Category Decision Management

Sep 11 2021

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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Sep 9 2021

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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Aug 23 2021

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

Aug 22 2021

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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Aug 8 2021

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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Apr 12 2021

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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Mar 22 2021

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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Dec 4 2020

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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Nov 24 2020

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.

 
Nov 13 2020

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 →

Oct 17 2020

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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Oct 5 2020

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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Aug 25 2020

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 →

Jul 26 2020

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 the real world our customers frequently define new types of decision tables that are specific to their business domains. For example, OpenRules was chosen for a large project that in particular dealt with spatial business rules. Their customers, suppliers, and operations vary by region, and the distances between them were important considerations for making decisions. They already used a Geospatial Information System (GIS) 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.

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Jun 26 2020

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 →

Jun 12 2020

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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Jun 10 2020

Decision Model Execution: Improved Explanations

Traditionally when we ran an OpenRules-based decision model using a script “test.bat”, a user received an execution protocol on the console view, a “black” screen. While it showed all executed rules, it still was difficult for a user to find all changes especially when there are too many rules. The latest release of OpenRules Decision Manager essentially beatifies rules execution protocols and reports showing all rules in a hierarchical order and highlighting the variables which values that were actually changed. Continue reading →

May 11 2020

OpenRules: Performance goes 10x Up, Cost goes 10x Down!

Congratulations to our development team and our customers!

OpenRules Decision Manager 8.1.2 is out and it comes with two big good news:

PERFORMANCE UP. Now OpenRules supports a new type of decision tables called “BigTable“. It’s created for decision tables with thousands of rules, and such big tables will be executed 10-100 times faster! The new execution mechanism is based on a self-balancing binary search algorithm and shows a fantastic speed improvement: decision tables with more than 20,000 rules are being evaluated within 1 millisecond! Try it yourself by clicking on this link. It will execute this big decision table deployed as AWS Lambda.

COST DOWN. Our customers deploy OpenRules decision services using multiple cloud-based microservices or smartphone apps that cannot be expensive. We listened and decided to cut run-time license fee 10 times! Now our customers using only one development license may create and deploy hundreds of decision services and still pay a very low cost:

  • Development License including Technical Support and one Run-Time license: only $4,995
  • Run-Time Licenses for multiple production instances such as AWS Lambda decision microservices or smartphone installations: starting at $500 and going sharply down as the number of licenses increases – see new pricing.

Try new release for free now!

Mar 23 2020

Deploying Decision Models to AWS EC2 Instance

You may find many ways to configure an AWS EC2 Instance and deploy an executable jar-file to this instance. To configure an AWS EC2 Instance you should go through several steps including setting up secured access. In this post we will describe a simplified way how to quickly:

  • Launch an AWS EC2 instance
  • Deploy an executable JAR with a decision service to this instance
  • Test the deployed service remotely. Continue reading →
Mar 23 2020

Packaging Decision Models into an Executable JAR

OpenRules business decision models can be deployed as a RESTful web service with a single click effectively utilizing SpringBoot and Maven – read how to do it here. In this post we will describe how you can package a business decision model into an executable JAR-file that can be deployed on the local server and tested from POSTMAN or Java. Continue reading →

Jan 10 2020

Regression Testing

OpenRules Decision Manager 8.1.0 supports regression testing to confirm that a recent change has not adversely affected existing features of multiple decision models. Regression testing is done to make sure that new changes should not have side effects on the existing functionalities. It ensures that the old functionality still works once the new changes in decision models are done. Continue reading →

Jan 9 2020

Debugging Business Decision Models

OpenRules Decision Manager 8.1.0 includes a special Rule Debugger that allows business users to debug their decision models while they are being executed. The debugger stops execution after executing the first selected rule and a user can analyze the current content of all decision variables to understand why certain rules were executed or skipped. After pushing “Enter” the next selected rule will be executed. A user may continue to push “Enter” until all rules are executed. Continue reading →

Oct 2 2019

One-Click AWS Lambda Deployment

OpenRules business decision models can be deployed as operational decision services utilizing the Serverless architecture provided by Amazon Web Services (AWS), the most powerful and popular cloud platform in the modern world.  When you deploy your decision models as AWS Lambda functions, you don’t even think about servers and pay only for the execution time your services actually consume. Continue reading →

Sep 2 2019

Stateful Decisioning and State Machines

Nowadays it’s not enough for decision-making applications to simply execute a complex rules-based transaction, forget about it, and wait for the next one. These applications frequently have to maintain states of the business objects they manage and and based on their states invoke different decision services. Orchestration of the decision services becomes a serious issue but different implementation techniques frequently utilize State machines. In this post we will use the DMCommunity Aug-2019 Challenge to demonstrate how it can be done. Continue reading →

Aug 20 2019

Rules Debugger for OpenRules Business Decision Models

When our customers create business decision models, they frequently want to have an ability to debug their business rules to understand when and why their rules were executed or skipped. We provided them with the Decision Model Analyzer that partially answers these questions but does it after (!) the model has been already executed. We’ve just completed the development of a new OpenRules Rules Debugger that allows a business user to debug her/his decision models while they are being executed. Continue reading →

