Managing and Mining Spatio -Temporal Data in Massive Multiplayer Online Games

1. Managing and MiningSpatio-Temporal Data in Massive Multiplayer Online Games Matthias Schubert joinedworkwith Hans-Peter Kriegel and Andreas Züfle Lehrstuhl für Datenbanksysteme Institut für Informatik Ludwig-Maximilians-Universität München

2. Outlook • Spatio-Temporal Researchand Computer Games • Managing Spatio-Temporal Game Data • Mining Game Data • DetectingCheaters • Evaluating Game Balance • Conclusions

3. Spatio-Temporal Game Data • Player avatars or units move within a virtual spatio-temporal environment • A game server has to manage a unique valid representation of the game state • Movement and timing are essential aspects of most games • Similarity to other spatio-temporal applications:traffic management, simulations, surveillance systems etc.

4. Why do research for Computer Games ? • Computer games are big business(market volume 2010:gaming industry approx. 74 billion USD,relational databases approx. 20 billion USD) • There is data: spatial objects, events,trajectories, flocks, • There are applications: Modeling computer controlled entities, manage servers, analyze players, detect cheaters • It’s fun

5. Why is research interesting for companies ? • Trends in computer games: • stand alone games -> Online Games • 1-10 players -> massive multi-player (1000+) • Console/PC -> Mobiles, Browsers .. • purchase price -> subscriptions , micro transactions. • Implications: • Games need distributed network technologies: Servers, P2P, synchronization, large data volumes, user management, spatial query processing • New threats: cheating, account hijacking, hacking… • New challenges: keep people investing time and money Preview oftheDiablo III auctionhouseusing real money

6. Management Challenges • Game Servers are high throughput • several thousand position updates in a tick (ca. 100 ms) • low jitter: a tick is required to take 100ms at most not on average • Large Games have to be distributed • a single server cannot maintain enough players • number of possible interactions increase quadratically • Distributing players depends on their spatial positions • fixed zoning via dynamic server relocation

7. Management Challenges • Persistency is mandatory • parts of the game state must be stored permanently • server must manage all relevant data (no local save games) • saving the game state must be done without extending tick processing times (disk updates must be distributed over time several ticks) • Temporal Synchronization • low dependency on heterogeneous connection latencies • temporal uncertainty: Where is player A when her last position update arrived 2s ago. • reduce transferred data volume while maintaining a fluent game play (e.g. employ dead reckoning)

8. Mining Game Data Analyzing the player behavior to • Detect Cheating • Bots (Programs playing the game for you: Farm bot) • Hacks (Modifications of the game client: Speed hack) • Exploits (Flaws in the game allowing unintended advantages: positions allowing to attack but not being attacked) 2. Evaluate Game Balance • Maintain a challenging but non-frustrating game play • Maintain a fair chance of winning for different character or faction types.

9. Why do players cheat? • economical reasons • Sell ingame money or goods for real money: poker bots, goldfarming, account trading, item trading..: • Example: playerauctions.com • Over $1 billion USD in accumulated player-to-player trading value • Over 25,000 average daily transactions (nearly 20 per minute) • Over 700 supported Massively Multi-Player Online Games • Over 30 million accumulative transactions

10. Why do players cheat? • Saving Time: Example: AFK bots are self-running programs doing simple gaming tasks without user interactions like collecting ore or herbs. • Prestige: Having success is often coupled with high prestige in the gaming community. Example: Reaching Masters League in Starcraft II • Fun: Winning the game is simply fun even without the satisfaction that the success is well deserved All types and motivations are a problem because • the gaming companies directly loses money (micro transactions) • the game becomes less attractive to other players which might quit(no fair competition)

11. Monitoring Cheats Challenges: • game state maintains current entity states but: analyzing behavior requires recent states as well • monitoring might strain server resources • checking all player actions for a reasonable time period requires a lot of processing overhead • checking should be as generic as possible (use rather outlier detection than supervized methods) • cheating players still have to be penalized (e.g. temporary ban)

12. Data Mining and Balancing challenges: • detect interesting events like boss encounters in the logs • monitor encounter results • estimate player strength to remove the bias from statistics • formalize and cluster encounter tactics => More than one successful strategy indicates interesting game play • use text mining on community web sites to measure player happiness

13. Conclusions • Computer games are an interesting application area • many games rely on a spatio-temporal virtual environment which is similar to monitoring and tracking systems • Recent developments will increase the need for managing and mining techniques • New challenges arise in real-time spatial querying, updating, persistency and server distribution • Cheat dection is a challenging task w.r.t. detection rates and throughput • Checking game fairness is statistically challenging • Measuring game difficulty and diversity requires combined consideration of game logs and community feedback

14. Joinus on MAMIVE‘11 1st International Workshop on Managing and Mining Virtual Environments In conjunctionwith ACM SIGSPATIAL GIS in ChicagoNovember the 1st, 2011 (Submission deadline September the 2nd)