • Implement all approaches, including some combinations
    • perform Initial testing
    • Begin write up
  • Week 4
    • Finish write up
    • Prepare presentation
    • Additional testing

End result visualization:

Given a Euler diagram, the aim will be to color it using the approaches described in such a way that it can be understood easily. Something like this, but in a way that scales to more complicated relationships and regions

Project Plan (LW, DA)

April 16, 2010

in Final Project,Student Posts

Topic:

The topic that we plan to explore in this project is how to compare multiple network visualizations. While techniques to address this issue have a wide variety of potential applications, there is not a substantial amount of existing literature directly addressing this problem. Thus far, we have a few potential ideas for implementation, but plan to elaborate on these and develop others over the course of the project.

Desired Outcomes:

The outcome of this project will be the implementation of multiple proposed solutions to the network comparison problem. Because of time limitations and lack of existing solutions for guidance, we will likely simplify the problem set to series of small networks. The details of the implementation, such as language and development package, have yet to be decided.

From this project, we hope to gain a better understanding of graph embeddings and techniques for improving readability as it applies to graph comparison. As we’ve seen, graph and network style visualizations are very common in practice. We hope to better understand how techniques that we’ve covered in class can help to create better means of comparison.

Reading List:

Graham and Kennedy. Exploring Multiple Trees through DAG Representations. IEEE InfoVis, 2007.

Frishman, Y. & Tal, A. Online Dynamic Graph Drawing IEEE Transactions on Visualization and Computer Graphics, 2008.

Frishman, Y. & Tal, A. Dynamic Drawing of Clustered Graphs Proc. IEEE Symposium on Information Visualization, 2004.

C. Collberg, S. Kobourov, J. Nagra, J. Pitts, and K.Wampler. A system for graph-based visualization of the evolution of software. In SoftVis ’03: Proceedings of the 2003 ACM symposium on Software visualization, 2003.

Ogata et al. A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. Oxford University Press, 2000.

Hoebe and Bosma. Visualizing multiple network perspectives. ACM, 2004.

Major Layout Algorithms. http://www.yworks.com/products/yfiles/doc/developers-guide/major_layouters.html

Erten et al. Simultaneous Graph Drawing: Layout Algorithms and Visualization Schemes.

Time Table:

    • Week 1:
      • Develop Project Plan
      • Compile Initial Reading List
      • Begin readings
    • Week 2:
      • Complete readings
      • Brainstorm potential solutions
      • Select most promising solutions
      • Select implementation language/package
      • Create initial data set
    • Week 3:
      • Implement solutions
      • Test over additional data sets
    • Week 4:
      • Prepare report and presentation

Output & Data Sources:

We hope to implement several solutions to the network comparison problem. The data sources used for this visualization will likely be small, derived data sets. Hopefully there will be potential to apply our solutions to real-world data sets. However, since the purpose of this project is for exploration of general technique, integrating real-world data visualization is not currently part of the project plan.

Project Plan (Shuang Huang)

April 16, 2010

in Final Project

Description:

My project is to visualize the hidden Markov chain structure of data set from educational study. The variables of the model are discrete, and the parameters are estimated via Bayesian model.

Data was provided by my friend working in Educational Test Service, along with some old data sets. They provide the statistical model so what I need is to use their models instead of analyzing data by myself.

Goal:

The result is for statisticians in educational field, so I hope my work will be able to understand by people in this domain. The visualization will describe the HM chain, parameters, important transit states and probabilities.

Initial reading:

1. JIAN ZHANG, MARKOV CHAIN AND HIDDEN MARKOV MODEL.

This reading is to understand the basic idea of fundamental HMM.

2. Gerard Rambally, A Hybrid Visualization Hidden Markov Model Approach to Identifying CG-Islands in DNA Sequences.

This reading is to learn how to explain HMM to people in other domain, like biology and genetics.

3. Valeria De Fonzo, Filippo Aluffi-Pentini and Valerio Paris, Hidden Markov Models in Bioinformatics, Current Bioinformatics, 2007, 2, 49-61

This reading provides a good example to visualize HMM with emphasizing probabilities in the model.

