Semantic Data Source Clustering for Software Service Dependency Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data search and analysis technologies are inefficient for large, complex, and distributed data sources due to their dependency on structured data and classification hierarchies, which limits their ability to integrate multiple disparate data sources and identify relevant data for specific applications.
Innovation Solution
A method for generating executable software components that represent clusters of data sources based on semantic associations, allowing for the selection of appropriate data sources to satisfy data dependencies by advertising relevant semantic identifiers and providing an interface for data delivery, without relying on a common ontology or classification hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data search and analysis tools (relational or object-oriented databases) are used for big data, then data storage and retrieval can be performed, but the tools become ineffective or inefficient due to the number, size and complexity of data stores and data items
Solution Approach 1:
The patent segments the complex data search problem into multiple independent components: data source descriptions with semantic identifiers, service requirements specifications, and matching algorithms. This segmentation allows each component to be processed independently, improving efficiency when dealing with large numbers of complex data sources.
Solution Approach 2:
The patent introduces data source descriptions as an intermediary layer between raw data sources and search services. These descriptions contain semantic identifiers that mediate the matching process, enabling efficient search without directly processing the complexity of underlying data stores.
2Adaptability or versatility
If web search techniques with comprehensive indexing and result ranking are used, then search results can be provided with proposed order of relevance, but the approach requires stable data sources conforming to known structure and cannot integrate multiple large, complex, disparate data sources
Solution Approach 1:
The patent creates a universal data source description format that can represent multiple types of data sources (relational databases, file systems, web services, etc.) through common semantic identifiers. This universal structure enables integration of disparate data sources without requiring each to conform to specific structural requirements.
Solution Approach 2:
The patent changes the parameter representation from structural attributes to semantic identifiers. Instead of requiring data sources to have known parseable structures, the system uses semantic identifiers that capture the meaning and relevance of data sources, allowing flexible integration of complex and disparate sources.
3Productivity
If Semantic Overlay Networks with classification hierarchy are used for peer-to-peer networks, then search efficiency is improved by avoiding irrelevant peers, but the dependence on predefined classification hierarchy reduces search effectiveness and requires precise classification of queries and peers
Solution Approach 1:
Instead of classifying queries according to a predefined hierarchy and searching only relevant SONs, the patent inverts the approach by having data sources advertise their semantic identifiers and having the matching algorithm determine relevance. This eliminates the need for precise classification while maintaining search efficiency.
Solution Approach 2:
The patent implements feedback through the matching algorithm that compares service requirements with data source descriptions. The algorithm provides feedback on the degree of match between semantic identifiers and requirements, enabling adaptive search effectiveness without relying on rigid classification hierarchies.
Data Source
AI summary
A data source software component generator apparatus for generating a representation of one or more data sources for selection from a plurality of data sources to satisfy a data dependency of a software service, each data source including a definition of at least one semantic identifier corresponding to data accessible via the data source, the data sources being represented organized into clusters of multiple data sources based on a semantic association between semantic identifiers of data sources in a cluster, each cluster being represented as one or more data structures, and the data dependency being defined by a specification including one or more semantic identifiers corresponding to data required for execution of the software service, the apparatus comprising: a data source encapsulator unit adapted to encapsulate each cluster as an executable software component; a semantic identifier selection unit adapted to select, from a set of semantic identifiers for all data sources represented in a cluster of a software component, a proper subset of the set of semantic identifiers based on at least one predetermined semantic identifier selection criterion; a software component configuration unit adapted to configure a software component to advertise semantic identifiers to components external to the software component, and provide an interface accessible by components external to the software component, the software component being adapted to deliver data from data sources in the cluster of the software component via the interface, such that, in operation, the apparatus generates and configures executable software components for selection of one or more software components to provide data for the software service based on the advertised semantic identifiers so as to satisfy at least part of the data dependency of the software service.