Superfast Performance Confirmed

OpenRules always was among the fastest decision execution engines. Our newest OpenRules Decision Manager was designed to provide super-fast performance for modern enterprise decision-making applications that handle millions of rules-based transactions per day and to naturally support cloud-based microservices. Now we started to receive a very positive feedback from our customers:  after switching to the newest OpenRules Decision Manager the same decision models show 6-10 times performance improvement! If you decide to switch as well, you may take advantage of our Migration Services.

Creating numerical Haiku and Tanka poems with JavaSolver

DMCommunity.org Challenge Nov-2019 asks us to build numerical versions of traditional Japanese haiku poems. Here is an example of a traditional Haiku poem:
(5) The sky is so blue.
(7) The sun is so warm up high.
(5) I love the summer.

A haiku poem consists of three-lines written in a 5/7/5 syllable count. Here is an example of a numerical haiku:
          77    [seventy seven has 5 syllables]
     + 123   [one hundred twenty three has  6 syllables + 1 syllable for “plus”]
     = 200   [two hundred has  3 syllables + 2 syllables for “equals”]

Nathan Brixius proposed a solution using the open-source constraint package written in Python. I really like this problem and decided to ponder this challenge with a free open-sourced JavaSolver. Below is my solution for numerical Haiku and Tanka.

Business Decision Modeling with Rule Engines and CP/LP Solvers

There are two major types of decision engines utilized by different Business Rules and Decision Management systems to execute business decision models:

1. Traditional rule engines (RETE-based or Sequential).
2. Constraint-based rule engines

Another Solution for DMCommunity Challenge “Recreational Fee”

Problem

The DMCommunity Challenge Apr-2019 “Recreational Fee” is based on the following problem provided by Ron Ross:

A city has created a decision table to determine appropriate usage fees for its recreational facilities based on length of usage and when the usage occurs:

Notes from DecisionCAMP-2019

DecisionCAMP-2019 in beautiful Bolzano on Sep. 17-19 will go down in history as one of the most successful DecisionCAMPs. As usual, it was packed with interesting presentations and even more interesting formal and informal discussions. I’ve written my notes from this memorable event – read them here.

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.

Solving the DMC Challenge “CrackTheCode” with JavaSolver

The Sep-2019 DMC Challenge “CrackTheCode” is a simple puzzle. We need to crack a 3 digit code based on these hints:

682 – one number is correct and in the correct position
645 – one number is correct but in the wrong position
206 – two numbers are correct but in the wrong positions
738 – nothing is correct
780 – one number is correct but in the wrong position.

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.

Now Business Analysts can Deploy their Business Decision Models on Amazon Cloud

Nowadays rules-based business decision models are usually developed and maintained by business analysts or other subject matter experts (not by software developers). And more and more people want to make their business decision models available from cloud as operational decision services. But is it possible for non-technical people to achieve this objective? There are so many new terms and concepts to learn, that it seems doubtful for business people to handle this task.

Decision Manager 8.0.1: AWS Lambda Deployment

Business decision models are usually developed and maintained by business analysts or other subject matter experts (not software developers). In the modern world the authors of these decision models want to make them available from cloud as operational decision services. But how difficult is to achieve this objective?

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.

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

Decision Manager – Introduction Video (3 mins)

On Aug. 1 we made OpenRules Decision Manager publicly available for the first time – everybody can download and try its free evaluation version. This 3 mins YouTube video provides a very simple introduction to the on-premise version of our new product that now can execute exactly the same decision models as the classic OpenRules BRDMS.

The major advantage of the Decision Manager is the fact that it is well-positioned to be used on cloud utilizing server-based or serverless architectures.  It perfectly fits the requirements of the modern microservices and is ready to support decision-making applications which handle the millions of complex rules-based transactions per day when every transaction should be executed within milliseconds. The next upcoming release of OpenRules^® Decision Manager will provide a cloud-based UI for analysis, debugging, automatic deployment and execution of decision models as microservices – stay tuned!

Automatic Generation of Java Interfaces for Tested Decision Models

OpenRules Release 8.0.0 adds one more nice feature requested by our customers. Usually business decision models are created by a business person in Excel using powerful OpenRules decision tables. The same people who create the decision models usually create test cases for them using special Datatype and Data tables. After the model is tested, the business person passes the Excel-based model to IT for integration. If their IT uses Java, they need to create Java classes for input and output objects. which are similar to the Datatype tables created by the business person. The release 8.0.0 added an ability to generate such Java classes automatically to provide ready-to-go interfaces for already tested decision models.

