Network Control Apparatus for Aggregate Data Classification
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Solution Overview
Problem
Modern communications networks face inefficiencies in managing diverse network operational data, which hinders automatic detection and response to cyberattacks or faults due to the lack of accurate classification and reliance on human administrators for data type identification.
Innovation Solution
A computer-implemented method that automatically infers the type of attributes in network operational data items by processing collective properties, enabling type-specific classification and common class-specific treatment, thereby improving network management efficiency and enabling automated responses to network issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual identification of data types by human administrators is used, then data type accuracy may be maintained, but automation extent and productivity deteriorate
Solution Approach 1:
The system performs self-service by automatically inferring data types through type inference techniques that analyze collective properties of data values. The network control apparatus autonomously identifies data types without human administrator intervention, using statistical analysis and pattern recognition to determine appropriate data types for network operational data attributes.
2Measurement precision
If diverse network operational data from multiple manufacturers is processed without type-specific processing, then device complexity is reduced, but measurement precision and classification accuracy deteriorate
Solution Approach 1:
The system applies parameter changes by implementing type-specific processing that adapts classification algorithms based on inferred data types. Different statistical methods and analysis techniques are applied depending on whether the data is numeric, categorical, temporal, or other types, thereby improving classification accuracy while managing complexity through systematic parameter adaptation.
Solution Approach 2:
The processing system is segmented into type-specific processing modules that handle different data types independently. After inferring data types, the system divides the classification task into specialized sub-processes for each data type, allowing accurate handling of diverse network operational data from multiple manufacturers without overwhelming system complexity.
3Productivity
If automatic type inference is implemented, then productivity and automation are improved, but device complexity increases
Solution Approach 1:
The system performs preliminary action by inferring data types before classification and processing operations. This preliminary type inference step enables subsequent processing to be optimized and streamlined, improving overall productivity while managing complexity through a structured two-phase approach: type inference followed by type-specific processing.
Solution Approach 2:
The type inference mechanism serves multiple functions: it identifies data types for accurate classification, determines appropriate statistical methods, and enables automated decision-making. This multi-functional approach improves productivity without proportionally increasing complexity, as the same inference engine supports multiple downstream processing requirements.
Data Source
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AI summary
A method of operating a communications network is disclosed. In order to manage a network, it is first necessary to establish the state the network is in. This is difficult in practice because the network operational data stored and transmitted in the network takes a myriad of forms owing to the variety of suppliers and types of network equipment. There is a need to distil that network operational data down to aggregate network operational data which can be taken to provide an indication of the state of the network which is of a manageable size, and to which network management apparatus can react by sending control commands to the network. The problem of generating aggregate network operational data is tackled by identifying the type of each attribute found in each network operational data item, and classifying the network operational data items in a manner which takes account of the identified types and thus provides network aggregate data which more accurately reflects the operational state of the network. This in turn leads to the network management apparatus controlling the network to operate in a more efficient manner than has hitherto been possible.