Decision-Tree-Guided Data Evaluation in Distributed Systems
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Solution Overview
Problem
Existing data evaluation technologies face challenges in efficiently and reliably performing data evaluation in distributed environments due to high computational complexity and network inefficiencies, often resulting in deadlock scenarios and inefficiencies in data retrieval and processing.
Innovation Solution
The implementation of targeted data retrieval using data-source-specific threads for structured and unstructured data extraction, along with decision-tree-guided data evaluation, which limits post-retrieval processing to specific threads configured for unstructured data sources to prevent deadlocks and enhance parallelism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data evaluation is performed in distributed environments using conventional technologies, then data retrieval and processing can be conducted, but computational complexity increases and network inefficiencies occur leading to deadlock scenarios
Solution Approach 1:
The patent divides the data evaluation system into multiple independent threads, each responsible for specific data sources. Structured data sources are handled by dedicated threads that query and retrieve data independently, while unstructured data sources are processed by separate threads that perform extraction and loading operations. This segmentation eliminates computational conflicts and deadlock scenarios by ensuring each thread operates autonomously on its assigned data source without interfering with others.
2Adaptability or versatility
If conventional data retrieval methods are used in distributed environments, then data can be accessed from multiple sources, but network inefficiencies and deadlock scenarios occur
Solution Approach 1:
The patent introduces an intermediary layer in the form of a data warehouse that acts as a buffer between multiple data sources and the evaluation engine. Data from various structured and unstructured sources is first retrieved and stored in the data warehouse, which then serves as a centralized repository for the evaluation engine to access. This intermediary approach improves network efficiency by reducing direct connections between evaluation engine and multiple data sources, eliminating network conflicts and deadlock scenarios while maintaining adaptability to diverse data sources.
3Productivity
If post-retrieval processing is performed by multiple threads, then data processing capacity increases, but deadlock scenarios occur due to resource conflicts
Solution Approach 1:
The patent implements dynamic thread assignment where processing capacity is adjusted based on data source type rather than using a fixed multi-threaded approach for all data. Structured data sources are processed by dedicated threads with specific query capabilities, while unstructured data sources are handled by separate threads with extraction and loading functions. This dynamic allocation ensures each thread has the appropriate capabilities for its assigned data source while preventing resource conflicts and deadlock scenarios that would occur with indiscriminate multi-threading.
Data Source
AI summary
There is a need for more effective and efficient data evaluation. This need can be addressed by, for example, techniques for data evaluation in accordance with a shared decision tree data object. In one example, a method includes generating, using a plurality of feature extraction threads, shared evidentiary data; generating, based on a selected shared evidentiary data subset of the shared evidentiary data that correspond to one or more selected nodes of the shared decision tree data object, refined evidentiary data; processing the refined evidentiary data in accordance with the shared decision tree data object to generate an evaluation output and an explanation output; and displaying an evaluation output user interface comprising user interface data describing the evaluation output and the explanation output.