OpenRules Java API became even Simpler

The latest OpenRules Release 8.0.0 comes with a new, very simple Java API for incorporation of business decision models created by business people in Excel into Java applications. It corresponds to the simple view of a decision model as “a Business Glossary surrounded by Decision Tables that specify decision logic for Goals and Sub-Goals“. OpenRules 8.0.0 explicitly introduced Java concepts “DecisionModel” and  “Goal” to support the Goal-Oriented Decision Modeling approach described in this book.

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.

Vertical and Horizontal Decision Tables

Traditionally, the majority of decision tables list their rules in the top-to-bottom order when different rules are placed in rows.  Here is an example of a typical single-hit decision table created in Excel in accordance with the OpenRules format:

How to build Decision Microservices with OpenRules, SpringBoot, and Docker

Nowadays, when microservices quickly overcome the legacy style of monolithic applications, it’s only natural to deploy Business Decision Models as Decision Microservices. We’ve just published a new tutorial that in a step-by-step manner explains how to convert OpenRules-based decision models, created and tested by business users, to decision microservices with Spring Boot and then containerize them with Docker. Following the tutorial you will learn how to:

Can ML help with Compression of Large Rulesets?

The integrated use of Machine Learning (ML) and Business Rules (BR) is one of the most practical trends in the development of modern decision-making software. OpenRules is involved in this development for more than 10 years starting with our successful ML+BR projects for IRS. Along with a general purpose Rule Learner, we also provide Rule Compressor, that uses ML to compress large decision tables to smaller ones. This recent presentation explains how it works.

OpenRules at AWS

We are aggressively making OpenRules-based services available from cloud environments such as Amazon EC2. In particular, we’ve just re-deployed our Decision Model Analyzer from a 3rd party remote Tomcat to Amazon. It was just a very simple reconfiguration, but the effect is really positive: the Analyzer is now much faster and much more reliable. You may try it yourself without any registration or fee: simply click on the button on the right.

The source code of the Analyzer is included in the OpenRules standard release and can be considered as an example of how to deploy OpenRules web applications created using OpenRules Dialog to cloud. Another example is the game “Nim” that you may play now from the cloud by clicking on the image below:

Java Solver: Modeling and Solving Optimization Problems

Java Solver is an open source product that provides a minimalistic, simple-to-use Java API for modeling and solving optimization problems. It’s freely available from JavaSolver.com. Download the product, try examples, and use it for your decision optimization problems.

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.

Integrated Use of Rule Engines and Constraint/Linear Solvers

Operational business problems can be defined by a set of decision variables and a set of rules that specify relationships between these variables – see the formal definition. This definition considers a decision as a solution of such a problem, but it doesn’t assume anything about ‘HOW’ how decisions will be produced. It means decisions can be found by applying any rule engine, a DMN engine, a constraint or MIP solver, a custom piece of software written in any programming language, a manually provided expert’s decision, or their various combinations.

Building Decision Models for DMCommunity.org Challenge “Balanced Assignment”

DMCommunity Sep-2018 Challenge “Balanced Assignment” gives an example of a complex business problem with a serious optimization component. This problem deals with the assignment of people to different project groups. Usually, such problems require deep knowledge of optimization techniques. My interest was to build a decision model for this problem and to investigate what can be done by business people and where the involvement of optimization experts is necessary. So, I attempted to use a business-friendly approach to represent and to solve this complex problem. It was not a simple journey, and this article describes what I did successfully and where I failed. Link

Decision Model for DMCommunity Challenge “Recreational Fee”

The latest DMCommunity.org Challenge “Recreational Fee” is very simple, but I still wanted to show how to create the proper decision model using the OpenRules goal-oriented approach. The implementations is described in this PDF document.

A Formal Definition of Operational Business Decision

Dr. Bob Moore in his DMCommunity.org post “What is a ‘Decision’?” considers different definitions of a “Business Decision” and “Business Decision Logic”, which Bob, Ron Ross, and I have recently been discussing in the context of the March DMC Challenge. Below I will share my point of view.

First of all, it’s important to stress that we are talking not about any ‘decision’ but ‘Operational Business Decision’ which provides a solution for an operational business problem. The majority of such problems can be considered as a special case of the classical Constraint Satisfaction Problem (CSP) defined in the famous book by S.Russell and P.Norvig “Artificial Intelligence: a Modern Approach” [5]. So, I simply applied the Russell/Norvig’s definition to our business problem.

