A Game Design Analytic System Based on Data Provenance

Key insights

6 takeaways

01
The proposed framework uses data provenance to map player actions and environmental variables into visual graphs, eliminating the need for designers to use specialized query languages.See it on page 2
02
The system identifies design discrepancies by correlating metrics such as distance traveled, item acquisition, and failure points to detect unexpected difficulty spikes or ineffective reward distribution.See it on page 4
03
By capturing both prospective and retrospective data, the methodology allows designers to compare intended gameplay loops against actual player behavior.See it on page 1
04
The research focuses specifically on the infinite runner mobile genre, utilizing web services to collect and analyze data across distributed environments.See it on page 1
05
The framework aims to replace manual, query-heavy analytical processes with a streamlined visual interface to accelerate design-related decision-making.See it on page 5
06
Future development will focus on full system implementation and empirical testing to validate the framework's efficacy compared to traditional analytical methods.See it on page 5

Summary

This analysis introduces a game design framework that utilizes data provenance to assist developers in identifying critical gameplay patterns and balancing issues. By capturing and documenting the lifecycle of player actions, the system generates visual graphs that allow designers to interpret complex performance data without requiring expertise in specialized query languages. The primary objective is to provide an intuitive, data-driven interface that highlights discrepancies between intended game design and actual player behavior, such as unexpected difficulty spikes or ineffective item distribution.

The proposed methodology focuses on the collection of both prospective and retrospective provenance data, which tracks the sequence of player activities, environmental variables, and execution logs. By mapping metrics like distance traveled, item acquisition, and failure points, the system creates graphical representations that reveal player progression trends. For instance, the framework can visualize the relationship between distance and round count to determine if a game is too difficult for new users, or correlate item collection with player survival to assess the efficacy of in-game rewards.

The scope of this research centers on the infinite runner genre, a mobile gaming segment characterized by high download volumes and continuous, repetitive gameplay loops. By leveraging Web services for data collection, the system is designed to function across distributed environments, enabling the analysis of diverse player experiences. While the current framework is conceptual, it aims to replace manual, query-heavy data analysis with a streamlined visual approach. Future development focuses on full system implementation and empirical testing to validate whether these graphical insights significantly improve the speed and accuracy of design-related decision-making compared to traditional analytical methods.