Dataset Encode Values for Matching Redundant Data Collections
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Solution Overview
Problem
Existing data monitoring systems are inefficient in detecting matching or redundant datasets at the dataset level, as they primarily focus on data record duplication and hash-based techniques fail due to changes in dataset structure or ordering, leading to resource wastage and increased time in data analytics projects.
Innovation Solution
The use of encode values generated based on dataset fields, such as numerical distribution, schema, update patterns, and semantic content, to compare new datasets with existing ones, determining similarity scores to identify matching datasets, including subsets or supersets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hash-based techniques are used to detect duplicate data records, then data record duplication can be detected, but dataset level matching fails due to changes in dataset structure or ordering
Solution Approach 1:
The patent segments the dataset into multiple fields and generates separate encode values for each field (e.g., numerical distribution encode values, schema encode values, semantic encode values). This segmentation allows the system to capture dataset characteristics at a granular level, enabling accurate matching even when the overall dataset structure or ordering changes.
Solution Approach 2:
The patent transforms dataset characteristics into encoded parameter representations (encode values) that are invariant to ordering changes. By converting numerical distributions, schemas, and semantic content into standardized encode values, the system maintains reliable matching accuracy despite parameter reordering or structural modifications.
2Measurement precision
If users manually inspect existing datasets to determine whether a desired dataset already exists, then accurate matching can be achieved, but the process becomes time- and labor-intensive
Solution Approach 1:
The patent performs preliminary encoding of datasets into encode values that capture essential characteristics (numerical distributions, schemas, semantic content). This preliminary action enables rapid comparison and matching without requiring manual inspection, significantly reducing the time needed to determine whether a desired dataset already exists while maintaining high matching accuracy.
Solution Approach 2:
The patent creates encoded representations (copies) of dataset characteristics that preserve essential matching information in a compact, comparable format. These encode values serve as efficient proxies for the actual datasets, enabling rapid comparison and matching operations without requiring users to manually inspect the full datasets.
3Productivity
If datasets are created without checking for existing matches, then data analytics projects can proceed quickly, but redundant datasets waste computing resources
Solution Approach 1:
The patent performs preliminary encoding and matching checks using encode values before datasets are created and stored. This preliminary action quickly identifies redundant datasets, preventing resource waste from duplicate storage and processing while maintaining fast data analytics project progression by automating the detection process.
Solution Approach 2:
The patent implements an automated system that performs dataset matching and redundancy detection without requiring manual user intervention. The system encodes datasets, compares encode values, and identifies matches autonomously, enabling rapid productivity improvement while eliminating computing resource waste from redundant datasets.
Data Source
AI summary
Techniques are disclosed relating to detecting matching datasets using encode values. In various embodiments, a data monitoring system may perform encoding operations on a first dataset to generate a first encode value that corresponds to a particular one of one or more fields included in the first dataset. The data monitoring system may then determine whether the first dataset matches a previously analyzed dataset. For example, in some embodiments, data monitoring system may compare the first encode value to a previous encode value that corresponds to a second field of the previously analyzed dataset. Based on this comparison, the data monitoring system may generate an output value that is indicative of a similarity between the first encode value and the previous encode value. The data monitoring system may then determine whether the first dataset matches the previously analyzed dataset based on this output value.