Grouping Interdependent Data Fields for Efficient Processing
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Solution Overview
Problem
Existing data processing systems face inefficiencies in storing, retrieving, and analyzing large datasets due to the limited utility of general statistics, especially when dealing with multiple sets of data that may not be meaningfully related to each other.
Innovation Solution
The implementation of field grouping logic that identifies and groups interdependent fields based on levels of interdependence, access frequency, and storage space, using divergence scores and density modeling to optimize data processing and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If general statistics are used to describe large sets of data, then data storage and processing can be performed, but the utility is limited especially when dealing with multiple unrelated data sets
Solution Approach 1:
The patent segments data fields into interdependent groups based on statistical analysis. Instead of treating all fields uniformly, the system identifies and groups fields that have meaningful relationships (interdependent fields) while separating unrelated fields. This segmentation allows for more targeted and efficient processing of related data while reducing overhead for unrelated data.
Solution Approach 2:
The patent changes the parameter of field relationships by calculating divergence scores and density metrics to determine interdependence. By introducing these new parameters (divergence score, density modeling results), the system can distinguish between related and unrelated fields, transforming the approach from general statistics to relationship-aware data organization.
2Reliability
If all fields in a data set are processed uniformly, then completeness is maintained, but computational resources are wasted on unrelated fields
Solution Approach 1:
The patent applies partial action by processing only the interdependent fields that are relevant to each other, rather than uniformly processing all fields. The system identifies subsets of related fields and focuses computational resources on these groups, performing complete analysis on relevant data while skipping or minimizing processing of unrelated fields.
Solution Approach 2:
The system introduces new parameters (interdependence threshold, divergence score) that enable selective processing. By evaluating fields against these parameters, the system can determine which fields warrant full processing and which can be excluded, thus maintaining reliability for relevant data while reducing overall computational consumption.
3Speed
If metadata is created for all data sets, then data retrieval and analysis can be optimized, but storage overhead increases significantly
Solution Approach 1:
The patent extracts and stores only the essential metadata characteristics that define interdependent fields, rather than creating comprehensive metadata for all fields. By taking out only the critical relationship information (divergence scores, density metrics, interdependence relationships), the system achieves optimization benefits without the full storage overhead of complete metadata for every field.
Solution Approach 2:
The system changes the metadata storage approach by using compact parameter representations (divergence scores, density values) instead of full statistical descriptions. These condensed parameters capture the essential relationship information needed for efficient retrieval and analysis while occupying minimal storage space compared to traditional comprehensive metadata.
4Measurement precision
If detailed statistical analysis is performed on all fields, then accurate insights can be obtained, but processing time increases
Solution Approach 1:
The patent segments the analysis process by first performing a quick divergence score calculation to identify interdependent fields, then applying detailed statistical analysis only to those identified groups. This two-stage segmentation maintains measurement precision for relevant fields while avoiding unnecessary detailed analysis of unrelated fields, thus reducing overall processing time.
Solution Approach 2:
The system performs preliminary action by calculating divergence scores and identifying interdependent field groups before conducting detailed statistical analysis. This preliminary filtering step quickly eliminates unrelated fields, so that subsequent detailed analysis is applied only where needed, maintaining accuracy where required while minimizing total processing time.
Data Source
AI summary
Processes, machines, and stored machine instructions are provided for grouping interdependent fields. Field grouping logic may include specially configured machines and/or stored instructions that identify group(s) of interdependent fields of a data set. The field grouping logic may receive, from a client on a customizable interface, a request for interdependent fields in a data set and, in response, cause generation of an output object that identifies the similar fields in the data set. The field grouping logic may exclude field(s) of the data set that are not interdependent, are not frequently accessed, or do not consume much space in storage, even though the request may not identify which fields are interdependent. The output object identifies the similar fields in set(s) or list(s) of fields, or in a hierarchy or hierarchies of groups and sub-groups.