Data Filtering System Using Quality of Information Quantization
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Solution Overview
Problem
Conventional M2M data filtering techniques are inefficient in filtering data effectively across different domains, leading to high information loss and inability to enforce context-based data selection and storage, especially when dealing with heterogeneous filters and third-party modules.
Innovation Solution
A method and system that quantify value information using Quality of Information assessment, determine data amount filtering, provide filter information with category assignment, and apply a common calculation procedure to update importance categories, enabling efficient filtering and load reduction while allowing integration of multiple filters and access control mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional filtering techniques are used, then data filtering can be performed, but information loss is high and filtering accuracy is low
Solution Approach 1:
The patent transforms heterogeneous filter characteristics into a unified parameter space by quantizing value information according to QoI assessment. This allows different filters to be processed through a common calculation procedure, improving filtering accuracy while reducing information loss through parameter standardization.
Solution Approach 2:
The patent segments the filtering process into distinct stages: QoI assessment of raw data, quantization of value information, determination of data amount filtering information, and application of common calculation procedure. This segmentation enables precise control at each stage, improving overall filtering accuracy.
2Adaptability or versatility
If multiple heterogeneous filters are integrated, then filtering capability is enhanced, but system complexity increases
Solution Approach 1:
The patent creates a universal filtering framework where a common calculation procedure handles multiple heterogeneous filters. By quantizing value information according to QoI assessment, the system achieves multi-functionality without proportionally increasing complexity, as the same procedural framework applies to all filter types.
Solution Approach 2:
The patent introduces QoI assessment and quantization as intermediary layers between heterogeneous filters and the common calculation procedure. This intermediary structure enables integration of diverse filters while maintaining manageable system complexity through standardized interfaces.
3Reliability
If context-based data selection is enforced, then data relevance is improved, but filtering overhead increases
Solution Approach 1:
The patent performs QoI assessment and quantization of value information as preliminary actions before the main filtering operation. By pre-processing data to extract quality metrics, the system improves data relevance selection while reducing the computational overhead during the actual filtering stage.
4Ease of manufacture
If data filtering is performed without quantization, then processing is simpler, but filtering precision deteriorates
Solution Approach 1:
The patent applies quantization to transform continuous value information into discrete, manageable parameters based on QoI assessment. This parameter transformation maintains processing simplicity through standardized discrete values while significantly improving filtering precision through accurate quality-based differentiation.
Data Source
AI summary
A method for managing data in a system includes a data filtering. Value information is quantized for each of one or more different filters according to a Quality of Information (QoI) assessment of data and related to a corresponding one of the filters. Data amount filtering information is determined for each of the value information indicating an amount of information to be filtered. Filter information is provided for each of the filters being used for filtering, the filter information including the determined data amount filtering information and category assignment information, wherein the category assignment information indicates an assignment of a respective one of the filters to a respective one of a plurality of predefined, appropriate data categories. Current importance category information is provided for at least one of the data categories and a common calculation procedure is applied for the filters.