Decision Services Handling Large Payloads

On Apr 20, 2022 I shared recent OpenRules experience building decision services capable to handle huge payloads with sound performance. He described how putting a decision service into a cloud-based environment supporting parallel execution allowed a large US corporation improve the performance 100 times! Watch Recording

OpenRules Debuggers

OpenRules Decision Manager provides two Decision Model Debuggers:

While the graphical debugger is the most user friendly debugger, it is currently available only under Windows and Linux. The Command Line Debugger is available under Windows, Mac, and Linux. Continue reading

RESTful Decision Services: Error Handling with OpenRules

Usually input validation is an essential part of business decision models created by OpenRules customers. They add rules that validate input decision variables for null values, empty strings, impossible values like Feb 29 during a non-leap year, and many other problem-specific input errors. These custom error reporting mechanisms usually generate lists of errors with explanations in the JSON response for RESTful decision services. However, while input errors are diagnosed by the decision service, its return code is usually 200 meaning it was a “good” request. In this post I will describe how your service may return code 400 for bad requests.

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Mitigating Log4J Security Vulnerability

On December 12, 2021 Apache Log4J 2.x reported that this widely used Java logging framework has been exposed to a serious security vulnerability. OpenRules Decision Manager like many other Java-based products uses Log4J. To mitigate this problem, we quickly switched to the recommended version 2.15.0 of log4j that was supposed to remove the above vulnerability. However, on December 14 the second vulnerability was discovered and Apache released the version 2.16.0 to address the problem.

Based on the seriousness of these events, we decided to create a new emergency release 8.4.3 of OpenRules Decision Manager that uses log4j version 2.16.0 (not 2.15.0). We’ve already built the first version 8.4.3 and it’s going through thorough testing. For urgent situations we made the evaluation version 8.4.3 available from here. Our team will continue to work hard to make sure that well-tested Release 8.4.3 will be available to all customers tomorrow morning. If you have any questions, please contact support@openrules.com.

Advancing AWS Lambda Decision Services

AWS Lambda has recently extended the capability to track the current state of a function through its lifecycle. In this post AWS wrote: “We’re extending the General Update from September 30 2021 to December 5 2021. The End of Delayed Update date is now also changed to December 6 2021.” With this change, all users of AWS Lambda need to update their AWS SDK-based automation workflows. How would it affect OpenRules-based decision services deployed as AWS Lambdas?

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Decision Services: Free POC Development

OpenRules Decision Manager becomes so powerful and easy to use that it dramatically reduces the efforts needed to develop new decision models and deploy them on cloud as decision microservices. Usually everything is done in Excel with our graphical Explorer without any coding. Along with development of large decision-making applications, our team has great practical experience of rapid creation of working prototypes or Proof of Concepts (POC). After a brief meeting with a customer, we quickly (usually within 1-3 days!) put together a POC tested locally and deployed on cloud, so the customer may start testing it remotely without any installations! Continue reading

OpenRules Release 8.4.2 improves Validation and Consistency Checking of Rule Repositories

Our customers will appreciate the new OpenRules Release 8.4.2 as it essentially improves the validation and consistency checking of their Rule Repositories. It provides much better error diagnostics in business friendly terms. In the Release 8.4.2 we actually implemented a new built-in compiler that runs during the BUILD phase (build.bat), so the majority of errors can be caught before Java code generation. We also added an ability to use CSV-files in situations when XLS-files are too big/slow to handle. We removed Maven Wrapper from all samples and made a free Apache Maven a pre-requisite. Release Notes

When to use Machine Learning, Optimization, and/or Business Rule Engines

Last week I listened the webinar “The Art of Knowing How to Leverage Decision Intelligence” presented by Roy Schulte, Distinguished VP Analyst at Gartner. Describing the major trends in decision intelligence and why it is growing, Roy concentrated on the question “when to use machine learning, optimization or business rule engines”:

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Using CSV Files in OpenRules

When it comes to handling large collections of data in business rules, OpenRules customers frequently use Excel-based DecisionData tables and/or decision tables of the type “BigTable“. It works fine when Excel tables contain thousands or even tens of thousands rows. However, when an Excel file contains hundreds of thousands rows Excel itself becomes much slower to search and requires much more time and memory to be downloaded in OpenRules. In this case we recommend our customers to switch from the Excel to the CSV format.

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Using DecisionData Tables in OpenRules

OpenRules Decision Manager provides all necessary tools to build, test, and debug your business decision models. The same people (subject matter experts) who created decision models can create test cases for these models using simple Excel tables or objects coming from the outside world (from Java, XML, or JSON). This post explains how to create and use test cases and data tables.

