{"id":451760,"date":"2025-03-13T15:00:55","date_gmt":"2025-03-13T15:00:55","guid":{"rendered":"http:\/\/savepearlharbor.com\/?p=451760"},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-29T21:00:00","slug":"","status":"publish","type":"post","link":"https:\/\/savepearlharbor.com\/?p=451760","title":{"rendered":"jBPM as AI Orchestration Platform<\/span>"},"content":{"rendered":"
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Author: Sergey Lukyanchikov, C-NLTX\/Open-Source<\/a><\/p>\n

Disclaimer:<\/strong> The views expressed in this document reflect the author’s subjective perspective on the current and potential capabilities of jBPM.<\/p>\n

This text presents jBPM as a platform for orchestrating external AI-centric environments, such as Python, used for designing and running AI solutions. We will provide an overview of jBPM\u2019s most relevant functionalities for AI orchestration and walk you through a practical example that demonstrates its effectiveness as an AI orchestration platform:<\/p>\n

\"Figure<\/p>\n
Figure 1 Stroke Prediction solution deployed in jBPM is run by the users of OpenEMR<\/figcaption><\/div>\n<\/figure>\n

In Brief: About jBPM<\/strong><\/p>\n

jBPM<\/a> (Java Business Process Management) is an open-source integration platform with a workflow engine at its core. Although the jBPM project defines itself as a BPM (Business Process Management) or workflow engine, toolkit, or suite, we argue that describing it as an integration platform is more precise.<\/p>\n

The features that support this characterization include:<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Exposing business logic implemented in jBPM for external access \u2013 e.g., via REST API:<\/p>\n

\"Figure<\/p>\n
Figure 2 A REST API call to jBPM resulting in a response (ID: 976) with status 201 (\u201csuccess\u201d)<\/figcaption><\/div>\n<\/figure>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Providing an engine for business process automation:<\/p>\n

\"Figure<\/p>\n
Figure 3 Web-based workflow editor, part of jBPM\u2019s business process automation engine <\/figcaption><\/div>\n<\/figure>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Embedding components for interaction with external providers \u2013 e.g., with Python:<\/p>\n

\"Figure<\/p>\n
Figure 4 jBPM\u2019s project-level dependency configurator displaying a dependency record for the Jep extension<\/figcaption><\/div>\n<\/figure>\n

These capabilities enable jBPM to interact with external systems, make automation decisions, and manage callouts to external providers efficiently.<\/p>\n

How jBPM Orchestrates Python<\/strong><\/p>\n

jBPM\u2019s Python integration can be implemented in various ways. Our current approach utilizes Jep<\/a> (Java Embedded Python), a Java extension that allows interaction with Python interpreters. When combined with jBPM\u2019s business process automation (e.g., script task activity), this enables a solid, foundational level of Python orchestration:<\/p>\n

\"Figure<\/p>\n
Figure 5 A script task executing Python code wrapped in Java via Jep within a jBPM business process<\/figcaption><\/div>\n<\/figure>\n

Although Jep enables Python integration, it has usability barrier that may require workarounds during initial implementation.<\/p>\n

A Practical Example: Stroke Prediction<\/strong><\/p>\n

A comprehensive illustrative example of jBPM orchestrating Python is our solution for training AI models and estimating stroke risk in a set of patients<\/a>. In a hospital or clinical setting, physicians use an EMR (Electronic Medical Record) system like open-source OpenEMR<\/a> (or any other software allowing customization of user screens). Stroke prediction requests may originate from various sections of the EMR, typically within a patient\u2019s file:<\/p>\n

\"Figure<\/p>\n
Figure 6 A standard patient file screen in jBPM with a Health AI Solution Catalog<\/figcaption><\/div>\n<\/figure>\n

The physician selects the Stroke Prediction AI solution and submits a prediction request to jBPM, where the AI logic is deployed:<\/p>\n

\"Figure<\/p>\n
Figure 7 The patient file screen displaying stroke risk prediction results<\/figcaption><\/div>\n<\/figure>\n

jBPM processes the request, orchestrating the AI logic and Python computations, and returns the estimated stroke risk to OpenEMR:<\/p>\n

