Data Timeline Branching for Client-Side Event Enrichment
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Solution Overview
Problem
Existing application instrumentation and event tracking methods consume significant computing resources and bandwidth on client devices, leading to long instrumentation cycles and inefficient data processing.
Innovation Solution
Implement a specialized data logging feature on client devices that processes events using a plug-in architecture, enabling efficient data collection, filtering, and enrichment before forwarding to designated destinations, with optional clean-up steps to optimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional application instrumentation methods are used to collect and process event data on client devices, then complete user behavior data can be gathered, but computing resource consumption and bandwidth usage increase significantly
Solution Approach 1:
The patent segments the event processing pipeline into distinct stages: event generation, enrichment, filtering, and forwarding. By dividing the processing into modular stages with independent components, the system can selectively process only necessary data at each stage, reducing overall computing resource consumption while maintaining data completeness.
Solution Approach 2:
The patent implements preliminary enrichment and filtering of event data before it leaves the client device. By performing data preparation, validation, and filtering in advance on the client side, the system reduces the amount of raw data that needs to be transmitted and processed on servers, thereby reducing bandwidth usage and server computing resources.
2Measurement precision
If comprehensive event data is processed and transmitted to destination servers, then accurate analytics can be performed, but bandwidth requirements increase
Solution Approach 1:
The patent extracts and removes unnecessary or redundant information from event data before transmission. By filtering out duplicate events, irrelevant data fields, and redundant metadata, the system maintains the essential information needed for accurate analytics while significantly reducing the data volume transmitted over the network.
Solution Approach 2:
The patent applies different processing qualities and levels of detail to different types of event data based on their specific requirements. Critical events receive full processing and transmission, while less important events undergo lighter processing, optimizing bandwidth usage while maintaining analytics accuracy for high-priority data.
3Adaptability or versatility
If event data processing is performed on client devices with multiple destinations, then customized analytics can be provided, but device complexity increases
Solution Approach 1:
The patent implements a universal event processing pipeline that can handle multiple destinations and event types through a single standardized framework. The enrichment and filtering components are designed to be destination-agnostic, allowing the same infrastructure to serve multiple analytics platforms with different requirements without increasing device complexity.
Solution Approach 2:
The patent employs dynamic configuration and routing mechanisms that adapt the processing pipeline based on the target destination and event characteristics. The system can dynamically select which enrichment and filtering operations to apply, and dynamically route events to appropriate destinations, providing versatility without requiring fixed complex processing paths for each destination.
Data Source
AI summary
Described are systems, methods, and computer-readable storage mediums for processing an event that is detected on a client device. The event is processed at a first data stream corresponding to a first branch of an event processing timeline. After initial processing and data enrichment, the event is processed at a second data stream corresponding to a second branch of the event processing timeline in parallel with the processing of the event at the first data stream. The processing of the event at the second data stream includes performing additional initial data processing and additional data enrichment of the data associated with the event. The additional initial data processing and the additional data enrichment is specific to a destination associated with the event. After the additional initial data processing and the additional data enrichment, the event is conditionally forwarded to the destination associated with the second branch.