1. User Characteristics & Application design

2. 2 Introduction The starting point of this course: –Users want to understand information about something to make an informed decision So far we learnt techniques that act as means to support users in comprehending information –Data mining (DM) and –Information visualization (InfoVis) techniques Real world applications require integration of these two technologies This is one of the grand challenges in informatics –No easy and readymade solutions

3. 3 Integrating Data Mining and InfoVis High level architecture Data Mining (DM) –Compute patterns or models (in general abstractions) from raw input data Information Visualization (infovis) –Present the relevant abstractions (patterns or models) in a form suitable to the end user –Support user interaction Integrating Data Mining and InfoVis is the main goal of this course Two Options for integration –Option 1 - Loose Coupling –Option 2 – Theory Driven Data Mining Information Visualization End-User Interaction Input Data

4. 4 Loose Coupling Separate libraries of data mining and infovis are offered to the user User is given freedom in exploiting the available methods to understand data Certain constraints may be defined in linking a specific data mining method with a specific infovis method Already available in many existing tools –In Excel and Weka to lesser extent –R, SPSS and other statistical packages Unlimited growth of the libraries –Because there is no notion of what is required Users may use only small portions of the tools –Using sledge hammer for domestic work Suitable only to expert users –A few experts gain monopoly over critical data owned by all

5. 5 Theory driven A general Human Computer Interaction (HCI) theory is required that focuses on –Systematic linking of application design to users, their tasks and task contexts –Taking into account human perception, comprehension and reasoning of information Visual Analytics (VA) is a new discipline that aims to develop such a theory –VA currently focuses on grand applications intelligence analysis genomic data analysis Simpler versions of VA (?) should be developed for simpler applications –E.g. Scuba dive computer HCE, TimeSearcher and GIS studied in this course integrate DM and InfoVis

6. 6 HCE and TimeSearcher HCE incorporates sense of statistical analysis into an InfoVis tool –GRID (Graphics, ranking and interaction for discovery) principle is the simple theory here –(refer to lecture 7) TimeSearcher adds a InfoVis driven front end to time series similarity matching –Visual query tools such as Timeboxes form the simple theory here Both tools are general purpose How to design simple applications in specific domains with –Real users and –Real tasks

7. 7 HCI Approach to user characterization Users vary along several dimensions –Age (e.g children vs adults) –Personality (e.g. extrovert vs introvert) –Physical disabilities (e.g visual impairments) –Skills (e.g. expert vs novice) –Etc Identify user groups among the complete set of users –Subsets of users with similar characteristics Identify the tasks (goals) of user groups Use this information to drive system design

8. 8 Configurable Systems Designing systems for different user groups comes under the study of ‘Accessibility’ This is an important topic in itself –Unix Vs Windows for sighted Vs visually impaired users The main principle behind improving accessibility is to allow system configuration –A configurable system is an accessible system We follow this principle in application design –But what features should be available for configuration?

9. 9 Implications of user characterization System design in our case involves mainly designing two components –Data mining –Visualization Designing data mining component involves –Collecting the right data (both attributes and instances) –Selecting an appropriate data mining task –Selecting an appropriate data mining method to achieve the task representation language search method pruning method

10. 10 Implications of user characterization (2) Role of user tasks in designing data mining component –User tasks (goals) help collecting the right data E.g. For judging the safety of a dive, you need data about dive depth, duration and rapid ascents –User tasks determine the appropriate data mining task(s) E.g. Cluster together dives with similar characteristics Applications usually require multiple data mining methods –Because performance of data mining methods varies widely User group characteristics determine the level of configurability offered to the users –E.g. expert Vs novice

11. 11 Implications of user characterization (3) Visualization techniques present information using a suitable visualization What is a suitable visualization? –Visualization is suitable if it enables users (with their characteristics) to understand the presented information –E.g. a learner scuba diver with poor graph reading skills might need visualizations that clearly mark dive depth and bottom time Design of visualization involves –Choosing a visualization technique –Mapping data features to graphical features The visualization technique used can vary with user characteristics –E.g. a doctor inspecting scuba dive data may like to view tissue saturation values and model predicted micro-bubble data

12. 12 Implications of user characterization (4) The mapping scheme used in the visualization can vary with user characteristics –E.g. for a user with red-green colour blindness to avoid using red for marking rapid ascent patterns on a green dive profile line graph User tasks too have some influence on design of visualization –E.g. a researcher on diving safety requires visualizations that are lot more technical than a regular scuba diver

13. 13 Practical problem with the HCI Approach Acquiring knowledge of user characteristics and user tasks is not easy HCI recommends two approaches –explicit characterization – e.g. asking users directly for user characteristics But users do not always know the required information –Implicit characterization – e.g. start with no explicit user information (cold start) but infer user characteristics from observable user behaviour But user behaviour is not always rational

14. 14 Our Approach Experts know the implications of user groups and their tasks One practical solution is to allow domain experts who regularly deal with different user groups and tasks to configure the system for different users –E.g, a weather forecaster may know how to analyse and present weather forecast information for more technically oriented oilrig staff

15. 15 ScubaText ScubaText project analyses scuba dive computer data and presents the results of analysis –Graphically – annotated graph –Textually – summary of safety related information It is assumed that learner divers may find the textual descriptions and their links to graphical displays useful for judging the safety of a dive –User group – learner divers –User task – judging the safety of a dive Based on user characterization (learner diver) textual descriptions are included in the presentation Real user evaluation showed that the simple user model did not work!

16. 16 Text+Annotated Graphics (D) Risky dive with some minor problems. Because your bottom time of 12.0min exceeds no-stop limit by 4.0min this dive is risky. But you performed the ascent well. Your buoyancy control in the bottom zone was poor as indicated by ‘saw tooth’ patterns marked ‘A’ on the depth-time profile.

17. 17 Revised Text One of the subjects revised the output text as follows: Potentially risky dive with some minor problems. The bottom time of 12.0min exceeds no-stop limit by 4.0min requiring mandatory decompression stops. The ascent was at a constant rate within the recommended rate. The saw tooth patterns marked ‘A’ on the depth-time profile should be avoided if possible as this increases the chance of developing DCI even within the recommended decompression limits. The re-descent from 5m to 10m in the later stages of the dive should also be avoided for the same reason as saw-tooth profiles. Revisions mostly aimed at neutralising the emotional content –But doctors who regularly treat divers with DCI prefer text with emotional content because of the direct impact it can have

18. 18 Discussion To communicate the safety message effectively –Good understanding of user personality required In the department we work on Affective NLG –Generating emotionally appropriate text Elsewhere in NLG, emotional issues such as politeness are explored