\"Figure<\/p>\n
Figure 8 Process instance logging screen in jBPM displaying a prediction request (ID: 977) from a physician<\/figcaption><\/div>\n<\/figure>\n

The stroke risk is initially calculated in jBPM before being returned to OpenEMR for the physician:<\/p>\n

\"Figure<\/p>\n
Figure 9 The process instance record (ID: 977) in jBPM displays the computed stroke risk to be returned to OpenEMR<\/figcaption><\/div>\n<\/figure>\n

This prediction workflow, when visualized end-to-end, appears as follows:<\/p>\n

\"Figure<\/p>\n
Figure 10 End-to-end flow of stroke prediction across OpenEMR and jBPM<\/figcaption><\/div>\n<\/figure>\n

Meanwhile, an analyst monitoring the AI models\u2019 performance can retrain and publish updated models:<\/p>\n

\"Figure<\/p>\n
Figure 11 Custom Health AI screen in OpenEMR for training AI models via jBPM<\/figcaption><\/div>\n<\/figure>\n

Similar to the physician\u2019s workflow, the analyst selects the AI solution and submits a train\/validate\/ test request to jBPM:<\/p>\n

\"Figure<\/p>\n
Figure 12 Custom Health AI screen displaying outcomes of train\/validate\/test requests<\/figcaption><\/div>\n<\/figure>\n

jBPM logs and executes the training process:<\/p>\n

\"Figure<\/p>\n
Figure 13 Process instance logging screen in jBPM displaying a train\/validate\/test request (ID: 978) from an analyst<\/figcaption><\/div>\n<\/figure>\n

Results are computed and returned to OpenEMR:<\/p>\n

\"Figure<\/p>\n
Figure 14 Process instance record in jBPM displaying computed accuracy metrics to be returned to OpenEMR<\/figcaption><\/div>\n<\/figure>\n

Load testing confirms that jBPM efficiently handles simultaneous user requests for both prediction and model training without bottlenecks or data leaks:<\/p>\n

\"Figure<\/p>\n
Figure 15 OpenEMR screens displaying stroke prediction and model training results from load testing<\/figcaption><\/div>\n<\/figure>\n

What Makes jBPM an AI Orchestration Platform?<\/strong><\/p>\n

Given its ability to orchestrate AI workflows, one may ask: Does jBPM have sufficient surrounding functionality to be considered a full-fledged AI orchestration platform?<\/p>\n

Let\u2019s examine the ecosystem around jBPM. Its \u201cnearest neighbor\u201d is likely Eclipse<\/a>, the preferred IDE (Integrated Development Environment) for jBPM, particularly due to plugins like Eclipse BPMN2 Modeler<\/a>:<\/p>\n

\"Figure<\/p>\n
Figure 16 A jBPM business process edited in Eclipse (offline)<\/figcaption><\/div>\n<\/figure>\n

Eclipse enhances jBPM by enabling offline development. The aforementioned plugins allow developers to perform most tasks locally that jBPM\u2019s web-based client supports. Moreover, as a highly extensible modular IDE, Eclipse provides tools to meet virtually any requirement faced by development teams.<\/p>\n

Another key \u201cneighbor\u201d is PostgreSQL<\/a>, one of several database engines that can be configured to support jBPM\u2019s underlying data structures:<\/p>\n

\"Figure<\/p>\n
Figure 17 Internal jBPM tables deployed in PostrgreSQL<\/figcaption><\/div>\n<\/figure>\n

PostgreSQL\u2019s ability to store jBPM\u2019s technical data unlocks vast opportunities for data mining and optimizing AI solutions deployed within jBPM.<\/p>\n

In this context, it is logical to mention another key \u201cneighbor\u201d \u2013 BIRT<\/a> (Business Intelligence Reporting Tool) \u2013 BIRT is one of open-source BI engines capable of extracting, transforming, and reporting on both technical and business data managed by jBPM:<\/p>\n

\"Figure<\/p>\n
Figure 18 BIRT maintaining reporting structures and visualizing jBPM data<\/figcaption><\/div>\n<\/figure>\n

