Game Data Processing Using Event and Semantic Classes
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Solution Overview
Problem
Managing large quantities of data from computer implemented games for analytics is challenging due to complexity and the need for real-time analysis across multiple platforms and evolving data sets.
Innovation Solution
A method and apparatus for processing game data by parsing event identity information using event and semantic classes, allowing for the transformation and querying of data to provide processed data, which includes adding new event types to existing classes for real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large quantities of game data are collected for analytics, then the ability to analyze user behavior and enhance games is improved, but the complexity of managing and processing the data increases
Solution Approach 1:
The patent segments game data into structured event types (e.g., player login, purchase, level completion) organized within event classes. This segmentation allows the system to handle large quantities of data by processing them in manageable, standardized units rather than as unstructured mass data, thereby reducing management complexity while maintaining comprehensive data collection capabilities.
Solution Approach 2:
The patent transforms raw game data into standardized event representations with consistent parameters and schemas. By changing the parameter structure of the data through event definitions and semantic classes, the system makes large volumes of data more manageable and easier to process analytically, resolving the contradiction between data quantity and management complexity.
2Loss of time
If real-time analytics are performed on large data sets, then the ability to provide timely insights is improved, but the processing time and computational resources required increase
Solution Approach 1:
The patent performs preliminary action by pre-defining event types, event classes, and semantic classes before data arrives. This schema is established in advance, allowing incoming data to be rapidly parsed and categorized without complex real-time decision-making, thus enabling real-time analytics with reduced processing overhead.
Solution Approach 2:
The system transforms data into standardized event parameters according to pre-established schemas, which accelerates processing. By changing the data representation into a standardized format with consistent parameters, the system can quickly aggregate, filter, and analyze data in real-time without the computational burden of interpreting varied data structures.
3Adaptability or versatility
If new event types are added to existing event classes, then the adaptability of the system to new game features is improved, but the complexity of maintaining the event class structure increases
Solution Approach 1:
The patent creates universal event classes that can accommodate multiple event types through inheritance and polymorphism. Existing event classes are designed with sufficient generality and extensibility to incorporate new event types without requiring fundamental structural changes, thereby maintaining adaptability while controlling complexity through reusable, modular class designs.
Solution Approach 2:
The patent segments the event class structure into hierarchical levels (base event classes, specific event types, semantic classes) where new event types can be added as leaf nodes without affecting the overall structure. This segmentation allows the system to adapt to new game features by simply adding new event type definitions within the existing hierarchical framework, minimizing structural complexity changes.
Data Source
AI summary
A method and apparatus for processing data are provided. The method and apparatus receive data relating to one or more computer implemented games. The data includes event identity information, which is parsed using an event definition of an event type. The event type is associated with the respective event identity information, wherein each event type belongs to an event class and to at least one semantic class. The parsed data is processed using information that defines one or more event classes or one or more semantic classes in order to provide a set of processed data.