4. Some coding skill. I think it is important for me.

Schedule:

Week 1 (04/16): Try to implement the reference related to the data and model. Read the papers to understand HMM.

Week 2 (04/23): Provide a visualization of the data, which at least has the basic features of HMM. If possible, I would get some feedback about what to do to improve it.

Week 3 (04/30): I hope to get some more visualization derived from the initial one and compare them.

Week 4 (05/06): Finish the implementation and make a function/package/toolbox that could be used for later analysis. Write a report.

Jim’s project plan

April 16, 2010

in Final Project

Description:

This project will investigate the mothods of producing stereo images in different areas of media with a focus on scientific data.  It will also investigate the relative value of stereo when compared to other depth cues such as shading and occlusion.  The project will culminate with a simple application that shows how stereo compares to other depth cues when both are present.

As a possible addition, I would like to have a number of different people try my software and comment on the relative usefulness of stereo.

Desired Outcomes:

I wish to produce several things including: a sumary of what is requrired to be produced to create a “good” stereo pair, a program that compares stereo different depth cues to stereo, and a list of depth cues with a ranking comparing it to the stereo depth queue (i.e, better or worse).

Initial Readings:

I have three initial readings that will hopefully produce leads on other literature:

  1. Michael F. Deering. Making Virtual Reality more Real, Experience with the Virtual Portal. Proc. Graphics Interface ’93.
  2. Michael F. Deering. The Limits of Human Vision. In 2nd International Immersive Projection Technology Workshop, 1998.
  3. Geoffrey S. Hubona. The relative contributions of stereo, lighting, and background scenes in promoting 3D depth visualization. ACM Transactions on Computer-Human Interaction, 1999.

Timetable:

By the following dates I would like the following to have occured

April Friday 23 – Read initial papers and any leads, have summaries and have initial code in place to produce stereo images on csl machines

April Friday 30 – Have stereo/depth cue comparison program done and have initial measurements from different people.

Example Visualizations:

I will produce hard copies of the images produced by my software.  In most cases this will be a simple image constrasting two depth cues.

Project Plan (ET)

April 16, 2010

in Final Project

I will be trying to create a tool to visualize Electroencephalography (EEG) data.  The specific experiment is measuring brain response to repeated auditory stimulation interspersed with periodic rest states.  Eventually, the brain response gets into phase with the auditory stimulation (a series of clicks).  However, what is difficult to show is the time it takes the brain to get into phase with the sounds and whether that varies over time.
I would like to produce a visualization which demonstrates the differences in time it takes to get into phase, which itself will vary over time.  I also want to produce a visualization which can compare between multiple patients.
The initial readings are:

Lin, Jessica et al. Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases. Information Visualization, 2005, 4(2), 61-82.

Marc Weber, Marc Alexa, and Wolfgang Muller.  Visualizing Time-Series on Spirals. Proceedings of the IEEE Symposium on Information Visualization, 2001, 7.
Cutting J E, 2002, “Representing motion in a static image: constraints and parallels in art, science, and popular culture” Perception 31(10) 1165 – 1193.
I am currently looking for more readings on representing large time series.
By April 23, I hope to have a basic structure for reading in the information and displaying the waves that represent the data.  I will also have scraped the data for the relevant information needed for my visualizations (eg peaks).
By April 30,  I hope to have an a few initial visualizations completed as well as an analysis on how well they seem to work.
By May 6, I should have a useful visualization and will have developed a way to compare between multiple patients data.

The first visualization I will make is the basic (somewhat obvious) one of wave forms.  I hope to then create one which will isolate the peaks of the waves, and draw connections between them.  It make be worth looking ,brat the slope between these connections.  Since the stimulation is periodic in nature, I am also considering testing the spiral method as described by Marc et al.

My data will be provided by the University of Pennsylvania Brain Behavior Laboratory.  It is EEG data obtained while a subject undergoes 40 seconds of repeated half second auditory stimulation (a series of clicks).  Which is interspersed with longer rest periods.  There will be both raw data and averaged data, from multiple electrodes.  This data will also be from multiple patients.

