Data Mediation Node Selective Decoding for Throughput
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Solution Overview
Problem
Telecommunication networks face significant computational burdens due to the repeated need for decoding and re-encoding of data in mediation systems, which consumes a substantial amount of processing time and resources, especially when handling complex data structures like Charging Data Records.
Innovation Solution
A method and apparatus for data mediation that uses a working data structure to store incoming data streams without initial decoding, allowing targeted data items to be extracted and decoded only when necessary, while intervening items are moved to further nodes without decoding, reducing the overall decoding and re-encoding requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is decoded and processed in traditional mediation systems, then data processing accuracy is improved, but computational burden and processing time increase significantly
Solution Approach 1:
The patent segments the data processing workflow into distinct phases: encoding preservation phase (where encoded data is stored and transmitted without decoding) and selective decoding phase (where only specific data items are decoded when needed). This segmentation allows the system to maintain data accuracy where required while avoiding unnecessary decoding operations elsewhere, thereby resolving the contradiction between processing accuracy and throughput.
Solution Approach 2:
The patent applies partial action by decoding only the specific data items that are actually needed for processing, rather than decoding entire data structures. The system identifies and extracts only the necessary fields from encoded data streams, performing decoding operations minimally and selectively. This approach maintains accuracy for required data while significantly reducing overall computational burden and improving processing throughput.
2Loss of information
If complete data decoding is performed, then data completeness is improved, but memory usage and processing overhead increase
Solution Approach 1:
The patent extracts only the specific data items that are needed for processing from the encoded data streams, rather than extracting and storing complete decoded data structures. By using selective extraction mechanisms that identify and pull out only necessary fields while leaving the rest of the encoded data intact, the system maintains data completeness for required elements while significantly reducing memory consumption and processing overhead.
Solution Approach 2:
The patent creates selective copies of only the necessary data items from encoded streams rather than copying entire data structures. The system identifies specific fields that need to be processed and creates minimal copies of those elements, preserving data completeness for required information while minimizing memory usage. This selective copying approach avoids the overhead of handling complete decoded data structures.
3Adaptability or versatility
If data re-encoding is performed after processing, then data transport compatibility is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by maintaining data in its original encoded format throughout the processing pipeline, preserving the encoding structure rather than decoding and then re-encoding. This approach allows the system to perform processing operations on encoded data directly or on selectively extracted items, thereby maintaining transport compatibility without incurring the time cost of complete re-encoding operations. The original encoding is preserved as a template for final data assembly.
Data Source
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AI summary
The teachings herein disclose a method and apparatus for data mediation in a telecommunication network provides advantageous processing of an incoming data stream in a matter that reduces the amount of decoding needed for processing and correspondingly reduces the amount of re-encoding needed for transport of the processed data stream. These teachings make use of a working data structure that provides a memory-efficient data structure for processing targeted data items in the data stream and are implemented, for example, in a data mediation node that includes one or more communication interfaces, e.g., for receiving incoming data streams for data-mediation processing, and for sending along processed data streams to targeted recipients.