Aug 15 2019

Open-Source BR Vendors Moving to the Serverless World

I published an article “Business Decision Models are moving to Serverless World“. In particular, it says:
Two major open-source products, Red Hat Drools and OpenRules, already announced the availability of their new implementations:

  • Red Hat is turning Drools into a first-class serverless component and has introduced Kogito that embraces the Quarkus framework and GraalVM’s for super-fast startup times and low memory footprint;
  • OpenRules introduced a brand-new product Decision Manager that executes exactly the same decision models as the classical OpenRules BRDMS, but uses a completely new execution mechanism that doesn’t need Excel-based Rules Repositories in run-time anymore as it converts all rules to Java ahead of time. As a result, decision services also can startup almost immediately, be executed within milliseconds, and have a minimal memory footprint.              Link
Aug 1 2019

OpenRules Decision Manager

OpenRules announced an availability of the new product “Decision Manager” that has been developed specifically for modern enterprises allowing their business analysts to create, deploy, and manage Business Decision Services on cloud, on premise, or even on smartphones. It comes with a completely new execution mechanism that is extremely fast, takes almost no time to start, and essentially reduces the memory footprint. It perfectly fits the requirements of modern containerized decision microservices.
OpenRules also announced the availability of the new release 8.0.0 of its Classic OpenRules BRDMS that includes many new features requested by customers – Release Notes 8.0.0.  It is important that both products can efficiently execute the same Business Decision Models created using MS Excel or Google Sheets in accordance with OpenRules Goal-Oriented Decision Modeling approach described in this book.

May 8 2019

Decision Microservices with Spring Framework and OpenRules

OpenRules Release 7.0.1 provides a sampling and detailed tutorial of how to add an OpenRules-based service to the popular Spring framework. The new tutorial “Developing Decision Microservices with Spring Boot and OpenRules” in a step-by-step manner explains how to convert OpenRules-based decision projects into Decision Microservices and to deploy them on any server or a cloud environment supported by Spring. Read it and try to run the demo microservice “GreetingService” by downloading the new workspace called “openrules.spring” now included in the evaluation version.

Jan 29 2019

Building and Using Domain-Specific Libraries of Decision Models

Our customers frequently build not one but multiple decision models for their business domains like property and casualty insurance, loan origination, medical guidelines, etc. After building several decision models, they already have a quite rich glossary and various decision tables that essentially cover their business domain. So, it gives them a good foundation to build a library of relatively small decision models which can be used to assemble more complex decision models. Sometimes they even add domain-specific decision tables and supporting Java classes. This PDF document uses well-known loan origination problems (described in the Chapter 11 of the DMN specification) to explain how to build and utilize a library of decision models. Link

Jan 22 2019

Which changes we may expect in 2019

I was asked by BPM.com to share my thoughts of what to expect in 2019. Digital Decisioning and DMN will continue to play an essential role in BPM. I can see two major trends in this development:

  1. Simplification. Representation of decision logic within business processes will be de-facto standardized using mainly simple DMN concepts such as decision tables and avoiding complex programming concepts. The simplified approaches such as “Goal-Oriented Decision Modeling” supported by OpenRules will continue to prevail in development of decision models incorporated into real-world business process models.
  2. Addressing Complex Decision Optimization Problems. So far, human decision modelers were forced to describe exactly HOW to find a decision by handling all possible combinations of business factors using business rules with multiple exceptions on top of exceptions. More powerful decision engines will allow decision modelers to concentrate on WHAT instead of HOW and will automatically determine multiple feasible decisions and select the optimal decision.

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Dec 31 2018

Migrating to OpenRules

OpenRules was introduced to general public as an open source product 15 years ago, and over these years it has become one of the most popular business rules and decision management systems. Every day OpenRules helps customers worldwide handle millions of transactions in real-world production environments for large corporations, government agencies, hospitals, and online businesses – see the list of selected customers. In 2018, along with the Business Rules Excellence Award, we received an overwhelming number of requests from organizations wanting to migrate their existing rules-based systems to OpenRules. This growing interest can be explained by this quote from Forrester Research: “OpenRules have the most-aggressive approaches to business-expert authoring and typically requires less developer support than IBM ODM, FICO Blaze Advisor, and Red Hat Drools“.

So, staring January-2019 we are essentially expanding our technical support and consulting services to help our customers move to OpenRules faster and provide them with superior support. On December 31, 2018 we announced new OpenRules Migration Services – click here to learn how they work.

Nov 15 2018

“Model-based” vs. “Method-based” Approaches to Decision Modeling

In Aug-2018 Prof. Robert Fourer gave a tutorial “Model-based Optimization“, in which he compares two essentially different approaches to modeling optimization problem: “model-based” vs. “method-based”. He is using a relatively complex “Balanced Assignment” problem to demonstrate his points. While Fourer’s tutorial deals with optimization, I believe the same arguments are directly related to Decision Modeling that so far mainly remains method-based. During DecisionCAMP-2018 we had interesting (and sometimes hot) discussions about these two approaches and in my closing remarks I described the major differences between them as follows: Continue reading →

Mar 16 2018

Decision Management and Semantic Reasoning

Considering the upcoming conferences, I was asked by Harold Boley to write about a possible integration of Semantic Reasoning and Business Decision Management. Today I posted an article at the RuleML Blog and decided to reproduce it here as well but in a bit more friendly format.
On September-2018 DecisionCAMP and RuleML+RR will be co-located again for the third time during the LuxLogAI-2018 summit in Luxembourg. These two events represent different but closely related fields of the knowledge representation movement: Business Rules & Decisions Management and Semantic Reasoning. In this post I want to talk about relationships between these two fields and events.

