Data Metric Modeling with Eligibility Filtering for Sparse Data
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Solution Overview
Problem
Conventional data metrics are often inaccurate and unreliable due to the complexity, sparsity, or difficulty in accessing underlying data, making it challenging to isolate and analyze correct data effectively.
Innovation Solution
A data structure management system comprising a data subscription unit, modeling unit, and data distribution device that filters, sorts, and adjusts data based on eligibility criteria to generate accurate data metrics, including bank yield curves and credit spread yield curves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data processing methods are used on sparse or complex data, then data metrics can be generated, but the accuracy and reliability of the metrics deteriorate
Solution Approach 1:
The system extracts only the relevant and eligible data from the sparse data set by applying multiple filtering criteria. The data subscription unit receives data from multiple sources and filters it based on eligibility criteria, extracting only the data that meets specific requirements for metric generation, thereby improving accuracy despite data sparsity.
Solution Approach 2:
The system performs preliminary filtering and validation of data before it is used for metric generation. By pre-filtering data at the subscription unit and applying eligibility criteria in advance, the system ensures that only high-quality data proceeds to the modeling stage, preventing inaccurate metrics from being generated due to poor data quality.
2Productivity
If all received data is processed to ensure completeness, then data coverage is improved, but processing efficiency and analysis accuracy deteriorate due to irrelevant data
Solution Approach 1:
The system extracts only the necessary data elements that meet eligibility criteria, removing irrelevant data before processing. This extraction approach maintains data coverage for relevant information while eliminating waste of processing resources on unnecessary data, thereby improving both efficiency and effective coverage.
Solution Approach 2:
The system applies partial processing to the data set by filtering and selecting only the portion of data that is relevant and eligible for metric generation. Rather than processing all received data equally, it focuses computational resources on the subset of data that will actually contribute to accurate metrics, improving processing efficiency without losing relevant information.
3Quantity of substance
If multiple data sources are integrated to improve data availability, then data completeness is improved, but data complexity and difficulty of analysis increase
Solution Approach 1:
The system segments the data processing function into distinct stages: data subscription with filtering, eligibility validation, and metric generation. By dividing the complex task of processing multi-source data into sequential segments, each handling a specific aspect of data preparation, the system manages data complexity while maintaining availability from multiple sources.
Solution Approach 2:
The system performs preliminary filtering and organization of data from multiple sources before integration into the modeling process. By pre-processing and structuring data from various sources according to eligibility criteria, the system reduces the complexity that would otherwise arise from integrating raw multi-source data, making subsequent analysis more manageable.
4Measurement precision
If extensive filtering and validation is applied to ensure data quality, then metrics accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The system performs filtering and validation operations in advance, before data is used for metric generation. By pre-validating data eligibility and filtering quality issues upfront, the system ensures high metrics reliability without adding time pressure to the actual metric computation, as the data preparation is already complete.
Solution Approach 2:
The system extracts only the essential validation and filtering operations that are necessary for ensuring data quality, removing unnecessary processing steps. By focusing on extracting and applying only the critical eligibility criteria and filters needed for reliable metrics, the system maintains high reliability while minimizing extraneous processing time.
Data Source
AI summary
A system and method for isolating electronic data and generating enhanced data. A data subscription unit receives data having plural data formats from data source devices. The data may be received pre-filtered or filtered by the data subscription unit. A modeling unit may receive the filtered data from the storage databases, may determine eligibility of the stored data based on eligibility criteria, sort the eligible data based on at least one sorting parameter, adjust and/or normalize the eligible data based on at least one adjustment parameter and/or an aggregate value for a data attribute, generate data metrics over a defined set of time periods based on the eligible data, generate further data metrics based on the data metrics, and derive specified values from these metrics. A data distribution device may transmit or make available the data metrics, further data metrics and values to remote devices.