April 19, 2023

Data Analysis on Massive Online Game Logs

Dora Cai – NCSA, Univ. of Illinois

Growing Popularity of Online Games

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• 135 million gamers are playing worldwide

• Thousands of game titles have been developed

• Enormous game logs have been generated and collected

• Game logs are unique resource for Social Science studies

• Many researchers are working on game log analysis

The Research Team

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Started in 2007, about 20 members

University of Illinois at Urbana-Champaign

Professor Marshall Scott Poole, post-doctoral scholars and PhD students

Northwestern University

Professor Noshir Contractor, post-doctoral scholars and PhD students

University of Southern California

Professor Dmitri Williams and PhD students

University of Minnesota - Twin Cities

Professor Jaideep Srivastava and PhD students

Project Data Flow

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Internet Players Game Logs UIUC Database

Analysis Software

Gordon Cluster

Research Issues in Game Log Analysis

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Are there social networks behind the scene?

What are the characteristics of the social networks in game play?

Is player’s behavior predictable?

Does player’s behavior reflect his/her personality?

What is the relationship between the virtual world and real world?

What is the impact of game play on player’s personal life?

Does team assembly improve play performance?

Project Achievement

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Project has been funded by NSF, ARI, AFRL, and ARL

More than 40 conference and journal papers have been published

More than 30 graduate students have been trained

8 PhD students worked on this project have graduated

A comprehensive game log database has been constructed

Project has attracted collaborations from many academic institutions and game companies

A spinoff company has been created by two of the PIs

My Involvement in the Project

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Join the project since 2008

Construct and maintain a game log database (4.5TB)

Integrate game logs in 3 languages (English, Chinese and Japanese) from 4 online games (Ever Quest II, Chevalier’s Romance 3, Dragon’s Nest, Eve Online) into one single database

Help researchers effectively use HPC and databases in their research

Work with the research team:

Build the prediction models based on player’s behavior

Design and implement the algorithms for group detection

Visualize the social networks in online games

EnglishEnglish

EnglishEnglish

中文

中文

日本語の

English

English

中文

A New Tool: SocialMapExplorer

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A web-based application for visualizing the social networks of online games

An application implemented using GoogleMap API, HTML, JavaScript

A highly interactive tool: Users can choose analysis variables, aggregation levels, time periods, and location regions

A tool using visual features (color, size, shape, weight and font) to represent various network features

A tool for visualizing data on a real map and tightly combining time and spatial information with other study attributes

A tool capable to process a terabyte-scale dataset with complex data structure

3 modules: NetViewer, GroupDetector, and CorrelationFinder

Work Flow for SocialMapExplorer

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Step 1: Data summarization

Apply data-mining/data-warehouse techniques to construct materialized views on data cubes

Step 2: Geocoding

Match players’ zip-code with an official USA zip-code book and assign latitude/longitude coordinates for each player

Step 3: Data visualization

Visualize data on real maps

Player Zip-Code Latitude Longitude

1234567 15603 -122.26252 37.90194

2345678 44327 -56.77754 23.78321

…… ….. …… ……

Module: NetViewer

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Designed for analyzing network dynamics by visualizing social networks in time series

Trace networking events and make the linkage between involved parties

Able to choose different data sets based on user’s interest

Display networks at different intervals: minute/hour/day

Run in two modes: dynamic and static

AJAX technique was used to automatically reload partial display

NetViewer - Chat Network

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Module: GroupDetector

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Designed to detect groups and visualize group evolution

Scan game logs and identify the trigger events for group reorganization

Able to choose game tasks and time periods

Display single group or multiple groups

Can run in two modes: dynamic and static

Use AJAX technique to automatically reload partial display

GroupDetector - Group evolution in a task

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Module: CorrelationFinder

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Designed to discover the correlation between census data and game play

Visualize census variables as the background colors at the county level, and visualize the players’ behaviors as the foreground marker and links

Reveal hidden correlations by overlapping two-layer graphs

Able to choose analysis variables from census data and game behavior data

Able to select location and regions based on user’s interest

Visualize variables in a quantitative manner

Verify correlation by statistic methods

Is there a correlation between

them?

Is there a correlation between

them?

CorrelationFinder – Overlapping Technique

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Two layers:

Two layers:

Each county of California is filled using gradient colors based on the population density

Player volume (aggregated to the zip-code level) is represented as markers with gradient colors

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CorrelationFinder:Median Age with Conversation Volume

Computation Complexity

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Major computation cost:

Data Summarization

Geocoding

Data Visualization

m – number of rows (R) in game logsn – number of time and location attributes (A)p – number of aggregation levels (L)

m – number of Players(P) in game logsn – number of zip-code in the zip-code book(Z)

x – number of snapshots in time series (T)m – number of edges (E) in drawingn – number of markers (R) in drawingp – number of links (L) in drawing

Data Analysis on Gordon

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Massive computer nodes with rich memory on Gordon speed up the data processing

On standalone sever: With 8 CPUs and 12GB RAM, data summarization and geocoding took over 500 hours

On Gordon: 8 parallel jobs with each using 16 cores, all jobs done with 48 hours

Software stack, especially R, supported on Gordon allows the project to run lengthy and complex data analysis

The system support group and consulting office at SDSC always provide prompt services

We appreciate the effort of the SDSC’s Gordon team