Project Plan (He,Khan,Liu)

April 14, 2010

in Final Project

Project: VAST 2010 Mini Challenge 2

David He, Faisal Khan, Ye Liu

Introduction

For this challenge, we have been given hospital admittance and death records for cities involved in the major epidemic outbreak. There are two main tasks. One is to characterize the spread of disease by considering attributes like symptoms of the disease, mortality rates, temporal patterns of the onset, peak and recovery of the disease. Secondly, we are asked to compare the outbreaks across cities as well.

Initial Readings

We will try to add more readings here before Friday. As for now we know that we will be going through this book –  “Illuminating the Path”. It is freely available as an e-book.

Milestones or Weekly Goals

Week 1: This week our main goal is to get familiarity with the dataset. We are planning to write small tools for assisting in understanding things like mortality rate over time, leading syndromes for mortality. For this prep-processing phase, we need to do some text processing because of the abbreviations and aliases used for name of the syndromes. Also we will be making simple plots to analyze above-mentioned variables. For this purpose, we have imported all the data in sqlite database and soon going to start running queries and do some basic charting to analyze the data. We are using python with gnuplot for this purpose.

Additionally, we will do some handsome amount of readings. Mostly we are targeting reading parts of the book on  “Illuminating the Path” to get some understanding of the idea behind visual paralytics. We will try to find some papers on epidemiology that are relevant to our project.

Week 2: During this week we will focus on coming up with multiple visual designs that answers the first question of the challenge i.e. characterize the spread of the disease. We are hoping that we will not have to do a lot of implementations for evaluating these design. Perhaps, use of some mock-up designs or static images might help us narrow down choices. We plan to begin by getting concrete understanding of how domain experts study spread of diseases. We will look into existing visualization tools and some literature dealing with the understanding of the spread of disease. If we get access to domain expert, we will seek his/her help on this topic as well.

We expect to finalize the development environment for our project by this week.

Week 3: By this week we are hoping to finalize a design for visualization the spread of the disease. Among different other criteria that we will consider for finalizing  the design, one important one will be the suitability/extensibility of this design to answer the second part of the challenge i.e. ability to compare disease spread across different cities. We will spent most of the time on implementations and remaining on doing some readings on the evaluation of visualizations.

Week 4: Finish implementing the visualization. Begin evaluating the visualization.  Write the report and present.

Individual Responsibilities

All three of us will be involved in design of visualization and determining whether the design resembles expectation.  We are planning to meet several times during next coming days. We expect to use these joint session for helping each other decide on readings and implementation, brainstorming ideas and planning next actions. David and Faisal will be doing most of the implementation. They will try to divide it equally among them. Ye (Alex) will be able to read their code and use any of the tool (custom built or off the shelf) for experimentation and analysis. He will do more readings for equal contribution.

Out Expectations

Applying the methods learned in the class to solve a practical problem.

Being able to answer the questions posed in the challenge.

Understanding of the process involved in the design of visualization and the evaluation of it.

Knowing how to handle large data sets in visualizations.

General understanding of opportunities, challenges involve in the field of visual analytic.

Hopefully interacting with some domain expert (epidemiologist) to gain insight into how the epidemics are studied.

Project outline: team CSV, NK, JYM

April 14, 2010

in Final Project

Non-linear zooming and Clique highlighting in Network Visualization

Team: Chaman, Nakho, Jeeyoung

Overview

Our group project aims to build an improved network diagram visualization which includes new effective measures for removing data cluttering and highlighting complete subgraphs(“cliques”). Discussing the many challenges in network graphs, we decided that the most basic and concrete problem to improve on will be how to make pattern finding in large datasets easier. Also, we wanted to handle the issue of cliques, which has been widely regarded to provide qualitatively distinct communication patterns.

To remove data cluttering we will explore the possibilities of non-linear zooming, while for visualization of cliques we will focus on how to assign effective visual cues to the subgraphs to make them stand out in the large network graph.

We will be using two distinct network data, one from the social science domain and the other from natural sciences. The first dataset is the coeditor network of the Wikipedia. Wikipedia is an popular open Internet-based collaborative platform to compile all fields of knowledge in a encylopedia format, currently hoding more than 3 million articles. Anyone can write and anyone can edit what anybody else has written. By examining on a large scale which two or more users have written and edited a common article together, we can see how specific knowledge production networks emerge in an open structure.

