Data Orchestration Platform Using Interpretation Dictionary
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Solution Overview
Problem
In network communication environments with diverse data sources, manual configuration and interpretation of raw data are necessary, as existing methods do not dynamically interpret data or determine appropriate AI logic units for processing, leading to inefficiencies and increased overhead.
Innovation Solution
A data orchestration platform that uses a data interpretation dictionary and machine learning techniques to automatically interpret raw data from various sources and select appropriate AI logic units for processing, eliminating the need for user intervention and enhancing data collection operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration and interpretation of raw data is performed, then data processing can be done with existing methods, but user overhead and time consumption increase significantly
Solution Approach 1:
The system performs self-service by automatically interpreting raw data from diverse sources and selecting appropriate AI logic units without requiring user configuration. The data interpretation dictionary and machine learning model enable the system to autonomously process data, eliminating manual intervention and significantly reducing user overhead and time consumption.
2Adaptability or versatility
If diverse data sources are integrated, then data collection capability is enhanced, but system complexity and configuration difficulty increase
Solution Approach 1:
The data interpretation dictionary serves as a universal interface that can interpret data from multiple diverse sources using various formats (binary, hexadecimal, proprietary formats). This multi-functional capability allows the system to handle diverse data sources without increasing configuration complexity, as the dictionary automatically adapts to different data formats and sources.
Solution Approach 2:
The data interpretation dictionary acts as an intermediary layer between diverse data sources and the AI logic units. It mediates the data flow by automatically interpreting and standardizing data from different sources, eliminating the need for complex manual configuration and simplifying the integration of diverse data sources.
3Ease of operation
If automated data interpretation is implemented, then user intervention is eliminated, but system processing overhead increases
Solution Approach 1:
The system performs preliminary action by pre-building the data interpretation dictionary and machine learning model during the setup phase. This preliminary preparation enables automated data interpretation without requiring real-time user intervention, simplifying operation while distributing processing overhead to the initial model training phase rather than ongoing operations.
Data Source
AI summary
Aspects of the disclosure relate to data orchestration platform management in a network communication environment including a set of information sources. A set of raw data may be ingested using the set of information sources. A set of interpreted data that indicates a set of attributes of the network communication environment may be generated using a data interpretation dictionary configured to analyze the set of raw data. An artificial intelligence (AI) logic unit to perform processing with respect to the set of interpreted data may be determined using a data orchestration platform management engine to analyze the set of attributes of the network communication environment. The set of interpreted data may be processed using the AI logic unit.