Together, Eclipse, PostgreSQL, and BIRT, along with jBPM\u2019s core capabilities, form a robust AI orchestration platform that facilitates AI model deployment, execution, and optimization.<\/p>\n

For more: C-NLTX\/Open-Source<\/a><\/p>\n<\/p>\n<\/div>\n<\/div>\n<\/div>\n

<\/div>\n


\u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438 https:\/\/habr.com\/ru\/articles\/890572\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

<\/div>\n
\n
\n
\n
\n

Author: Sergey Lukyanchikov, C-NLTX\/Open-Source<\/a><\/p>\n

Disclaimer:<\/strong> The views expressed in this document reflect the author’s subjective perspective on the current and potential capabilities of jBPM.<\/p>\n

This text presents jBPM as a platform for orchestrating external AI-centric environments, such as Python, used for designing and running AI solutions. We will provide an overview of jBPM\u2019s most relevant functionalities for AI orchestration and walk you through a practical example that demonstrates its effectiveness as an AI orchestration platform:<\/p>\n

\n
Figure 1 Stroke Prediction solution deployed in jBPM is run by the users of OpenEMR<\/figcaption><\/div>\n<\/figure>\n

In Brief: About jBPM<\/strong><\/p>\n

jBPM<\/a> (Java Business Process Management) is an open-source integration platform with a workflow engine at its core. Although the jBPM project defines itself as a BPM (Business Process Management) or workflow engine, toolkit, or suite, we argue that describing it as an integration platform is more precise.<\/p>\n

The features that support this characterization include:<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Exposing business logic implemented in jBPM for external access \u2013 e.g., via REST API:<\/p>\n

\n
Figure 2 A REST API call to jBPM resulting in a response (ID: 976) with status 201 (\u201csuccess\u201d)<\/figcaption><\/div>\n<\/figure>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Providing an engine for business process automation:<\/p>\n

\n
Figure 3 Web-based workflow editor, part of jBPM\u2019s business process automation engine <\/figcaption><\/div>\n<\/figure>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Embedding components for interaction with external providers \u2013 e.g., with Python:<\/p>\n

\n
Figure 4 jBPM\u2019s project-level dependency configurator displaying a dependency record for the Jep extension<\/figcaption><\/div>\n<\/figure>\n

These capabilities enable jBPM to interact with external systems, make automation decisions, and manage callouts to external providers efficiently.<\/p>\n

How jBPM Orchestrates Python<\/strong><\/p>\n

jBPM\u2019s Python integration can be implemented in various ways. Our current approach utilizes Jep<\/a> (Java Embedded Python), a Java extension that allows interaction with Python interpreters. When combined with jBPM\u2019s business process automation (e.g., script task activity), this enables a solid, foundational level of Python orchestration:<\/p>\n

\n
Figure 5 A script task executing Python code wrapped in Java via Jep within a jBPM business process<\/figcaption><\/div>\n<\/figure>\n

Although Jep enables Python integration, it has usability barrier that may require workarounds during initial implementation.<\/p>\n

A Practical Example: Stroke Prediction<\/strong><\/p>\n

A comprehensive illustrative example of jBPM orchestrating Python is our solution for training AI models and estimating stroke risk in a set of patients<\/a>. In a hospital or clinical setting, physicians use an EMR (Electronic Medical Record) system like open-source OpenEMR<\/a> (or any other software allowing customization of user screens). Stroke prediction requests may originate from various sections of the EMR, typically within a patient\u2019s file:<\/p>\n

\n
Figure 6 A standard patient file screen in jBPM with a Health AI Solution Catalog<\/figcaption><\/div>\n<\/figure>\n

The physician selects the Stroke Prediction AI solution and submits a prediction request to jBPM, where the AI logic is deployed:<\/p>\n

\n
Figure 7 The patient file screen displaying stroke risk prediction results<\/figcaption><\/div>\n<\/figure>\n

jBPM processes the request, orchestrating the AI logic and Python computations, and returns the estimated stroke risk to OpenEMR:<\/p>\n

\n
Figure 8 Process instance logging screen in jBPM displaying a prediction request (ID: 977) from a physician<\/figcaption><\/div>\n<\/figure>\n

