Semantic Web Data Extraction for ERP Systems
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Solution Overview
Problem
Existing systems lack efficient methods to integrate data from the Semantic Web into enterprise resource planning (ERP) systems for processing and updating purposes, such as storing, generating messages, or triggering workflows.
Innovation Solution
The integration involves coupling data sources compliant with the Resource Description Framework (RDF) specification to the Semantic Web, using query languages like SPARQL, SquishQL, and Triple to extract data, which is then processed by an ERP system through a feed reader or external extractor, and forwarded to the system for storage or workflow execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is extracted from Semantic Web using query languages like SPARQL, then data extraction capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary data extraction layer that sits between the Semantic Web and the ERP system. This layer uses standardized query languages (SPARQL, SquishQL, RDQL, Triple) to extract data from RDF-compliant sources and transforms it into formats suitable for ERP processing. The intermediary handles the complexity of semantic web queries, shielding the ERP system from direct interaction with complex semantic data structures.
Solution Approach 2:
The system segments the data extraction and processing functionality into distinct modules: query components for extracting data using various RDF query languages, feed readers for RSS-based data sources, and external extractors for specialized data sources. Each segment handles specific types of data extraction tasks, making the overall system more manageable and adaptable to different data sources.
2Quantity of substance
If multiple data sources compliant with RDF specification are coupled to Semantic Web, then data availability is improved, but data integration complexity increases
Solution Approach 1:
The patent implements a universal data extraction framework that can handle multiple types of RDF-compliant data sources through a common interface. The system supports various query languages (SPARQL, SquishQL, RDQL, Triple) and multiple extraction methods (feed readers for RSS, external extractors, direct querying) that can work with any RDF-compliant data source, eliminating the need for source-specific integration logic.
Solution Approach 2:
The system changes the parameter of data source connectivity by supporting multiple query languages and extraction methods that can be configured based on the specific data source being accessed. This allows the same underlying ERP system to adapt to different data sources by changing the extraction parameters (query type, language, method) rather than requiring different integration architectures for each source.
3Loss of time
If data is regularly extracted and processed by ERP system, then data freshness is improved, but resource consumption increases
Solution Approach 1:
The patent implements periodic data extraction where the system regularly queries the Semantic Web for updated data at scheduled intervals. The feed readers and external extractors can be configured to check for new data at specific frequencies, balancing the need for fresh data with resource conservation by not continuously monitoring all data sources.
Solution Approach 2:
The system includes event-driven components that can automatically trigger data extraction and processing workflows when specific conditions are met, such as when new data is available or when threshold values are exceeded. This self-service approach allows the ERP system to process data on-demand rather than following a rigid schedule, optimizing resource usage based on actual business needs.
Data Source
AI summary
Systems, methods, and computer program products are provided for data processing. In one exemplary embodiment, a method is provided that includes extracting data from a Semantic Web and processing the data with an enterprise resource planning system.