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Feb 24 2018

Happy 15th Birthday, OpenRules!

Today is exactly 15 years since OpenRules, Inc. was incorporated on Feb. 24, 2003. It’s a quite serious milestone, so I decided to write a few words for this occasion. A year ago, I described a brief history of our company and key factors that made it successful. 2018 was an extremely successful year for OpenRules as well: we improved the product and many major corporations became our new customers. But in this post I want to look to the future and to share some of our upcoming and long term plans. Continue reading →

Oct 20 2017

Decision Modeling: LEGO Style

Recently I helped one of our larger customers to build a set of their business decision models. At the end I said that now they can continue to assemble new decision models themselves. Then one of their business analysts said that our decision modeling approach reminds him of LEGO. I believe this is a really good analogy and I will elaborate on it using specific examples.

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Jun 22 2017

Transforming DMN XML to OpenRules

DMN-OpenRulesOpenRules already can read and execute decision models represented in the DML XML format. To test new DMN-to-OpenRules capabilities I decided to implement DMCommunity’s June-2017 Challenge using the DMN Modeling tool from Trisotech that allows non-technical people to create DMN-compliant decision models and import them to the DMN XML format. My objective was to create DMN XML for the Loan Origination problem described in the Section 11 of the DMN specification and then execute this DMN XML using OpenRules. Continue reading →

Jun 21 2017

Loosely Coupled Decision Models for Loan Origination

DMCommunity.org June-2017 Challenge is looking for the best decision models that implement a well-known Loan Origination problem described in in the Section 11 of the DMN specification. So, I decided to address this challenge using the core DMN constructs implemented in OpenRules. I will describe my solution in the form of dialog between a fictional READER who is assumed to be a business analyst (not a programmer) and the AUTHOR, who represents myself. It is similar to the dialog-sessions described in my recent book “DMN in Action with OpenRules“. While it may look long it doesn’t omit any implementation detail. Besides serving as a good solution for the Challenge, this document describes a good decision modeling practice for OpenRules customers.
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Apr 24 2017

Solving Flight Rebooking Challenge With and Without Programming Constructs

DMCommunity.orgFlightRebookingImage announced its Challenge “Rebooking Passengers from Cancelled Flights” in Oct-2016 and until now 4 different solutions have been submitted. It is a relatively complex use case for decision modeling. In this post I will describe different implementation approaches for this challenge and will discuss good and not so good DMN constructs used to support the underlying decisioning logic. I hope this post will initiate more discussions which may lead to the future DMN improvements by replacing the existing programming constructs to more business-friendly representations. Continue reading →

Apr 18 2017

DMN: Iterating Over Arrays Without Explicit Loops

Iteration1I consider myself among the initiators and big supporters of the DMN standard, and I do my best to help bringing the standard to the real-world business decision modeling. Naturally, the current release of DMN includes some constructs with which I strongly disagree but I am trying not to criticize DMN too much as the standard itself is not mature enough yet and I hope it will be gradually improved based on the real-world acceptance. Continue reading →

Mar 25 2017

Raymond Smullyan’s Retro-Analysis and Decision Modeling

When I learned that the famous Prof. Raymond Smullyan passed away this February at the age of 97, I felt grateful to the man whose books and puzzles my friends and I enjoyed reading as young programmers many years ago. Later on we shared them with our children. I wanted somehow to mark this event and decided to buy his book “The Chess Mysteries of Sherlock Holmes” to read on vacation. Ten days ago I started to read the book during my flight from Newark to Jamaica and… haven’t even noticed as we landed. Continue reading →

Feb 28 2017

A new book “DMN in Action with OpenRules”

bookcoverprintThis month I published a new book “DMN in Action with OpenRules” at Amazon. This is a practical guide for people who want to understand how to create and maintain business decision models. The guide is based on the DMN standard and OpenRules. It is oriented to business analysts looking to build operational decision models for their own business environment.

Continue reading →

Feb 6 2017

OpenRules Implementation of the Loan Approval Challenge

loanapprovalimageIn Dec-2016 DMCommunity.org published a new Challenge called “Loan Approval“. This challenge actually was proposed by myself based on real-world experience with one of our customers (a large bank). The most important point of this challenge was a general architecture that allows to invoke the same business logic based on real-time events coming from external sources. I described how such architecture may look like at this video that demonstrates a decision engine integrated with a PUB/SUB message broker.

However, the business logic itself also may be interesting especially if implemented using the latest DMN approach. So, I decided to do a new implementation that is described below. Everything is defined in Excel tables with zero Java code. Continue reading →