Attribution Server for Cloud Event Data Normalization
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Solution Overview
Problem
Cloud computing systems face challenges in efficiently collecting and evaluating user feedback, particularly due to high upfront costs and the tedious process of distinguishing constructive from unconstructive responses, which diminishes the quality of explicit feedback.
Innovation Solution
A cloud-based implicit feedback service that uses an attribution server to receive a data schema, normalize event data instances, and calculate attribution scores based on customer-defined criteria, linking events to assess their likelihood and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual collection and evaluation of user feedback is performed, then feedback quality can be maintained through human judgment, but high upfront costs and tedious evaluation processes reduce productivity and increase time consumption
Solution Approach 1:
The system enables self-service by automatically collecting event data from multiple sources and computing attribution scores without requiring manual human intervention. The attribution server autonomously processes event streams, links related events, and generates feedback quality metrics, freeing users from tedious manual evaluation while maintaining consistent quality standards through algorithmic processing
Solution Approach 2:
The patent replaces the mechanical human evaluation process with an automated computational system. Instead of manual review of user feedback, the system uses event data instances, attribution scores, and algorithmic processing to objectively measure and evaluate feedback quality, substituting human labor with automated mechanical computation
2Measurement precision
If manual evaluation of user feedback is performed, then nuanced understanding of constructive vs unconstructive responses can be achieved, but the tedious process increases time consumption and reduces productivity
Solution Approach 1:
The system implements continuous automated evaluation by continuously receiving event data instances from multiple sources, continuously computing attribution scores, and continuously generating feedback quality assessments. This uninterrupted automated process eliminates the time loss associated with manual batch evaluation while maintaining consistent measurement precision through ongoing algorithmic analysis
Solution Approach 2:
The attribution server performs self-service evaluation by autonomously analyzing event data, determining event relationships, and computing feedback quality metrics without requiring external human intervention. This automated self-evaluation maintains accuracy through consistent application of attribution algorithms while eliminating the time consumption of manual review processes
3Productivity
If automated event processing is implemented, then productivity and feedback collection efficiency are improved, but complex data normalization and event linking processes increase device complexity
Solution Approach 1:
The attribution server implements multi-functionality by simultaneously performing data reception from multiple sources, event normalization against schemas, event linking based on attribution criteria, and feedback quality computation. This universal system handles diverse event types and processing tasks through a single integrated platform, managing complexity through functional consolidation rather than proliferation of separate systems
Solution Approach 2:
The system manages processing complexity by dynamically adjusting normalization parameters and attribution score thresholds based on event types and data sources. The schema-based normalization adapts to different event structures, and attribution criteria can be modified to change linking behavior, allowing the system to handle complex data variations through parameter adjustment rather than structural complexity
4Measurement precision
If comprehensive event data collection is performed, then feedback quality and attribution accuracy are improved, but data processing requirements and computational resources increase
Solution Approach 1:
The system extracts only the essential event characteristics needed for attribution by normalizing event data against schemas that define required fields and formats. Instead of processing all possible event attributes, the system extracts and processes only the critical data elements necessary for computing attribution scores, reducing computational resource consumption while maintaining accuracy through focused data extraction
Solution Approach 2:
The system implements partial processing by computing attribution scores based on the most significant event relationships and characteristics. Rather than exhaustively analyzing every possible event attribute and relationship, the system focuses computational resources on the most impactful factors for attribution accuracy, achieving sufficient precision through selective rather than complete data processing
Data Source
AI summary
The present embodiments relate to an event attribution for estimating a downstream impact for a computing device of a cloud computing system. The computing device can transmit a data schema to a first client application executing on a first web server, the data schema describing a plurality of event data instance characteristics. The computing device can receive a first event data instance and a second event data instance from the first client application. The computing device can format the first event data instance and the second event data instance to conform to a uniform format as described by the data schema. The computing device can link the first event data instance and the second event data instance based at least in part on an event data instance characteristic. The computing device can calculate an attribution score between the first event data instance and the second event data instance.