Data Record Auditing for Mobile Billing Accuracy
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Solution Overview
Problem
Data processing applications in mobile telephone communication systems often experience data record loss or rejection, leading to inaccurate billing records and inefficiencies in infrastructure planning, due to the lack of effective monitoring and reconciliation of data quantities and processing latency.
Innovation Solution
A method is introduced to determine the quantities of data records received and output by modules within these applications, applying reconciliation rules to identify data losses and latency, and providing reports to ensure accurate billing and inform infrastructure upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data processing applications process communications data without monitoring data quantities, then processing speed is maintained, but data record loss occurs leading to inaccurate billing records
Solution Approach 1:
The patent implements feedback mechanisms by monitoring data record quantities at each module stage and comparing them against expected values. When discrepancies are detected (indicating data loss), the system generates alerts and reports that feed back to operators and system administrators, enabling corrective actions to maintain billing accuracy without requiring complete system redesign
Solution Approach 2:
The patent introduces intermediary monitoring components that act as mediators between the data processing modules and the billing system. These intermediaries track data record quantities and verify data integrity without disrupting the core processing flow, thus improving measurement precision while adding controlled complexity only where necessary
2Productivity
If data processing applications process large volumes of data records, then productivity increases, but data record loss and rejection increase
Solution Approach 1:
The patent applies preliminary action by establishing monitoring and reconciliation mechanisms before data processing begins. Data record quantities are tracked and validated at the input stage and at each processing module, allowing potential issues to be identified and addressed before they result in data loss or rejection, thus maintaining both high productivity and data integrity
Solution Approach 2:
The system continuously monitors data record quantities throughout processing and provides feedback when anomalies are detected. This real-time feedback enables immediate corrective actions to prevent data loss, allowing the system to maintain high processing volumes while ensuring data record integrity through active monitoring and intervention
3Measurement precision
If reconciliation rules are applied to monitor data quantities, then data accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements partial monitoring by applying reconciliation rules selectively to critical data stages and modules rather than uniformly across all processing steps. This approach focuses monitoring efforts on high-risk areas where data loss would have the greatest impact, thereby improving data quantity accuracy while minimizing the time overhead associated with comprehensive monitoring
Solution Approach 2:
The system performs preliminary validation of data record quantities at module interfaces before detailed processing occurs. This preliminary check quickly identifies obvious discrepancies without requiring full reconciliation, reducing processing latency while maintaining adequate data accuracy through targeted monitoring at critical checkpoints
Data Source
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AI summary
A method includes determining a first quantity of data records of a group of data records from a stream of data records received by an application having a plurality of modules. The method includes, for one or more of the modules of the application, determining a respective second quantity of data records output by the module during processing of the group of data records. The method includes determining whether the first and second quantities of data records satisfy a rule. The rule is indicative of a target relationship among a quantity of data records received by the application and a quantity of data records output by one or more modules of the application.