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Continuous Digital Decisioning

Modern enterprises quickly evolved from monolithic to microservices architectures and they naturally expect that rules-based decision services to be good citizens of the new CI/CD world. My DecisionCAMP-2021 presentation https://lnkd.in/g_rWAazF shares OpenRules experience of developing, integrating, and deploying operational decision microservices which satisfy the requirements of modern enterprise architectures including security, continuous integration and delivery/deployment. Using specific examples, it explains how business analysts may represent and maintain these requirements in their business decision models.
Keywords: Business Decision Models, Decision Microservice, Invocation Context, Authorized access, Security, CI/CD Watch

Titanic Booking Service

This is the name of the DMCommunity.org Aug-2021 Challenge that is supposed to predict survivors of the Titanic tragedy based on partially known actual results. Dr. Bob Moore has already submitted a very good analysis of this challenge and applied several ML-based approaches to solve it. It inspired me to ponder the same challenge from a bit different angle. I thought I could try two approaches: 1) using common sense or human prejudice-based rules to predict who could survive; 2) using our very simple Rule Learner to let ML algorithms to do the same. I spent almost the whole day today working on this problem, and this post describes what and how I did. It is interesting to compare the final results:

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Operational Decision Microservices in the CI/CD World

On Sep 14 I will present “Continuous Digital Decisioning or Operational Decision Microservices in the CI/CD World” at DecisionCAMP-2021. In this presentation we plan to share recent OpenRules experience of developing, integrating, and deploying operational decision microservices which satisfy the requirements of modern enterprise architectures including security, continuous integration and delivery/deployment. Here are two key slides:

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Securing OpenRules Decision Services

Release 8.4.1 of OpenRules Decision Manager is about “Securing OpenRules Decision Services” – click on this link to see the new tutorial that describes how to secure decision services using JWT Authentication and SSL communication. Release 8.4.1 also provides business users with an additional control over input and output of their decision models allowing them to validate incoming JSON requests and choose which decision variables to exclude from the produced responses. Read Release Notes

Glossary: Controlling Decision Model Input and Output

Glossary with UsedAs-properties

Being at the heart of any decision model, OpenRules Glossary uses columns “Variable Name”, “Business Concept”, “Attribute”, and “Type” to define all used decision variables. It is recommended to add the optional column “Description” with plain English descriptions of these variables.

Additionally, OpenRules 8.4.1 Glossary may include optional columns “Used As” and “Default Value” that allow you to effectively control the input and output of the decision model. It can be especially important when your decision model is deployed as a RESTful web service, and you want to validate the incoming JSON structure and decide which variables should be included in the outgoing JSON structure.

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Glossary at the Heart of the Decision Model

Any practical decision modeling tool allows a user to represent a business glossary that specifies all decision variables and thus is at the heart of any decision model. The DMN standard does not explicitly specify a glossary letting different implementation products to compete. So, the modern digital decisioning systems use different constructs to organize their glossaries. In this post and follow-ups I will describe how OpenRules deals with its most important tables of the type “Glossary” which are always the focal point of any decision modeling process.

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OpenRules Decision Manager 8.4.0

This new Release 8.4.0 is a response to practical needs of our large and mid-size customers. It essentially enhances deployment capabilities including:

Read Integration/Deployment, Release Notes, an updated User Manual for Developers, and try it from here.

Rules-based Machine Learning

The integrated use of Rule Engine and Machine Learning (ML) products becomes more and more popular. OpenRules Rule Learner is a good example of such integration. While there are plenty of powerful Machine Learning algorithms available off-the-shelf, it can be quite practical to use rules-based machine learning instead. The classical rules-based technology may address learning problems considering historical and constantly changing operational data. In such situations a “Rule Engine” plays a role of a “Rule Learner”. Here is a good example.

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OpenRules Growing Popularity

Since the beginning of 2021 OpenRules Decision Manager‘s popularity is growing at an unusually high pace. The last month our website had 33K visitors that is much more than traditional 20K visitors per month. We’ve already closed several very important deals this year, and now several other large customers have successfully completed their POCs building and deploying OpenRules-based decision microservices on their preferred clouds. Here are important factors that contribute to this success:

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Benchmarking Decision Service

For years many Rule Engine customers were satisfied with their performance as the majority of rule engines delivered in general good performance.  However, the picture changed dramatically in the last 3-4 years when the number of rules-based real-time transactions started to grow from hundreds of thousands or several millions to hundreds of millions per day. The typical execution time of 50-100 milliseconds per transaction what used to be considered as a good performance indicator is not sufficient anymore. Nowadays large customers require the performance that goes down to only a few milliseconds per transaction or in many cases under 1 millisecond. This real-world requirement along with the dramatic shift to cloud-based deployment was the main reason why two years ago we redesigned our Classic OpenRules product and built its new implementation known today as “OpenRules Decision Manager“.

