Analytics Engine for Multi-Language Task Execution Sequencing
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Solution Overview
Problem
Conventional computing systems face inefficiencies when executing tasks written in different programming languages, as the dependencies and sequence of execution are unknown, leading to a time-consuming and manual process of verifying data manipulation and output generation.
Innovation Solution
An analytics engine determines the sequence of tasks by matching input and output parameters, instantiating processes specific to each task's language, and automatically executing them until the final output is generated, allowing for sequential or parallel execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tasks are written in different programming languages, then language versatility and adaptability are improved, but dependency detection and execution sequencing become unknown and complex
Solution Approach 1:
The patent introduces a task manager as an intermediary component that sits between the diverse programming language tasks and the execution environment. This task manager abstracts the complexity of multi-language dependency detection and execution sequencing, allowing tasks to be written in different languages while maintaining a unified execution model. The task manager handles the mediation between incompatible language interfaces and the execution system.
2Reliability
If manual verification of data manipulation and output generation is performed, then execution reliability is improved, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements self-service through automatic dependency resolution and execution sequencing. Instead of requiring manual verification of data manipulation and output generation, the system automatically detects dependencies between tasks, determines execution sequences, and coordinates data flow between tasks. This automation allows the system to verify its own execution correctness without human intervention, significantly reducing time consumption while maintaining reliability through systematic execution control.
3Productivity
If automatic execution of multi-language tasks is implemented, then productivity is improved, but system complexity and difficulty of detecting dependencies increase
Solution Approach 1:
The patent applies parameter changes by transforming the dependency detection problem into a parameter-matching problem. Instead of attempting to understand complex semantic dependencies between multi-language tasks, the system represents task dependencies through standardized input/output parameter definitions. By changing the representation from semantic complexity to parameter-based interfaces, the system can automatically detect dependencies through parameter matching, significantly reducing detection difficulty while enabling automatic execution and improving productivity.
Data Source
AI summary
Systems, methods, and computer program products for executing a sequence of tasks are provided. An analytics engine receives a request to execute a group of tasks, where tasks execute in different programming languages. A sequence for executing the tasks in the group of tasks is determined using the input and output parameters of the tasks. The tasks are executed according to a determined sequence. To execute each task, the analytics engine instantiates a process that corresponds to programming language associated with the tasks. The process executes the task. Once all tasks complete execution, an output of the last task is a result of the group of tasks.