Relativistic Retriever for Data Set Discovery
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Solution Overview
Problem
Conventional information processing systems are limited in their ability to support diverse similarity measures and fail to effectively query data sets based on suitability for specific purposes, goals, or analytic roles, restricting their functionality in data set discovery and recommendation.
Innovation Solution
An information processing system comprising a data set discovery engine with a data set indexer and a relativistic retriever that generates similarity indexes using multiple similarity measures, including frequency-based and non-frequency-based measures, and executes queries based on suitability templates to identify and rank data sets suitable for particular purposes or roles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional document-oriented data processing tools are used, then the system structure is simple, but the similarity measures supported are limited
Solution Approach 1:
The patent implements a universal data processing framework that supports multiple similarity measures (frequency-based, non-frequency-based, and custom measures) through a unified architecture. The system uses a common data model with flexible indexing and retrieval mechanisms that can accommodate various similarity calculations without requiring separate specialized tools for each measure type.
2Adaptability or versatility
If conventional data processing tools are used, then the implementation is straightforward, but the ability to query data sets based on suitability for specific purposes is insufficient
Solution Approach 1:
The patent segments the data processing system into distinct functional modules: data ingestion components, preprocessing modules, multiple indexing strategies (frequency-based indexes, inverted indexes), similarity calculation engines, and query processing units. This modular segmentation enables the system to handle complex suitability-based queries while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces intermediary components including suitability templates that act as mediators between user queries and data sets, and a relativistic retriever that mediates between multiple similarity measures and the final retrieval results. These intermediaries enable complex suitability-based queries by translating user intent into structured query operations that can be processed by the underlying data processing engine.
3Measurement precision
If multiple similarity measures are supported, then the data set discovery accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing multiple types of indexes (frequency-based indexes, inverted indexes, and other auxiliary structures) during the data ingestion and preprocessing phase. This allows the system to perform fast similarity calculations during query processing without computing all similarity measures from scratch, thereby reducing online computational complexity while maintaining high discovery accuracy.
Solution Approach 2:
The patent implements partial action by allowing users to selectively enable only the similarity measures relevant to their specific query needs, rather than computing all possible similarity measures. The system supports a superset of similarity measures but enables efficient partial evaluation based on query context, suitability templates, and user preferences, reducing unnecessary computational overhead.
Data Source
AI summary
An apparatus in one embodiment comprises a processing platform implementing a data set discovery engine. The data set discovery engine comprises a data set indexer configured to generate similarity indexes for a plurality of data sets, and a relativistic retriever coupled to the data set indexer and configured to obtain a suitability template for a query and to execute the query against one or more of the similarity indexes based at least in part on the suitability template. A given one of the similarity indexes comprises at least first and second auxiliary information generated from respective ones of at least first and second different similarity measures of a plurality of different similarity measures. The first and second similarity measures comprise selected ones of the plurality of different similarity measures that are supported by the data set discovery engine with the supported similarity measures comprising both frequency-based and non-frequency-based similarity measures.