SPARQL Query Mapping for Multidimensional Data Warehouses
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Solution Overview
Problem
Current technologies have not effectively interfaced multidimensional data stores with the Semantic Web, primarily due to the lack of standardized vocabularies and methods for mapping relational data to RDF Linked Data, limiting the integration of data warehouses with Semantic Web technologies.
Innovation Solution
A query mapping system that uses the SPARQL Protocol and RDF Query Language to translate SPARQL queries into native queries for multidimensional databases, enabling access to multidimensional databases using a SPARQL endpoint, which generates a mapping from metadata to describe the structure of the database without duplicating data, allowing for execution of queries against the database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multidimensional data warehouses are integrated with Semantic Web technologies, then data analysis capabilities and insights are enhanced, but data duplication and system complexity increase
Solution Approach 1:
The patent introduces a query translator as an intermediary component that mediates between SPARQL queries and multidimensional database queries. This translator converts Semantic Web queries into native database queries, enabling integration without requiring data duplication. The intermediary layer handles the complexity of interfacing different data models while maintaining data integrity and avoiding redundancy.
Solution Approach 2:
The patent creates a virtual copy of the multidimensional database structure through RDF mappings, allowing Semantic Web technologies to access data without physically duplicating it. The mapping layer provides a conceptual representation that enables query translation while maintaining the original data structure and avoiding data redundancy.
2Adaptability or versatility
If standardized vocabularies and mapping methods are implemented, then integration with Semantic Web is enabled, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent develops a universal query translator that can handle multiple types of multidimensional databases and Semantic Web vocabularies through a single unified system. The translator is designed to be multi-functional, supporting various query types and data models without requiring separate specialized components for each combination, thereby reducing overall system complexity.
Solution Approach 2:
The patent implements preliminary mapping configurations that define relationships between vocabularies and database structures before query execution. By pre-establishing these mappings, the system avoids complex real-time translation decisions during query processing, reducing operational complexity while maintaining integration capability.
3Ease of operation
If direct queries against triple stores using non-standardized vocabularies are used, then data access is simplified, but data interoperability and reusability are limited
Solution Approach 1:
The patent introduces vocabulary mappings as intermediary layers that mediate between non-standardized triple store vocabularies and standardized Semantic Web vocabularies. These mappings enable data to be accessed using simple direct queries while simultaneously ensuring interoperability through standardized vocabulary translation, resolving the contradiction between ease of access and data reusability.
Data Source
AI summary
Disclosed is a system allowing to query data warehouses using SPARQL. An aspect of the system may support the representation of multidimensional data as virtual graphs. Another aspect of the system may provide mapping of SPARQL queries directed against multidimensional data vis-à-vis the graphs to native queries directed against the multidimensional data. Responses from the native queries may then be translated to a SPARQL response format.