While developing a relational database, some elements of the conceptual and the logical data model are left behind. This event produces a built-in inconsistency between how businesses conceptualize their business domain, and how this conceptualization is translated into concrete data models. Virtual Structured Query Language Ontologies (SQL-Os) can resolve this discrepancy by providing key functions that data modeling cannot offer to relational databases.
During the process of modeling a relational database, some elements of both the conceptual and logical data models such as the collected and documented semantic information are left behind. In contrast, SQL-O employs standard SQL to implement the key capabilities of Semantic Web ontologies. SQL-Os ensure the key information left behind by data modeling is retained by enabling the definition of abstract concepts that give common meaning to the data enriched with business rules, relationships, context, and logic.
Furthermore, the abstract concepts are mapped to existing databases to enable querying of the underlying data in standard SQL and connect to the business intelligence tools and data science notebooks used by enterprises. In addition, querying context-enriched concepts delivers direct and substantial benefit as the size of the SQL queries is reduced by up to 90%.
In modern data lakes solutions where no actual data modeling is done, SQL-Os provide semantic and relationship enablement. Besides, SQL-Os help organizations maintain an accurate, business-meaningful glossary or taxonomy of the terms that describe all the artifacts in a data lake, by employing concepts. This empowers users to search, discover, understand, and query the appropriate elements of the data lake by themselves.
Briefly, SQL-Os leverage the huge SQL ecosystem, modernize databases without disturbing the IT infrastructure, deliver a universal solution for linking heterogeneous data sources, and enable smart data.
Brought to you by Scope e-Knowledge Center, an SPi Global Company, a trusted global partner for Digital Content Transformation Solutions, Knowledge Modeling (Taxonomies, Thesauri and Ontologies), Abstracting & Indexing (A&I), Metadata Enrichment and Entity Extraction.
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