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ProcessCon-21

Last week I was on the panel “Hyper Automation and the Convergence of #BPM, #RPA, #Rules, & #iPaaS” of the ProcessCon-21, a User Conference organized by our partner ProcessMaker. Together with leaders from UiPath, SnapLogic, and ProcessMaker we discussed the latest trends in low-cod/no-code development and much more. Watch Video. You may also check out this webinar “Building lightweight composable process applications using rules + workflow

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Rule Debugger for Business Users

Whether you like it or not, your business decision models will contain bugs like regular software always does. They will fail to produce expected results for certain inputs and under different unforeseen circumstances. As a result, their authors, who are usually business people, will have troubles to understand why certain rules were not executed while others did. For real-world business models even the best explanation reports and execution traces could be difficult to follow. When complex logical errors occur, decision model authors discover that their business rules and decision management environment is only as good as the rule debugging tools it offers! Continue reading

New Release 8.3.0 with Graphical Explorer

New OpenRules Decision Manager Release 8.3.0 comes with a Graphical Integrated Decision Modeling Environment called “Decision Model Explorer“. It automatically builds decision model diagrams, opens Excel files associated with different nodes, and allows business people to modify, test, and deploy their decision models from one place. Watch the video and read more. Join our free webinar on Feb 5, 2021 – register here. Continue reading

Java API for Decision Service Invocation

OpenRules Decision Manager deploys decision models as cloud-based decision services such as AWS Lambda with “one-click“. In this post I will explain how to invoke deployed OpenRules services from any Java application. There are at least 3 options:

  • Using the standard Java HttpURLConnection
  • Using DecisionServiceClient API
  • Using automatically generated API

You can try to run all three options using the sample decision project “VacationDaysLambda” included in the standard OpenRules installation. Continue reading

Text Interpolation in OpenRules

Many popular languages (e.g. Angular or Mustache) use text interpolation to incorporate dynamic string values into the text. OpenRules Decision Manager Release 8.2.1 gives our customers an option to use text interpolation by putting text decision variables in the double curly braces {{ and }}.  For example, see how it’s being used in the standard sample project “PatientTherapy”:

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Partnership with ProcessMaker

Today ProcessMaker and OpenRules announced a new partnership that provides our customers with an integrated business process and decision management solution. The partnership adds high performance decision services created in OpenRules and deployed as AWS Lambdas to business applications built with ProcessMaker. It unlocks a powerful new level of sophistication for process, workflow, and business rules designers around the globe. This webinar demonstrates an implementation of a loan origination process in ProcessMaker that utilizes complex decision services built in OpenRules. Read Press Release.

 

RuleDB.com – Accessing Database from Business Rules

Traditionally, Business Rule Engines do not communicate with databases directly and expect to receive input and provide output via intermediate objects defined in Java, JSON, or XML. However, our customers frequently prefer to use business-friendly rules defined in Excel instead of separately defined SQL statements. Our new product “Rule DB” does exactly this. In this post I will describe how it works using the MySQL Sample Database. Continue reading

Rules-based Service Orchestration

OpenRules provides business users with abilities to build and deploy operational decision microservices. Now we empowered business users with an ability to assemble new decision services by orchestrating existing decision services independently of how they were built and deployed. The service orchestration logic is a business logic too, so it’s only natural to apply the decision modeling approach to orchestration. In this post I will explain how to orchestrate different services by creating a special orchestration decision model that describes under which conditions such services should be invoked and how to react to their execution results.

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Compressing Decision Tables

The DMC Challenge Sep-2020 deals with compression of decision tables trying to replace relatively large decision tables with “almost” equivalent but smaller decision tables. It is only natural to apply Machine Learning to this problem as it allows us to automatically discover business rules from the sets of labeled historical data records. So, I decided to use the open source Rule Learner to address this problem. In this post I will describe how I approached this problem with these implementation steps:

  1. Write a simple generator of data instances with various combinations of known attributes
  2. Run the existing decision table using OpenRules to produce labeled instances
  3. Feed the labeled instances to Rule Learner (or SaaS Rule Learner) to automatically discover a new decision table and evaluate its performance.

