Unified Logging System for Multi-Channel Data Aggregation
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Solution Overview
Problem
The increasing size of datasets in big data analytics poses challenges in collecting, storing, managing, transferring, sharing, analyzing, and visualizing data across multiple channels and platforms, leading to incompatible tracking systems and errors due to different identifiers and attributes, which hinders efficient data processing and analysis.
Innovation Solution
A unified logging system generates standardized records of job-related actions across channels by using a reference ID, member ID, and job ID to aggregate events, handling missing IDs and standardizing fields, allowing for comprehensive data aggregation and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple independent tracking systems are used to track responses over various channels, then each channel can be tracked independently, but the systems become incompatible due to different identifiers and attributes, leading to data inconsistency
Solution Approach 1:
The patent implements a universal tracking system that handles multiple channels (email, mobile, web, etc.) through a single unified architecture. The system uses standardized identifiers and attributes that work across all channels, eliminating the need for channel-specific tracking systems while maintaining the ability to track diverse response types independently.
Solution Approach 2:
The patent enforces homogeneous data structures by requiring all tracking events to use the same identifier formats and attribute schemas regardless of channel. This standardization ensures that data from email campaigns, mobile app interactions, and web activities all conform to the same structure, enabling consistent processing and analysis across channels.
2Quantity of substance
If conventional software tools and relational databases are used to store data, then data can be stored in structured format, but the tools cannot handle petabytes or exabytes of loosely structured data generated continuously from multiple sources
Solution Approach 1:
The patent segments the data management system into multiple distributed components that can independently handle different portions of the data workload. This segmentation allows the system to scale horizontally across multiple servers, enabling it to manage petabytes or exabytes of data without overwhelming a single database instance.
Solution Approach 2:
The patent introduces an intermediary layer between data ingestion and storage/processing that standardizes and structures loosely formatted data before it enters the database system. This intermediary component transforms unstructured data from multiple sources into a consistent format, reducing the complexity of managing large volumes of diverse data.
3Productivity
If tracking systems are independently designed for different channels, then each system can be optimized for its specific channel, but bugs and corner cases cause tracking records to include missing or erroneous identifiers and attributes
Solution Approach 1:
The patent implements preliminary validation and standardization steps that occur before tracking data is finalized. The system pre-processes incoming tracking events to verify required identifiers and attributes are present and correctly formatted, catching errors early in the data pipeline before they propagate through the system.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor tracking data quality and identify patterns of missing or erroneous identifiers. The system uses this feedback to automatically correct common errors and alert operators to systematic issues, continuously improving tracking accuracy across all channels.
Data Source
AI summary
The disclosed embodiments provide a system for processing data. During operation, the system receives, over a set of event streams, a set of logging events for actions performed between members and jobs over multiple channels. Next, the system aggregates a subset of the logging events spanning a logging window by a reference identifier (ID) generated based on a user session of a member, a first member ID for the member, and a first job ID for a job. The system then creates, based on a unified data logic, a record containing a subset of the actions represented by the logging events and contexts for the subset of the actions. Finally, the system outputs the record for use in subsequent analysis associated with the member and the job.