Visual Analytics of User Behavior

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

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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.

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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)

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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.

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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

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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

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• 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

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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)

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Visual Analytics of User Behavior Exhibition

• Database of analytics data– Events categorized in triples (Subject – Verb – Object)

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Subject Verb Object

HousePiece activates City

Player completes GlobeMode

Server deactivates HousePiece

enters Lot

exits Road

joins Simulation

pauses

resets

starts

unpauses

Visual Analytics of User BehaviorExhibition

• Database of analytics data– User data polled and logged for each player every second

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Measure Description

Timestamp Time of recording

Player ID Server’s identification integer for the player

Trackball Input (x,y) movement of the trackball since last poll

Num Input Events Number of unique input events since last poll

Current City The city that the player currently occupies

Player Position World space location of the player

Player Direction Direction the player is facing

Player Activity State Measure of current player activity [Very Active, Active, Inactive]

Camera ID Indicates camera settings , based on activity levels

Camera Position Current world space camera position

Camera Relative Position Position relative to the player’s position

Camera Orientation Indicates where the camera is looking

Visual Analytics of User BehaviorExhibition

• 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

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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?

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• User’s View (Screen Images)– What was the player seeing in the virtual world?– Visual experience influences behavior

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• Image Analysis– Color distribution– Object recognition– Feature analysis

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• 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

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• 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

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Visual Analytics of User Behavior Exhibition Data

• Findings from Exhibition– Trackball analysis

• Users tended to go left instead of right

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Visual Analytics of User BehaviorData

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 160

500

1000

1500

2000

2500

• Findings from Exhibition– Number of Cities visited by each user

• 77 % visited just one city

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1234567812

• 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

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curly grid maze0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Frequency of multi-city exploration per starting city type

• Findings from Exhibition (position)

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• Findings from Exhibition (camera position)

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• 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

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Visual Analytics of User BehaviorVisualization

• Visualization Toolset

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• 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.

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Visual Analytics of User BehaviorFuture 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

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Visual Analytics of User BehaviorFuture Work

Visual Analytics of User Behavior

Documents

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