Data Search Engine Using Statistical Profile Vectors
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Solution Overview
Problem
Conventional data search methods are inefficient, produce misleading or irrelevant results, and have limited scope when searching for datasets, as they rely on search terms rather than data profiles, failing to account for the schema and statistical metrics of datasets.
Innovation Solution
The system generates a sample data vector based on the data schema and statistical metrics of a sample dataset, which is used to search a data index of reference datasets, calculating similarity metrics to identify relevant datasets, allowing for a more targeted and effective search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional search-term based methods are used to search for datasets, then the search can be performed using simple keywords, but the results are inefficient, misleading, or irrelevant and have limited scope
Solution Approach 1:
The patent transforms the search approach by changing the parameters from simple keyword matching to multi-dimensional data profiling. Instead of searching based on single-term keywords, the system generates comprehensive data profiles including statistical metrics (mean, variance, skewness, kurtosis), data schemas, and metadata for both query and reference datasets. This parameter transformation enables efficient and accurate dataset discovery by comparing datasets based on their structural and statistical characteristics rather than superficial keyword matches.
2Loss of time
If search-term based methods are used, then the search can be performed quickly with simple queries, but the scope is limited to a small number of drives, databases, or online resources
Solution Approach 1:
The patent implements a universal data profiling framework that can operate across diverse data sources including local drives, databases, and online resources. The system generates standardized data profiles that capture essential characteristics regardless of the source type, enabling consistent comparison and search across heterogeneous environments. This multi-functional approach allows the search system to adapt to various data storage locations and formats while maintaining efficient performance through profile-based matching.
3Quantity of substance
If conventional keyword search is used, then the search can return large numbers of results, but many results are irrelevant to the desired objective
Solution Approach 1:
The patent replaces the mechanical keyword-matching system with a statistical and structural analysis approach. Instead of relying on simple text search algorithms that return numerous potentially irrelevant results, the system uses data profiling to compute statistical metrics and compare data schemas. This substitution transforms the search mechanism from superficial keyword matching to deep structural and statistical analysis, significantly improving result relevance while maintaining manageable result sets.
4Device complexity
If search-term based approaches are used, then the search can be performed without understanding data profiles, but the search cannot account for data schema or statistical metrics
Solution Approach 1:
The patent applies preliminary action by generating comprehensive data profiles for all reference datasets before the actual search occurs. During the preprocessing phase, the system computes statistical metrics, extracts data schemas, and creates metadata profiles for each dataset. This preliminary profiling enables the search phase to efficiently compare query datasets against pre-analyzed reference datasets using profile similarity, avoiding the need to perform complex analysis during the search itself and preserving rich data profile information.
Data Source
AI summary
Systems and methods for searching data are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a sample dataset and identifying a data schema of the sample dataset. The operations may include generating a sample data vector that includes statistical metrics of the sample dataset and information based on the data schema of the sample dataset. The operations may include searching a data index comprising a plurality of stored data vectors corresponding to a plurality of reference datasets. The stored data vectors may include statistical metrics of the reference datasets and information based on corresponding data schema. The operations may include generating, based on the search and the sample data vector, one or more similarity metrics of the sample dataset to individual ones of the reference datasets.