PIAR Management Application Data Type Discovery
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Solution Overview
Problem
Current plug-in application recipe management systems require significant user input and are inefficient in generating and executing plug-in application recipes, particularly in identifying and mapping data types and triggers, leading to suboptimal automation of tasks.
Innovation Solution
The system employs machine learning and semantic analysis to automatically discover data types, generate plug-in application recipe extensions, and execute recipes with minimal user input by mapping discovered data types to actions and triggers, using a PIAR management application that integrates with various plug-in applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual user input is used to define PIARs, then user control and precision are improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-service by automatically discovering data types, generating candidate PIAR extensions, and mapping triggers to actions without requiring manual user configuration. The machine learning model autonomously analyzes data patterns and generates recipe extensions based on discovered data types.
Solution Approach 2:
The system performs preliminary actions by pre-discovering data types from data values and pre-generating candidate PIAR extensions before user interaction. This allows the system to prepare multiple potential recipe configurations in advance, reducing the time required for final PIAR definition.
2Productivity
If automatic machine learning-based generation is used, then productivity and automation are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the PIAR generation process into distinct modules: data type discovery, candidate extension generation, trigger-action mapping, and recipe execution. Each module handles a specific aspect of the process, making the overall complex system manageable and maintainable through functional decomposition.
Solution Approach 2:
The system introduces an intermediary machine learning model that acts as a mediator between raw data and PIAR generation. This intermediary component automatically discovers data types and generates candidate extensions, simplifying the interface between data sources and the recipe management system.
3Reliability
If data type discovery and mapping is performed, then automation accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The system changes parameters by adjusting the granularity and depth of data type discovery based on confidence levels. When high confidence is achieved in data type identification, the system reduces further analysis, thereby optimizing computational resource usage while maintaining mapping accuracy.
Solution Approach 2:
The system applies local quality by performing detailed data type discovery and mapping only for specific fields and data values where it is most needed, rather than uniformly processing all data. This selective approach concentrates computational resources on critical mapping tasks.
Data Source
AI summary
Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.