The stroke risk is initially calculated in jBPM before being returned to OpenEMR for the physician:<\/p>\n

\n
Figure 9 The process instance record (ID: 977) in jBPM displays the computed stroke risk to be returned to OpenEMR<\/figcaption><\/div>\n<\/figure>\n

This prediction workflow, when visualized end-to-end, appears as follows:<\/p>\n

\n
Figure 10 End-to-end flow of stroke prediction across OpenEMR and jBPM<\/figcaption><\/div>\n<\/figure>\n

Meanwhile, an analyst monitoring the AI models\u2019 performance can retrain and publish updated models:<\/p>\n

\n
Figure 11 Custom Health AI screen in OpenEMR for training AI models via jBPM<\/figcaption><\/div>\n<\/figure>\n

Similar to the physician\u2019s workflow, the analyst selects the AI solution and submits a train\/validate\/ test request to jBPM:<\/p>\n

\n
Figure 12 Custom Health AI screen displaying outcomes of train\/validate\/test requests<\/figcaption><\/div>\n<\/figure>\n

jBPM logs and executes the training process:<\/p>\n

\n
Figure 13 Process instance logging screen in jBPM displaying a train\/validate\/test request (ID: 978) from an analyst<\/figcaption><\/div>\n<\/figure>\n

Results are computed and returned to OpenEMR:<\/p>\n

\n
Figure 14 Process instance record in jBPM displaying computed accuracy metrics to be returned to OpenEMR<\/figcaption><\/div>\n<\/figure>\n

Load testing confirms that jBPM efficiently handles simultaneous user requests for both prediction and model training without bottlenecks or data leaks:<\/p>\n

\n
Figure 15 OpenEMR screens displaying stroke prediction and model training results from load testing<\/figcaption><\/div>\n<\/figure>\n

What Makes jBPM an AI Orchestration Platform?<\/strong><\/p>\n

Given its ability to orchestrate AI workflows, one may ask: Does jBPM have sufficient surrounding functionality to be considered a full-fledged AI orchestration platform?<\/p>\n

Let\u2019s examine the ecosystem around jBPM. Its \u201cnearest neighbor\u201d is likely Eclipse<\/a>, the preferred IDE (Integrated Development Environment) for jBPM, particularly due to plugins like Eclipse BPMN2 Modeler<\/a>:<\/p>\n

\n
Figure 16 A jBPM business process edited in Eclipse (offline)<\/figcaption><\/div>\n<\/figure>\n

Eclipse enhances jBPM by enabling offline development. The aforementioned plugins allow developers to perform most tasks locally that jBPM\u2019s web-based client supports. Moreover, as a highly extensible modular IDE, Eclipse provides tools to meet virtually any requirement faced by development teams.<\/p>\n

Another key \u201cneighbor\u201d is PostgreSQL<\/a>, one of several database engines that can be configured to support jBPM\u2019s underlying data structures:<\/p>\n

\n
Figure 17 Internal jBPM tables deployed in PostrgreSQL<\/figcaption><\/div>\n<\/figure>\n

PostgreSQL\u2019s ability to store jBPM\u2019s technical data unlocks vast opportunities for data mining and optimizing AI solutions deployed within jBPM.<\/p>\n

In this context, it is logical to mention another key \u201cneighbor\u201d \u2013 BIRT<\/a> (Business Intelligence Reporting Tool) \u2013 BIRT is one of open-source BI engines capable of extracting, transforming, and reporting on both technical and business data managed by jBPM:<\/p>\n

\n
Figure 18 BIRT maintaining reporting structures and visualizing jBPM data<\/figcaption><\/div>\n<\/figure>\n

Together, Eclipse, PostgreSQL, and BIRT, along with jBPM\u2019s core capabilities, form a robust AI orchestration platform that facilitates AI model deployment, execution, and optimization.<\/p>\n

For more: C-NLTX\/Open-Source<\/a><\/p>\n<\/p>\n<\/div>\n<\/div>\n<\/div>\n

<\/div>\n


\u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438 https:\/\/habr.com\/ru\/articles\/890572\/<\/a>
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