Real-Time Analytics Platform for Scalable Event Stream Processing
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Solution Overview
Problem
Managing large quantities of data from various sources, such as game analytics, is challenging due to the complexity of real-time analytics and the need to relate different events and store contextual information in a scalable manner, often requiring specialist knowledge and inefficient processing methods.
Innovation Solution
A method and apparatus that receive and process data streams, allowing multiple scripts to run concurrently, aggregate data, and update information in real-time, using a scalable real-time analytics platform that supports connecting events and storing contextual information directly from live streams, simplifying complex data stream analytics and reducing the need for specialist knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data processing methods are used to manage large quantities of data from various sources, then data can be stored and processed, but the system becomes complex and requires specialist knowledge to operate
Solution Approach 1:
The patent introduces an intermediary processing layer that sits between data sources and analysis tools. This layer includes components like the event store, state manager, and script execution engine that translate complex stream processing operations into simpler, more manageable operations. The intermediary layer handles the complexity of real-time data processing, aggregation, and state management, while presenting a simplified interface to users through the analysis tool.
2Speed
If real-time analytics are performed on large data streams, then instantaneous results are obtained, but the processing becomes computationally intensive and difficult to scale
Solution Approach 1:
The patent segments the data processing system into independent, modular components that can operate in parallel. The event stream is divided into multiple partitions or segments that can be processed simultaneously by different script execution instances. The state management is segmented by entity identifiers, allowing independent state tracking for different entities. This segmentation enables horizontal scaling and improves processing efficiency while maintaining real-time analytics capabilities.
Solution Approach 2:
The system employs dynamic script execution where scripts are loaded, compiled, and executed on-demand based on the incoming data stream and user queries. The script execution engine dynamically manages the lifecycle of analysis scripts, allowing them to be added, removed, or modified without stopping the overall processing pipeline. This dynamic approach enables flexible adaptation to changing analytical requirements while maintaining efficient real-time processing.
3Adaptability or versatility
If multiple scripts run concurrently to analyze different aspects of data, then comprehensive analytics are achieved, but managing and coordinating these scripts becomes complex
Solution Approach 1:
The patent implements a universal script execution engine that can run multiple different types of analysis scripts concurrently using a common framework. This engine provides universal interfaces for script initialization, event processing, state access, and result output. All scripts, regardless of their specific analytical purpose, interact with the system through standardized methods, simplifying management and coordination. The state manager provides a universal mechanism for scripts to access and update entity state, eliminating the need for complex inter-script communication protocols.
Data Source
AI summary
A computer device receives a stream of event data which event data has the data about the event itself and a device or user identifier. One or the events is stored for different identifiers to be used as state. Two or more different scripts are run and the information about at least one event is shared by at least two different scripts.