API Mashup Generation via Keyword Sub-Clusters
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Solution Overview
Problem
Developers face a burdensome and time-consuming process when identifying suitable APIs for application development, as they need to manually search across diverse platforms, understand multiple APIs, and verify their compatibility for creating API mashups.
Innovation Solution
A method that groups APIs into sub-clusters based on keywords, identifies keyword combinations using real-world data, and determines possible API mashups, then processes these to generate prioritized mashups through text mining and natural language processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If developers manually search across diverse platforms to identify suitable APIs, then they can find appropriate APIs for application development, but the process becomes burdensome and time-consuming
Solution Approach 1:
The patent segments the large set of APIs into multiple sub-clusters based on keyword analysis. Each sub-cluster contains APIs with related keywords, making it easier for developers to navigate and find relevant APIs without searching through the entire API database manually.
Solution Approach 2:
The patent introduces keyword combinations as intermediaries between developers and APIs. By analyzing real-world data to identify meaningful keyword combinations, the system mediates the search process, automatically matching developer needs with appropriate API sub-clusters and reducing manual search effort.
2Reliability
If developers need to understand multiple APIs and verify their compatibility for creating API mashups, then they can create functional API integrations, but the process becomes complex and time-consuming
Solution Approach 1:
The patent performs preliminary analysis by pre-grouping APIs into sub-clusters based on keyword relationships and pre-identifying keyword combinations from real-world data. This preliminary organization reduces the complexity of compatibility verification during actual API mashup creation, as developers can select from pre-validated groupings rather than verifying compatibility from scratch.
Solution Approach 2:
The patent uses keyword combinations as intermediaries to bridge API compatibility verification. By analyzing real-world data to identify which keyword combinations frequently appear together, the system provides guidance on compatible API pairings, reducing the burden on developers to manually verify compatibility while maintaining reliability.
3Ease of operation
If developers perform manual searches across diverse platforms, then they can identify APIs, but the process requires extensive manual effort and reduces efficiency
Solution Approach 1:
The patent implements self-service by automatically analyzing real-world data to identify keyword combinations and their relationships with API sub-clusters. The system serves itself by autonomously organizing APIs into meaningful groups based on actual usage patterns, eliminating the need for developers to perform manual searches across diverse platforms while improving integration efficiency.
Solution Approach 2:
The patent changes the organizational parameters of API presentation by grouping APIs based on keyword analysis rather than traditional categorization. By using real-world data to determine grouping parameters, the system adapts the API presentation to match actual developer needs, making the search process easier without sacrificing productivity.
Data Source
AI summary
A method of generating application program interface (API) mashups is provided. The method may include grouping a plurality of APIs into a plurality of sub-clusters based on at least one keyword for each API of the plurality of APIs. The method may also include identifying a plurality of keyword combinations for the plurality of sub-clusters based on real-world data and two or more keywords for the plurality of sub-clusters. Further, the method may include determining a plurality of possible API mashups including two or more APIs of the plurality of APIs for the plurality of keyword combinations. The method may also include processing the plurality of possible API mashups to generate at least one prioritized API mashup of the plurality of possible API mashups, the at least one prioritized API mashup associated with at least one keyword combination of the plurality of keyword combinations.