SPARQL Engine Dynamic Function Integration
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Solution Overview
Problem
Current SPARQL systems lack the ability to easily extend useful functions without implementation expertise, as they do not support expressing complex logic beyond graph pattern matching, requiring users to manually write calculations like 'co-prime' logic each time, and do not allow for dynamic integration of user-defined or external functions.
Innovation Solution
A system and method that allows users to dynamically extend the SPARQL function library by integrating user-defined and external functions, enabling users to select and instantly integrate functions from a library, even if they are written in different programming languages, without requiring knowledge of implementation details, and supports analytics for function selection and chaining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If SPARQL systems use standard graph pattern matching techniques, then querying capability is maintained, but ability to express complex logic and computations is limited
Solution Approach 1:
The patent introduces an intermediary component that translates natural language or high-level computational expressions into SPARQL queries. This mediator layer enables complex logic expression without requiring users to manually write complex SPARQL syntax, thus improving adaptability while managing system complexity through abstraction.
Solution Approach 2:
The system enhances SPARQL with universal computational functions that can handle multiple types of operations (mathematical computations, logical operations, data transformations) within a unified query language framework. This allows the system to express diverse complex logic using a single extended SPARQL interface, improving versatility without proportionally increasing complexity.
2Adaptability or versatility
If SPARQL function library is extended with custom functions, then computational capability is improved, but implementation expertise is required
Solution Approach 1:
The system implements a self-service mechanism where users can define and register custom functions through a simplified interface that automatically handles the integration process. The system automatically generates the necessary SPARQL function definitions and integrates them into the query engine, eliminating the need for users to have implementation expertise while still allowing computational capability extension.
Solution Approach 2:
The system provides pre-configured function templates and libraries that users can select and customize without from scratch implementation. Common computational functions are pre-built and ready to use, with options for easy customization, thus improving ease of operation while maintaining computational capability.
3Manufacturing precision
If users manually write computational logic in SPARQL syntax, then query specificity is achieved, but time consumption increases
Solution Approach 1:
The system allows users to copy and reuse previously defined computational logic as templates. Once a complex computation is defined once, it can be copied and reused in multiple queries with minimal modification, maintaining query specificity while dramatically reducing time consumption for repeated operations.
Solution Approach 2:
The system enables users to pre-define and store computational logic in a library of reusable functions. Common computations can be prepared in advance and then quickly invoked in queries without rewriting, thus achieving query specificity while reducing time consumption through preliminary preparation.
4Adaptability or versatility
If SPARQL systems support dynamic function integration, then extensibility is improved, but system stability may be compromised
Solution Approach 1:
The system implements validation and testing mechanisms that activate before new functions are integrated into the SPARQL engine. Incoming functions undergo automated syntax validation, semantic checking, and compatibility verification to prevent unstable or erroneous functions from compromising system stability, thus enabling extensibility while protecting reliability.
Solution Approach 2:
The system incorporates feedback loops that monitor the performance and correctness of dynamically integrated functions. When issues are detected, the system can alert administrators, roll back problematic integrations, or adjust system behavior to maintain stability, thus balancing extensibility with reliability through continuous monitoring and adaptive response.
Data Source
AI summary
The present disclosure provides system and method for executing SPARQL query on a SPARQL engine. For executing the SPARQL query, a function may be instantly integrated with the SPARQL query which leads to extension of the SPARQL query. The extension may be achieved through a user friendly interface which may allow transparent integration of code (i.e., the function) in any language such as JAVA, C, C++ and the like, supporting a particular functionality. The system may integrate the code by addition of newly added code to the SPARQL library after validation. Further, the system may analyze the functionality associated with the code to optimize decision making of a user. The system may further support auto compilation and rating of the functions based on the user feedback and re-usability of the code working in a collaborative environment. Further the system may enable also enable to integrate external tools and web services.