Network Request Parsing for Dynamic Interaction Data Entries
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Solution Overview
Problem
Existing systems require publishers to pre-define interaction data for tracking user interactions with online applications, lacking flexibility and efficiency in handling network requests.
Innovation Solution
A data processing system that includes an input port, parser engine, search engine, and logic engine to automatically parse and generate interaction data from network requests, enabling dynamic tracking of undefined interaction types across multiple accounts without pre-formatting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If publishers pre-define interaction data for tracking, then tracking reliability is improved, but system flexibility and ease of operation deteriorate
Solution Approach 1:
The system automatically parses network requests to extract interaction parameters without requiring publisher configuration. The parser engine autonomously identifies and extracts relevant data from incoming requests, and the logic engine automatically generates tracking entries, enabling the system to serve itself rather than requiring manual pre-definition by publishers.
Solution Approach 2:
The system pre-processes network requests by parsing them in advance to identify interaction parameters before formal tracking is established. This preliminary parsing action allows the system to prepare interaction data structures ahead of time, ensuring reliable tracking is ready when needed without requiring publishers to pre-define all possible interactions.
2Measurement precision
If publishers pre-format network requests with specified parameters, then measurement precision is improved, but ease of operation and device complexity worsen
Solution Approach 1:
The parser engine automatically extracts interaction parameters from network requests without requiring publishers to manually specify or format parameters. The system self-services by autonomously identifying relevant parameters and formatting them appropriately, eliminating the need for publisher configuration while maintaining precise interaction tracking.
Solution Approach 2:
The system performs preliminary parsing of network requests to extract and validate interaction parameters before processing. This advance preparation ensures measurement precision is achieved through proper parameter extraction and formatting, while the automated nature of this preliminary action eliminates manual publisher effort.
3Adaptability or versatility
If the system processes network requests without pre-defined formats, then adaptability improves, but processing time and productivity may worsen
Solution Approach 1:
The parser engine performs preliminary parsing and extraction of interaction parameters from network requests as soon as requests are received. This advance processing prepares data structures in anticipation of tracking needs, reducing the time required for formal processing while maintaining the ability to handle various request formats flexibly.
Solution Approach 2:
The processing system is divided into specialized components: the parser engine handles format-flexible parsing, the search engine manages data structure organization, and the logic engine handles entry generation. This segmentation allows each component to optimize for its specific function, maintaining high processing speed while accommodating diverse request formats.
4Adaptability or versatility
If the system queries multiple data structures across accounts, then adaptability improves, but device complexity and processing time worsen
Solution Approach 1:
The search engine implements a universal query mechanism that can search across multiple account data structures using a standardized approach. This multi-functional search capability allows the system to handle cross-account tracking requests uniformly, managing complexity through a single versatile search interface rather than separate handling for each account.
Solution Approach 2:
The system performs preliminary organization of data structures by account and interaction type, preparing data for efficient cross-account queries in advance. This preliminary structuring allows the search engine to quickly locate relevant data across multiple accounts without ad-hoc complexity, reducing processing time while maintaining cross-account adaptability.
Data Source
AI summary
A data processing system for searching network requests to extract values of parameters in the network requests includes a shared memory that stores data structures, wherein each of the data structures is stored in association with a value of a key, and wherein each of the data structures includes one or more entries for storage of data representing a network request. A parser engine parses a particular network request for values of parameters and for a value of a key. A search engine queries the shared memory for a particular data structure stored in association with the value of the key. A logic engine detects an absence of a particular entry, in the particular data structure, that stores data representing the particular network request, generates an entry in the particular data structure, and stores in the generated entry the values of the parameters.