CS5561 Data Interpretation and Visualization

1. CS5561 Data Interpretation and Visualization Yaji Sripada Dept. of Computing Science, University of Aberdeen

2. Time table • Lectures • 2 lectures on Wednesdays in Meston 311 • 9:30 -10:30 • 11:00 -12:00 • No lectures in Week 8 • Practicals • 1 two hour practical on Wednesdays in Meston 311 • 14:00-16:00 Dept. of Computing Science, University of Aberdeen

3. Assessment • Course is worth 10 credits • Two components • 25% continuous assessment • 75% end of term exam • Continuous assessment • Issued in Week 6/7 • Due on the Friday of Week 8 Dept. of Computing Science, University of Aberdeen

4. Lectures Discuss topics including Data analysis Time series Spatial data Information Visualization (InfoVis) Case Studies Practicals 7 weeks Using some existing software Developing your own software (Java) 8th week assessment Course Organization Dept. of Computing Science, University of Aberdeen

5. Reading • Mostly lecture notes and some research papers • I will provide all the required reading material Dept. of Computing Science, University of Aberdeen

6. Introduction Dept. of Computing Science, University of Aberdeen

7. Introduction • Humans have access to large volumes of data in many domains • Scientific • Complete sequence data from Human Genome Project • of 3 billion DNA units • Medical • Physiological data • 10s of parameters such as blood pressure and heart rate measured every second • Engineering • 100s of sensors on a gas turbine taking measurements every second • And many more Dept. of Computing Science, University of Aberdeen

8. Varying purpose/task • Different people use data for different purposes/tasks • For example, physiological data is used by • Medical staff on the ward to monitor the patient • Medical researchers for scientific explorations • Medical admin staff to store/retrieve them Dept. of Computing Science, University of Aberdeen

9. Not all humans are equal in using the available data 1 in 4 adults in the UK has poor numerical skills 1 in 7 people in the UK suffers from some form of physical disability (such as visual impairment) Many of us just don’t have the time to use all the data at our disposal Data from our credit card bills and utility bills Many of us don’t have the required domain knowledge to interpret the data Data from medical lab tests such as blood tests Varying abilities/disabilities Dept. of Computing Science, University of Aberdeen

10. Two common goals • Understanding data • All users in all application domains want to make sense of their data • Once they understand their data, they can use their understanding to achieve their individual and domain specific tasks • Communicating to others insights into data gained from understanding Dept. of Computing Science, University of Aberdeen

11. Example Scenario • Main Task: Imagine you want to buy a digital camera • Your possible subtasks: • You first collect data about all the available cameras • Data attributes include megapixel ratings, zoom ratings, price etc • You analyse the data to gain insight into the world (domain) of digital cameras • You use this understanding to guide the purchasing decision • You may cycle through the above steps 1 and 2 several times to improve the quality of step 3 • Also you want to communicate your knowledge of digital cameras to others Dept. of Computing Science, University of Aberdeen

12. Computers help humans understand data • Two views of computer assisted data comprehension • Data Analysis View • Machines can automatically (or semi-automatically) extract meaningful information from heaps of raw data • CS5565 Data Mining takes this view • Given the data about all digital cameras, a clever software system makes the purchasing decision • Information Visualization (InfoVis) View • Humans themselves can make sense of data if data are presented appropriately • Data about all digital cameras are presented (using a combination of graphics and text) by a software system that enhances human understanding of the data • Which then leads to the purchasing decision Dept. of Computing Science, University of Aberdeen

13. This course combines data analysis and infovis views • We exploit strengths of both these views • We build systems that perform both automatic data analysis • And also present information to users to enhance understanding • Advantages • Humans are part of the data comprehension loop • Human perception and cognition capabilities are utilised • Humans can exploit the superior computer power to sift through large volumes of raw data • Disadvantages • End user diversity is not fully appreciated • Users need the time and skill to use these combined systems • Not all users can use visual displays (e.g. visually impaired users) Dept. of Computing Science, University of Aberdeen

14. Accessibility • We make some enhancements to the combination of data analysis + InfoVis • to make up for the disadvantages • Our systems have separate interfaces for different user groups • e.g. experts use interactive visual interfaces to understand data • while general public use textual+simple graphical interfaces with limited interaction • InfoVis =Text+Graphics • Textual descriptions can be linked to visual displays in InfoVis Dept. of Computing Science, University of Aberdeen

15. Select Examples • Data Analysis • R • InfoVis • Newsmap • Touchgraph • ManyEyes • CountryScape • Piccolo infovis library • Data Analysis+InfoVis • RGGobi • …. Dept. of Computing Science, University of Aberdeen

16. System building • Two approaches • Exploit existing libraries • E.g. RGGobi – uses R for data analysis and GGobi for infovis • R functionality can be accessed from Java using JRI class library • Piccolo is a Java class library for writing zoomable interfaces • Develop systems from scratch • Develop both data analysis and infovis functionality in Java • In this course • You learn to use some existing resources such as R • Develop simple systems from scratch in Java Dept. of Computing Science, University of Aberdeen

17. System Building Life cycle • Several Iterations of the following phases • Knowledge Acquisition (requirements collection and analysis) • System design • Implementation • Evaluation • Differs from the normal software development life cycle • Poorly understood requirements – not many examples to learn from • System design ideas still under research – linking data analysis and visualization • Evaluation ideas too still under research Dept. of Computing Science, University of Aberdeen

18. Related Projects within the department • Many projects develop related technology • Projects • SumTime – Summarising Time Series Data • RoadSafe – Automatically generating advisory text for road maintenance vehicle routing – new project • BabyTalk – Generating textual summaries of clinical temporal data – new project • ScubaText – Generating textual reports of Scuba dive computer data • Atlas.txt – Generating textual reports of Census data for visually impaired people Dept. of Computing Science, University of Aberdeen

19. Lectures three groups Group 1 Data Analysis Time series data Spatial data Group 2 Information Visualization Time series data Spatial data Group 3 Real world applications Case Studies Lectures will be ordered to suit the practicals Practicals Week1 – Exploratory Data Analysis Week2 – Time Series Exploration Week3 – Time Series Analysis Week4 – Time Series Visualization Week5 – GIS Week6 – Spatial Data Analysis Week7 – HCE Week8 – Spatiotemporal data analysis and visualization Detailed Course Organization Dept. of Computing Science, University of Aberdeen

20. Summary • All modern organizations • possess large volumes of data and • Users want to understand these data • You learn technologies to • Analyse and interpret large data sets by adapting data analysis techniques developed in other fields • present relevant information to different users with different tasks and abilities • Exploit these technologies to build systems that help users understand data in specific domains Dept. of Computing Science, University of Aberdeen