Multi-Source Web Service Metadata Collection and Reporting
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Solution Overview
Problem
Current methods fail to provide a granular level of detail in tracking user interactions with individual components across multiple web sources, and existing solutions can only monitor and track interactions for a single web source, making it challenging to analyze and correlate data from multiple sources.
Innovation Solution
A system and method that automatically generates reports from component-level metadata collected from multiple web sources by using web services with associated plugins to detect and collect user interactions, which are then parsed and stored to create detailed metadata elements for reporting, allowing for customized reports across multiple platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If monitoring and tracking user interactions is implemented across multiple web sources, then the quantity and comprehensiveness of interaction data is improved, but the device complexity and difficulty of detecting and measuring increases
Solution Approach 1:
The system segments the monitoring task by deploying separate web source plugins for each web source (e.g., LinkedIn, Twitter, Facebook plugins). Each plugin independently collects interaction data from its specific web source, dividing the complex multi-source monitoring task into manageable, specialized components that can be developed and maintained independently.
Solution Approach 2:
The patent introduces an intermediary component - the standardized data collection interface and metadata schema - that mediates between diverse web source plugins and the central analytics system. This intermediary layer standardizes data formats and interaction protocols, allowing the system to handle multiple web sources without proportionally increasing complexity.
2Measurement precision
If granular component-level tracking is implemented, then measurement precision is improved, but the difficulty of detecting and measuring and device complexity increases
Solution Approach 1:
The system segments content into discrete, trackable components (articles, videos, downloads, presentations) with unique identifiers. Each component can be individually tracked at a granular level across multiple web sources, enabling precise measurement of user interactions with specific content pieces while maintaining manageable detection complexity through standardized tracking mechanisms.
Solution Approach 2:
The patent employs parameter changes by transforming diverse interaction types (views, downloads, shares, clicks) into a standardized metadata format with consistent parameters. This standardization allows granular tracking of individual components across different web sources while simplifying the detection and measurement process through uniform data structures.
3Adaptability or versatility
If customized reports for multiple web sources are generated, then adaptability is improved, but the loss of time for data processing and analysis increases
Solution Approach 1:
The system performs preliminary action by pre-processing and standardizing interaction data as it is collected from web sources. Metadata is structured and validated in advance according to predefined schemas, preparing the data for rapid report generation. This preliminary processing reduces the time required for later data analysis and report customization.
Solution Approach 2:
The patent utilizes parameter changes by implementing a flexible metadata schema that can adapt to different report requirements. The standardized data structure allows for efficient filtering, aggregation, and customization of reports based on various parameters (time periods, web sources, content types, interaction types) without requiring extensive reprocessing of the underlying data.
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for automatically generating reports from component-level metadata collected from multiple web sources. A predetermined set of web sources are respectively monitored to detect user interactions with one or more predetermined components. Component-level metadata associated with such user interactions is then respectively collected from each web source and parsed to generate component-level metadata elements. The component-level metadata elements are then used to automatically generate reports on the user interactions with the predetermined components at multiple web sources.