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SaaS Rule Learner

No alt text provided for this imageFinally, our SaaS Rule Learner became publicly available allowing business analysts to extract business rules from their historical data sets. They can do it without any downloads directly from Amazon cloud using AWS Marketplace SaaS subscription – see https://aws.amazon.com/marketplace/pp/B08HNF1Q5J. Watch this video https://youtu.be/88B5rJa2yrI that describes how to use it for the demo and custom data sets. Try SaaS Rule Learner from http://saas.rulelearner.com. Continue reading

Self-Learning Decision Models

Today I presented “Rule Learner: Self-Learning Decision Models” at the DecisionCAMP Monthly Meeting. It’s about an integrated use of Machine Learning (ML) and Business Rules (BR) within Digital Decisioning Platforms. It describes how “RuleLearner.com” can help business analysts to discover business rules from large historical data sets. Without forcing business analysts to become experts in data science or programming, Rule Learner discovers business rules by naturally incorporating ML algorithms into Business Decision Models. The session focuses on practical aspects of rules generation by developing ever-learning decision-making applications. Video Slides

New Rule Learner for Ever-Learning Decision-Making Systems

OpenRules, Inc. was among first BR vendors who introduced the integrated Machine Learning and Business Rules approach back in 2007.  Over the years, our Rule Learner was successfully used to discover business rules by analyzing large sets of historical data in different problem domains. One of the first success came in the large IRS project “The integrated use of BR+ML technologies” – read more.

Today we introduced a new version of Rule Learner publicly available as an open source product under the terms of the LGPL (it means no restrictions for commercial use!). You can download it for free from RuleLearner.com. It naturally integrates Machine Learning (ML) and Business Rules (BR) techniques by incorporating ML algorithms into rules-based Decision Models. Continue reading

Using Templates to Create Domain-Specific Decision Tables

While DMN-like decision tables are powerful enough to represent business logic for many practical problems, in real-world our customers frequently define new types of decision tables that are specific for their business domains. For example, years ago OpenRules was chosen for a large project that in particular dealt with spatial business rules. Their customers,, suppliers, and operations vary by region, and distance between then affected their decisions. They already used a Geospatial Information System (GIS) in order to explore spatial relationships that leveraged the industry standard Java Topology Suite (JTS) with a powerful Java API.

However, they wanted their business (!) users to natively define and maintain complex spatial rules without becoming experts in specific Java API. This 2014 presentation describes how OpenRules helped this customer to create a Spatial Decision Table template allowing stakeholders with no GIS training to use plain English in familiar Decision Model spreadsheets to define spatially aware business rules without any additional software. Continue reading

Stateful Loan Approval Process with AWS Lambda+EventBridge+SNS+SQS

At DecisionCAMP-2020 we had a lot of discussions about the orchestration of the decision services and how to invoke different decision services when states of the business objects change over time. One of the most popular orchestration technique is a Pub/Sub architecture with State machines. The Challenge “Dynamic Loan Approval” is an example of perpetually running decision-making applications which should be able to learn from already executed transactions and evaluate new facts as they become available. This post describes how we implemented this application utilizing OpenRules-based GUI connected to AWS Lambda, EventBridge, SNS, and SQS. Continue reading

Incorporating Optimization Engines in Business Decision Models

On June 30 I will present “Developing Decision Optimization Microservices for Real-World Decision-Making Applications” at DecisionCAMP-2020. Preparing my presentation, I thought about the major points I want to make. Of course, first of all, I want to demonstrate how to develop optimization services, but I also want to stress how the proposed approach helps to bring already great optimization tools into the everyday reality of business application development. Continue reading

Solution for DMCommunity Challenge “Pay-As-You-Go Pricing Rules”

There are already 3 solutions to DMCommunity May-2020 Challenge. I especially like how Carol-Ann Berlioz created her solution using a test-driven approach when test cases prompt her which rules should be added. So, I’ve tried to reproduce her solution but instead of a product specific GUI I decided to use OpenRules decision tables defined in Excel. So, here are our test-cases with input data and expected results: Continue reading

OpenRules: Enhanced Testing Capabilities

Let’s look how the release 8.2.0 enhanced OpenRules testing capabilities by extending tables of the type “DecisionTest” with new columns:

  • ActionDefine
  • ActionActive
  • ActionComment.                                                                Here is an example:


                                       (You may click on this table to enlarge)
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