Path-Based Database Structure for Competitive Metric Generation
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Solution Overview
Problem
Generating a competitive metric from network activity is challenging due to extensive processing time, requiring efficient sorting, parsing, and grouping mechanisms to quickly identify user interactions across multiple content providers.
Innovation Solution
The system generates a path data structure by correlating interaction data elements with timestamps, parsing major path data sets, and filtering based on a network model to create a minor path data set, which allows for the determination of competitive metrics by comparing interaction numbers between content providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional user polling methods are used to determine user interactions with multiple content providers, then user interaction data can be collected, but the processing time is extensive and efficiency is low
Solution Approach 1:
The system performs preliminary actions by collecting and storing interaction data elements with identifiers and time stamps in advance, organizing them into a structured format before analysis is needed. This pre-organization of data allows for rapid retrieval and processing when competitive metrics need to be calculated, eliminating the need for time-consuming data collection at the moment of analysis.
Solution Approach 2:
The patent segments the large dataset of interaction data into manageable path data structures, where each path represents a sequence of interactions by a single entity. This segmentation allows the system to process and analyze individual user paths independently and in parallel, significantly improving processing efficiency compared to analyzing all data collectively.
2Measurement precision
If comprehensive interaction data from multiple content providers is analyzed to generate competitive metrics, then accurate user behavior insights are obtained, but the data processing complexity increases
Solution Approach 1:
The system extracts only the essential elements needed for competitive metric calculation from the comprehensive interaction data. By identifying and extracting relevant interaction data elements (identifiers, time stamps, content provider associations) and organizing them into structured paths, the system reduces data processing complexity while maintaining the accuracy needed for meaningful competitive analysis.
Solution Approach 2:
The patent introduces path data structures as an intermediary representation between raw interaction data and final competitive metrics. These paths serve as a intermediate processing layer that simplifies the complex relationships between multiple content providers and user interactions, making the data more manageable and easier to analyze for competitive comparisons.
3Reliability
If real-time competitive metrics are generated by processing all interaction data, then up-to-date insights are provided, but the processing time and computational resources increase
Solution Approach 1:
The system implements a dynamic approach where path data structures are built incrementally as new interaction data arrives, rather than processing all data from scratch each time metrics are needed. This dynamic construction allows the system to maintain up-to-date competitive metrics with minimal additional processing time, as the foundational path structures are already in place and can be quickly queried for current comparisons.
Data Source
AI summary
Systems, methods, and computer-readable storage media that may be used to generate a competitive metric are provided. One method includes receiving interaction data elements including identifiers and time stamps and generating a path data structure. A path data structure includes the interaction data elements sorted by identifier and time stamp. Further, the method includes sorting, parsing, and filtering the path data structure to generate a minor path data set. The method includes generating the competitive metric by using the minor path data set to determine the number of interactions an entity has with a group of content providers and comparing that number of interactions to the number of interactions a second entity has with the same group of content providers.