Method and System for Enhanced Ontology-Assisted Querying of Data Stores

The vastness and diversity of data sources present significant challenges to data analysts. The increase in quantity, complexity, and diversity of data necessitates advanced systems for efficient and reliable data analytics. The traditional methods that require deep knowledge regarding the structure of the underlying storage system limit the efficiency and speed of data processing. The conventional approaches, rooted in specific understanding of physical data storage, often lead to a knowledge bottleneck, affecting the efficiency and reliability of data analytics. With the inability to deal with heterogeneous data sources, analysts have a hard time decoding the valuable information concealed within these unstructured datasets. Furthermore, the lack of incorporation of provenance information in the results often raises questions regarding the credibility of analysis.

Technology Description

This technology provides systems and methods that enable high-level, ontology-based analysis of low-level, unstructured data stored in a key/value store model. It caters analytically to large volumes of data arising from numerous sources, conveniently bypassing any need for the analyst to be acquainted with the complex structure of underlying data storage. Significant features of this technology include flexible ontology-assisted addressing and the potential to incorporate such addressing into widely used query languages like Structured Query Language (SQL). This method enables the procurement and display of results and provenance information of the obtained results. The unique differentiation of this technology is its ontology-based nature. It guides an analyst to make sense of a vast array of unstructured data without any specific knowledge of the underlying physical data storage. Its flexibility in addressing permits easier navigation through complex data structures, and its ability to integrate within existing query languages ensures wider compatibility and reach. Furthermore, the provision of provable sources of results is a distinguishing characteristic that provides enhanced reliability and accountability in data analytics.