Serialized Charging Record Storage for CDR Compression
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Solution Overview
Problem
Current storage solutions for Call Detail Records (CDRs) face challenges with high storage consumption, performance issues, and latency due to the large volume and complexity of data, leading to increased costs and resource requirements for billing systems.
Innovation Solution
A method and apparatus for creating a serialized charging record that uses a byte array representation of a tree-structured CDR, allowing for efficient storage in a single database row, with condensation and compression of data values and the application of platform-agnostic key/value formats, enabling reduced storage needs and improved access mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CDRs are stored in a relational database system with normalized and denormalized structures, then data accessibility and performance are improved, but storage consumption increases significantly
Solution Approach 1:
The patent segments CDR data into hierarchical parts (header, service parts, supplementary service parts) that can be selectively stored and accessed. Only necessary attributes are stored in the database while maintaining the ability to reconstruct the complete tree structure, reducing storage requirements while preserving data accessibility.
Solution Approach 2:
The patent extracts and stores only essential key attributes (customer ID, contract ID, call start time, record ID, etc.) in the database, while the complete hierarchical CDR structure is reconstructed in memory when needed. This extraction approach reduces storage consumption while maintaining data accessibility.
2Productivity
If de-normalization is applied to increase storage efficiency, then data retrieval performance improves, but consistency issues arise during updates
Solution Approach 1:
The patent implements a dynamic approach where the CDR storage structure adapts based on update operations. During rating and charging processes, the system can selectively update specific parts of the hierarchical structure without requiring complete denormalized table updates, maintaining consistency while preserving retrieval performance.
Solution Approach 2:
The patent prepares the hierarchical CDR structure in advance with clearly defined parts and attributes, establishing a framework that prevents consistency issues before they occur. The structured approach with explicit part definitions ensures that updates during billing processes maintain data integrity.
3Reliability
If the storage model reflects the tree structure in normalized relational structure, then data integrity is maintained, but storage size increases due to information duplication
Solution Approach 1:
The patent implements a nested structure where the hierarchical CDR tree is represented within a compact database row format. The nested representation allows the complete tree structure to be stored efficiently without duplication, maintaining data integrity while reducing storage size.
Solution Approach 2:
The patent changes the parameter representation from traditional normalized relational columns to a compact hierarchical format with part indicators and attribute-value pairs. This parameter transformation reduces storage requirements while preserving the ability to maintain and query data integrity.
4Quantity of substance
If classic de-normalization solutions are used to handle huge CDR volumes, then storage capacity is optimized, but performance and latency issues persist
Solution Approach 1:
The patent segments the CDR processing into efficient stages: storage of essential attributes in optimized database rows, selective retrieval based on query needs, and in-memory reconstruction of complete hierarchical structures for processing. This segmentation enables both storage optimization and high processing throughput.
Solution Approach 2:
The patent introduces an intermediary layer between database storage and application processing that efficiently reconstructs hierarchical CDR structures from compact stored representations. This intermediary mechanism enables fast data retrieval and processing without the performance penalties of classic de-normalization approaches.
Data Source
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AI summary
Method and data access unit for storage of data records for creating a serialized charging record formatted for insertion into a charging database. The method includes traversing the hierarchical charging record and for each part node of said hierarchical charging record identifying an attribute of the part node and determining if said attribute is a key attribute or a search attribute and if affirmative storing an attribute value of said attribute in a field of the serialized charging record based on a charging database configuration definition. A part segment comprising the attribute value and a data value token is stored in a payload body field of the serialized charging record with a part node indicator representing the location of the part node in the hierarchical charging record based on a hierarchical charging record configuration definition. A method and data access unit for creating a hierarchical charging record is also disclosed. An advantage is that a serialized charging record may be stored in one storage entity such as a table row.