Data Collection Enablement for Multi-Source Network Analytics
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Solution Overview
Problem
Current systems lack clear specifications for selecting data sources and optimizing data collection processes for network analytics, particularly in scenarios where the same type of data is available at multiple entities, leading to inefficiencies and repeated data collection.
Innovation Solution
A data collection enablement service (DCES) that determines optimal data sources, processes data collection requests, and manages collected data to ensure efficient and coordinated data retrieval from various layers and entities within the 3GPP service enablement layer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected from multiple entities to ensure data availability, then data reliability is improved, but data collection complexity increases
Solution Approach 1:
The patent introduces a data collection enablement service as an intermediary layer between data sources and analytics consumers. This service manages data collection requests, selects appropriate data sources based on available information, and coordinates data retrieval, thereby reducing the complexity for individual entities while maintaining data availability through multiple potential sources.
Solution Approach 2:
The data collection enablement service provides universal functionality for managing data collection across multiple entities and data sources. It handles various data types, selection criteria, and coordination scenarios through a unified service interface, reducing the need for entity-specific data collection mechanisms while ensuring reliable data acquisition.
2Reliability
If data collection requests are processed independently for each analytics request, then data freshness is improved, but data collection efficiency deteriorates
Solution Approach 1:
The patent merges multiple data collection requests into a unified management framework. The data collection enablement service consolidates requests for the same or similar data, coordinates their processing, and retrieves data in an optimized manner, thereby maintaining data freshness while significantly improving collection efficiency by avoiding redundant operations.
Solution Approach 2:
The service performs preliminary analysis of data collection requests to identify opportunities for consolidation and optimization before actual data retrieval. By pre-processing requests and determining the most efficient collection strategy in advance, it ensures data freshness is maintained while avoiding inefficient repeated collection operations.
3Productivity
If optimal data source selection is implemented, then data collection efficiency is improved, but system complexity increases
Solution Approach 1:
The data collection enablement service acts as an intermediary that handles the complexity of optimal data source selection. It maintains information about available data sources, their capabilities, and current states, then uses this information to make intelligent selection decisions, thereby improving efficiency without requiring individual entities to implement complex selection logic themselves.
Data Source
AI summary
Methods, devices, and systems for data collection enablement services are described herein. In one aspect, a method may include receiving, by a data collection enablement service (DCES), a data collection request; determining, by the DCES, one or more data sources based at least on discovered data source profiles; receiving, from the one or more data sources and by the DCES, data according to the received request; generating, by the DCES, a dataset from the received data; storing the generated dataset in a repository in communication with the DCES; and transmitting, by the DCES, a response to the data collection request indicative of a storage location of the generated dataset.