Visual Analytics of User Behavior

1. Visual Analytics of User Behavior • Project Description: Analyze and predict user behavior in a virtual world to inform dynamic modifications to the environment to create a richer virtual experience. • Major Accomplishments: • Identified virtual environmental differences that affect the user’s exploration affinity level • Extended game engine technology with visualization toolset to represent user position, velocity, time, map coverage, interaction events • Utilized deterministic nature of the engine to mine additional data of interest after original play session

2. Visual Analytics of User BehaviorSupport • Complimentary grant support: NSF EAGER (EArly Grants for Exploratory Research): “Identifying and Integrating Creative Patterns of User Behavior and Experience in Virtual Worlds” • Grant description: A new interdisciplinary methodology for both the analysis of user’s experiences in virtual worlds and how this analysis can influence the behavior of the world to produce more effective user experience. This is of particular value to virtual worlds which don’t have a single “winning condition” such as a game, but instead might support a wide variety of interactive behavior, all of which are valid, but can differ widely in simple analysis.

3. Visual Analytics of User BehaviorBackground • Present state of knowledge in virtual world analysis • network analysis (connectivity, load, latency) • econometrics on virtual economies • profiling game play activities • Primarily driven by game companies • during development (Microsoft Labs, Halo series) • for an ongoing MMOG (Blizzard, World of Warcraft) • over a game network service (Steam, Xbox Live)

4. Visual Analytics of User BehaviorBackground • Currently, virtual world design is modified based on anecdotal observation of users during sessions and interviews afterwards. • This won’t be possible with massive multi-user system. • As virtual world becomes more complex, the ability to understand behavior of user and system diminishes. • Multi-user system will need more robust analysis methods. • These methods can modify world behavior so that different interaction types can have successful experiences.

5. Visual Analytics of User BehaviorMethodology • New methods: Cultural Analytics • “the use of computational methods for the analysis of patterns in visual and interactive media.” • Data mining, knowledge exploration, and information visualization as applied to cultural artifacts and experiences such as paintings, cartoons, or virtual worlds. • Logging, visualizing, designing • Record events in the world and telemetry on the user • Record images of user and user’s view • Visualize spatial, temporal, and narrative patterns • Explore mechanisms to dynamically modify the virtual world based on behavior patterns

6. Visual Analytics of User BehaviorLogging • Event logging • Server-side code hooks fire when an event occurs • Events logged as time-stamped “triples” (subject-verb-object) • Object / user interactions (Player1 activates Object5) • World state changes • User telemetry logging • Data is polled from client at set rate (1/sec) and logged on server • User input (trackball direction, velocity) • User avatar position / orientation • User camera position / orientation / type

7. Visual Analytics of User Behavior Exhibition • The Real-Fake Exhibition • [April 1 – May 28, 2011] • California State University, Sacramento • University Library Gallery • Student Population: 27,000 • The Scalable City was featured • Video monitoring system installed • To track physical interactions • Virtual World monitoring system enabled • To track events, world state and archive screen renders

8. Visual Analytics of User Behavior Exhibition • The Real-Fake Exhibition • The Scalable City • Approx. 73 hours of playtime recorded • 528,546 lines of tracking data • 2,290,000 surveillance images (41 GB)

9. Visual Analytics of User BehaviorExhibition • Database of analytics data • Events categorized in triples (Subject – Verb – Object)

10. Visual Analytics of User BehaviorExhibition • Database of analytics data • User data polled and logged for each player every second

11. Visual Analytics of User BehaviorImage Data • Image data • View of User • Video feed captures user interaction with physical interface • Tracks bystanders experiencing but not interacting • Still images archived at one frame / second • User’s view • Screen renders presented to the user are sampled and archived ( one frame / second ) • Long periods of inactivity disable archiving

12. Visual Analytics of User BehaviorImage Data • View of User footage • A dimension of user behavior data typically disregarded • Social influences come into play • Were others waiting during a user’s play session? • Did the user watch someone else interact first? • Did some play sessions have multiple users taking turns?

13. Visual Analytics of User BehaviorImage Data • User’s View (Screen Images) • What was the player seeing in the virtual world? • Visual experience influences behavior

14. Visual Analytics of User Behavior • Image Data • Image Analysis • Color distribution • Object recognition • Feature analysis

15. Visual Analytics of User BehaviorImage Data • User’s View (Screen Images) • Chroma Key Rendering prototype for simplification of object recognition • Requires two simultaneous render modes • Real-time performance not to par for use in exhibition

16. Visual Analytics of User Behavior Exhibition Data • Database of analytics data • 679 unique play sessions identified • Additional data mined after original sessions • Facilitated by deterministic nature of engine • Example: city completion level

17. Visual Analytics of User BehaviorData • Findings from Exhibition • Trackball analysis • Users tended to go left instead of right

18. Visual Analytics of User BehaviorData • Findings from Exhibition • Number of Cities visited by each user • 77 % visited just one city

19. Visual Analytics of User BehaviorData • Findings from Exhibition • Starting city type drives exploration affinity to other cities • Starting in curly road pattern makes users much less likely to travel to other cities Frequency of multi-city exploration per starting city type

20. Visual Analytics of User BehaviorData • Findings from Exhibition (position)

21. Visual Analytics of User BehaviorData • Findings from Exhibition (camera position)

22. Visual Analytics of User Behavior • Visualization • Visualization Toolset • Flash based gui integrated into virtual world • Avatar visual representation controls • Indicators for position, velocity & time data • Lighting / Time of Day controls • Playback controls • Real time data visualizations

23. Visual Analytics of User Behavior • Visualization • Visualization Toolset

24. Visual Analytics of User Behavior • Visualization • Visualization Toolset

25. Visual Analytics of User Behavior • Visualization • Visualization Toolset

26. Visual Analytics of User Behavior • Visualization • Visualization Toolset

27. Visual Analytics of User Behavior • Future Work • Virtual world behavior driven by analysis • Real-time analysis will customize and enhance experience on a per-user basis • Categorize user on the fly • Timid • Speedy • Immersed • Loading based on Navigation prediction • Preload assets based on paths users typically take at certain decision points.

28. Visual Analytics of User Behavior • Future Work • Virtual world behavior driven by analysis • Analytics Camera • Navigation assistance • Based on previous user patterns • Resetting behavior • Reset to environments conducive to more fulfilling experience