Ontology-Based Querying for Unstructured Key-Value Stores
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Solution Overview
Problem
Existing unstructured key/value stores are challenging to perform high-level analysis on due to their lack of support for complex queries and data abstraction, making it difficult for organizations to derive insights from massive amounts of diverse data sources.
Innovation Solution
A method and system that utilize an ontology-based approach to query and retrieve data by receiving user queries with address expressions, evaluating them to resolve table and field entities, and generating rewritten queries for execution over the data store, allowing for real-time analysis across diverse data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unstructured key/value stores are used to store massive amounts of diverse data, then data storage flexibility and scalability are improved, but the ability to perform high-level analysis and complex queries deteriorates
Solution Approach 1:
The patent introduces an ontology layer as an intermediary between the unstructured key/value store and the query interface. The ontology provides structured definitions of entities, attributes, and relationships that enable complex queries without requiring changes to the underlying unstructured storage format. This mediator allows users to perform high-level analysis while the system handles the complexity of data retrieval from the unstructured store.
Solution Approach 2:
The patent segments the system into distinct layers: the unstructured data storage layer (key/value store), the ontology layer (providing structured semantics), and the query layer (handling user requests). This segmentation allows each layer to operate independently with optimized characteristics - the storage layer maintains flexibility while the ontology layer provides structure for analysis.
2Reliability
If traditional databases with schema constraints are used, then query structure and data integrity are improved, but write/read performance and scalability deteriorate
Solution Approach 1:
The ontology acts as a mediator that provides data integrity guarantees without imposing rigid schema constraints on the storage layer. The ontology defines expected data structures and relationships, enabling validation and consistent querying while allowing the underlying key/value store to maintain its high-performance unstructured format.
3Loss of time
If ontology-based query processing is implemented, then query execution time and processing efficiency are improved, but system complexity increases
Solution Approach 1:
The ontology is pre-defined and loaded into the system before query processing begins. This preliminary action allows the system to have structured knowledge ready for query optimization, enabling faster query execution without adding complexity during the actual query processing phase. The ontology definitions are prepared in advance to guide efficient data retrieval.
Data Source
AI summary
Systems and methods are disclosed that provide high-level, ontology-based analysis of low-level data stored within an unstructured key/value store. The systems and methods allow an analyst to make sense of massive amounts of data from diverse sources without having any knowledge of the underlying physical data storage. The systems and methods provide flexible ontology assisted addressing, embedding such addressing in existing query languages such as widely used Structured Query Language (SQL), and returning results and provenance information of the results.