Configurable Computation Modules for Web Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Website providers face challenges in processing web analytics data due to varying requirements from different entities, which necessitates customized code implementation, often requiring expertise in writing program code to configure processing preferences.
Innovation Solution
The development of configurable computation modules that can be assembled and configured to analyze network analytics data, allowing users to select and connect multiple modules to process data based on specific preferences, enabling flexible data analysis and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customized code implementation is used to process web analytics data according to specific entity requirements, then the adaptability to different processing preferences is improved, but the device complexity and difficulty of implementation increase
Solution Approach 1:
The patent segments the web analytics data processing system into multiple independent computational modules, each responsible for a specific processing function. These modules can be selectively assembled to create customized processing pipelines without requiring custom code implementation. The segmentation allows entities to configure their preferred processing by selecting and connecting pre-built modules rather than writing code from scratch.
Solution Approach 2:
The patent creates universal computational modules that can be used across different entities and processing scenarios. Each module is designed to perform a specific function that can be reused in various configurations. This multi-functionality allows the same set of modules to serve multiple entities with different preferences, eliminating the need for separate code implementations for each entity.
2Adaptability or versatility
If entity-specific processing preferences are implemented through custom code, then the adaptability to specific needs is improved, but the ease of operation deteriorates as non-programmers struggle to write the required code
Solution Approach 1:
The patent enables non-programmer users to configure their own data processing preferences through a graphical interface where they can select and connect computational modules visually. The system provides self-service capabilities allowing users to assemble their preferred processing pipelines without requiring programming knowledge. The configuration is done through point-and-click operations rather than code writing.
Solution Approach 2:
The patent introduces a graphical user interface as an intermediary between the user's processing preferences and the underlying computational modules. This mediator translates simple user selections into complex processing configurations automatically. Users interact with high-level concepts through the interface rather than directly manipulating code or module parameters.
3Adaptability or versatility
If multiple customized code implementations are created for different entities, then the adaptability to various preferences is improved, but the loss of time for development and maintenance increases
Solution Approach 1:
The patent performs preliminary action by pre-building a library of computational modules that encapsulate common data processing functions. These modules are prepared in advance and can be reused across multiple entities. Instead of developing custom code for each entity, the system allows rapid assembly from pre-prepared modules, significantly reducing development time.
Solution Approach 2:
The patent merges the processing preferences of multiple entities into a shared library of computational modules. Common processing logic is consolidated into reusable modules that serve multiple entities. This combining approach eliminates redundant code development and maintenance across different entities, reducing overall time investment while maintaining individual customization capabilities.
Data Source
AI summary
Input may be received indicating selection of multiple modules from a plurality of predefined configurable computation modules and to configure the selected modules. Configuring the selected modules may include defining one or more module inputs to each selected module and may also include defining one or more connections between modules of the selected multiple modules. The selected modules may be assembled based on the respective define one or more module inputs to each selected module and the defined one or more connections between modules. The assembled selected module may enable analysis to be performed on data received at the respective one or more module inputs.