Streaming Data Settlement Cursor for Calculation Accuracy
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Solution Overview
Problem
Existing systems face challenges in accurately managing and differentiating between settled and unsettled data in streaming environments, particularly due to issues like data latency, timestamp errors, and variable data rates, which can lead to incorrect calculations and decisions based on changing data.
Innovation Solution
A method involving a hardware processor that accesses a data set and a reference object to demarcate settled and unsettled data portions, optimizing transformations based on the relative position of the reference object along a dimension, and applying these transformations to generate an aggregated data set with clear demarcation between settled and unsettled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If calculations are run periodically without concern for variable streaming data rates and latency, then productivity is improved, but measurement precision deteriorates because subsequent data may make previous results incorrect
Solution Approach 1:
The system dynamically adjusts the calculation approach based on data settlement status. For unsettled data, it uses provisional calculations that can be updated as data arrives. For settled data, it uses final calculations. This dynamic adaptation allows frequent calculations while maintaining accuracy where possible.
Solution Approach 2:
The patent segments data into settled and unsettled portions, allowing different calculation strategies to be applied to different segments. This enables the system to run calculations frequently on settled data while acknowledging that results for unsettled data may need revision.
2Measurement precision
If calculations are run only when all streams have provided data, then measurement precision is improved, but productivity deteriorates due to delayed results
Solution Approach 1:
The system performs partial calculations using available data rather than waiting for complete data from all streams. It produces provisional results that can be updated later, rather than delaying until all data is available. This allows more frequent calculations while maintaining the option to refine results.
Solution Approach 2:
The system performs preliminary calculations with the data available at each point in time, creating provisional results that can be updated when additional data arrives. This preliminary action enables faster initial results while allowing subsequent refinement.
3Ease of operation
If data is used without differentiation between settled and unsettled portions, then ease of operation is improved, but reliability deteriorates due to changing data impacting decisions
Solution Approach 1:
The system applies different quality treatments to different portions of the data based on settlement status. Settled data receives final processing while unsettled data receives provisional processing. This local differentiation maintains operational simplicity while improving reliability through appropriate handling of uncertain data.
Solution Approach 2:
The patent introduces an intermediary mechanism (the settlement status indicator) that mediates between raw data and processing operations. This intermediary allows the system to maintain simple operations while reliably distinguishing between data that is final and data that may change.
Data Source
AI summary
A cursor demarcating a data set between a settled portion and an unsettled portion can be generated. A transformation can be applied to the data set, the transformation accounting for the cursor and transforming the settled portion of the data set differently than the unsettled portion of the data set in order to create a transformed output data set. The transformed output data set may further include a modified cursor based on the applied transformation and demarcating settled and unsettled portions of the transformed output data set.