The second dataset is the protein interaction network of yeasts. Protein-protein interaction reveals functional control of target proteins as well as molecular complexes. This protein-protein interaction data can be in the form of binary interaction or group of interaction. Also, those data can be from several different experiment methods. First, we visualize those interaction information from several methodologies in an efficient way. We cliques overlapping links to have high-confidence interactions. Second, we visually integrate protein-protein interaction with known pathway or genetic interaction. Then, we can investigate the relationship between protein-protein interaction, pathway, and genetic interaction.

Our team is composed of members of different academic backgrounds, which will provide a significant merit if the subtasks are properly allocated. Chaman will be focusing on algorithm implementation, Nakho will provide the Wikipedia data and work on design aspects, and Jeeyoung will provide the protein data and process the raw material into network data. Other subtasks including the literature review will be done collaboratively.

Project schedule(tentative)

Week 1 (~Apr/16) : Planning and compiling the initial literature
Week 2 (~Apr/23) : Literature review, preprocessing of data, analyzing algorithms of existing software (e.g. H3viewer, HypViewer, LGL code), Progress report #1
Week 3 (~Apr/30): initial implementation of improved visualization tools, Progress report #2
Week 4 (~May/6 11am): finalizing tools, presentation, final report

Literature to review (tentative)

– Frishman, Y. & Tal, A. Online Dynamic Graph Drawing IEEE Transactions on Visualization and Computer Graphics, 2008, 14, 727-740
– Frishman’s thesis
– Frishman, Y. & Tal, A. Multi-Level Graph Layout on the GPU IEEE Transactions on Visualization and Computer Graphics, 2007, 13, 1310-1319
– Frishman, Y. & Tal, A. Dynamic Drawing of Clustered Graphs Proc. IEEE Symposium on Information Visualization INFOVIS 2004, 2004, 191-198
– deMoll, S. B. & McFarland, D. A. The Art and Science of Dynamic Network Visualization Journal of Social Structure, 2006, 7
– C. Collberg, S. Kobourov, J. Nagra, J. Pitts, and K.Wampler. A system for graph-based visualization
of the evolution of software. In SoftVis ’03: Proceedings of the 2003 ACM symposium on Software
visualization, pages 77–ff, 2003.
– M. Freire and P. Rodriguez. Preserving the mental map in interactive graph interfaces. In AVI ’06:
Proceedings of the working conference on Advanced visual interfaces, pages 270–273, New York,
NY, USA, 2006. ACM.
– Michael Kaufmann & Dorothea Wagner. Graph Drawing: Methods and Models. 2005. (selected chapters)
– http://www.leader-values.com/Content/detail.asp?ContentDetailID=975 social groups visualization which focuses entirely on design techniques
– http://www.ibluemojo.com/school/clique_algorithm.html algorithms to calculate cliques efficiently
– http://portal.acm.org/citation.cfm?id=860151 algorithms to calculate cliques efficiently
– http://www.cytoscape.org/ Visualization tool used in biological data
– “A survey of visualization tools for biological network analysis” by Pavlopoulos et al. (2008)
– “Visualization of omics data for systems biology” by Gehlenborg et al. (2010)

Project Plans

April 13, 2010

in Final Project

By Friday, April 16th, you need to have posted a project plan. The original idea was that you should post one earlier and we would iterate over it to refine it. I hope you will still do that – when you have something together, post it and send the instructor and TA email and we’ll get you some feedback. The goal is to find things that are good topics, but also where you can do something meaningful in the short time.

Each proposal should have:

  • A description of the topic
  • A description of the desired outcomes: what do you expect to produce from the project (a program, an experiment, a document, etc)
  • An initial reading list: what are the first few things you plan to read on the subject. If you need help in identifying relevant readings, ask for help.
  • A timetable: what do you expect to have achieved by the milestones
  • A description of the example visualizations you will make (remember, even if you write a survey or build software, you must actually do visualization on some data). Please tell us about your data sources.

In general: ask for help. Talk to us as much as possible. We really want to help guide you towards doing something cool and interesting.

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