Wireless Data Preparation Configuration for AI/ML Data Quality
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Solution Overview
Problem
Current 3GPP architecture lacks consideration for data preparation, a crucial step in AI/ML model lifecycle, which affects analytics performance due to varying data quality and source types, leading to model drift and inefficiencies in data sharing and processing.
Innovation Solution
Implement a data preparation configuration entity and entity in a wireless communication system to manage and process raw data, including data collection, cleaning, and formatting, with intelligent and policy-based configuration to ensure data quality and compatibility across different vendors and tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple heterogeneous data sources, then data quantity and coverage are improved, but data quality and consistency deteriorate due to varying formats and characteristics
Solution Approach 1:
The patent applies parameter changes by transforming data from multiple heterogeneous sources into a unified format with consistent characteristics. The data preparation entity modifies data parameters (formats, types, structures) to ensure homogeneity while preserving the quantity and diversity of information from various sources.
Solution Approach 2:
The patent introduces a data preparation entity as an intermediary between multiple data sources and the AI/ML model. This intermediary component standardizes and prepares data from heterogeneous sources, ensuring quality and consistency while maintaining the benefits of collecting data from multiple sources.
2Reliability
If data preparation is implemented to ensure data quality, then model performance is improved, but system complexity increases due to additional processing steps
Solution Approach 1:
The patent implements a universal data preparation entity that handles multiple data preparation tasks (collection, cleaning, formatting, validation) through a single multi-functional component. This reduces system complexity by consolidating preparation functions while maintaining high model performance through comprehensive data quality assurance.
3Manufacturing precision
If data is cleaned and formatted to ensure consistency, then data quality is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by performing data preparation tasks (cleaning, formatting, validation) before data is used for AI/ML modeling. This advance preparation ensures high data quality is achieved without increasing processing time during the actual modeling phase, as the data is ready in the required format beforehand.
Data Source
AI summary
There is provided a data preparation configuration entity in a wireless communications system. The data preparation configuration entity comprises a transceiver arranged to receive (910), a request for application layer data processing management, the request comprising a requirement for managing raw data from at least one data source. The data preparation configuration entity comprises a processor arranged to configure (920), at least one parameter of a data preparation configuration based on the request for application layer data processing management, the at least one parameter comprising information for preparing the required data. Also relates to a data preparation entity.