A New Book “Goal-Oriented Decision Modeling with OpenRules”

In February-2019 I published a new book “Goal-Oriented Decision Modeling with OpenRules” available from Amazon. It explains the goal-oriented approach to business decision modeling introduced by OpenRules in 2018. The book is a practical guide that explains how to create and maintain operational business decision models in a step-by-step easy to understand style. The guide consists of a series of dialog-sessions in which an AUTHOR explains major decision modeling concepts and methods to an inquisitive READER who asks questions and implements the concepts. You will quickly learn how to represent complex decision logic and end up with a deep understanding of practical decision modeling techniques. The book contains only 150 pages, and you may start developing your own decision models after reading first 1-2 chapters.

Using OpenRules with RESTful Web Services

Based on a request from OpenRules Discussion forum, I asked our support to provide a simple example that demonstrates how OpenRules-based decisions can be invoked from RESTful Web Services. Alex Goldin describes how he did it in this document.

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

My New Year Letter to OpenRules Customers

Dear Customers,

I’m Jacob Feldman, the CTO of OpenRules, Inc. I wanted to reach out to wish you a Happy New Year and thank you for your ongoing support of OpenRules. 2018 was a very successful year for OpenRules. We essentially advanced our decision engine to support a new decision modeling approach. We also received the Business Rules Excellence Award. For those of you who have already experienced the latest OpenRules Release 7.0.0, I hope you are seeing the benefits of the work we did during the last few years to improve the OpenRules robustness, reliability and simplicity of use.

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.

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.

Model-based Optimal Solutions for Flight Rebooking Challenge

During the closing discussion at the DecisionCAMP-2018, I promoted a new “model-based” approach to decision modeling that contrary to the commonly used “method-based” approach allows a human decision modeler to concentrate on “WHAT” instead of “HOW”. This approach gives more power to decision modelers allowing a decision engine to come up not just with one of many possible decisions but to find the optimal decision. I wanted to demonstrate this power using a more complex business problem and to do it before the end of this year. So, I decided to apply the model-based approach to the problem “Rebooking Passengers from Cancelled Flights” that is the most challengeable problem among multiple DMCommunity.org Challenges.

Model-based Solutions for Advanced Vacation Days Challenge

I provided two solutions for DMCommunity.org Challenge “Vacation Days Advanced”. So far, all decision models submitted as solutions for the old Jan-2016 Challenge and the new challenge were “method-based” meaning they describe exactly how to assign extra days while avoiding possible conflicts. I tried to apply a new “model-based” approach that concentrates on “what” instead of “how” and finds not just one of many possible solutions but the optimal one.  The first decision model consists of clearly separated two parts: 1) Business Part presented in Excel decision tables; 2) Technical Part presented in Java using the JSR-331 standard. The second decision model is an attempt to present both parts completely in DMN-like decision tables using Excel only (the supporting Java code is hidden in new Excel templates). You may analyze both solutions here. I’d appreciate comments and suggestions for improvement.

“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:

OpenRules Customer is a Winner of the 2018 Business Rules Excellence Awards

On October 16 Business Rules Excellence Awards (BREA) announced the winners of the 2018 Awards. Congratulations to the OpenRules’s customer, a major California bank, who became a winner! Here is the brief from the BREA’s presentation:

After successfully putting a new OpenRules-based system in production, the bank extended the use of OpenRules to another mission-critical application related to risk management. Below you can read the description of this OpenRules success story.

Notes from DecisionCAMP-2018

This year DecisionCAMP was held in Luxembourg Sep 17-19 as a part of the Logic for AI Summit. It became an important event that attracted experts and practitioners in the business decision management from 14 countries. We had 54 official registrations and sometimes even more people were present in the auditorium. The representatives from almost all major BR&DM vendors and many well-known experts attended the event. As the chair of the event, I wrote the Notes from DecisionCAMP-2018 published at the DMCommunity.org blog.

OpenRules customer selected as worldwide Awards Finalist

A major California bank, facing looming regulatory deadlines, needed to develop a highly dynamic web application to support the bank’s complex customer account management processes. The integrated use of OpenRules’ s rule and rendering engines became the foundation for the successful and quick implementation. OpenRules nominated this real-world application to the first annual Business Rules Excellence Awards (BREA).  This success story was selected as a worldwide Finalist in the 2018 Business Rules Excellence Awards. This is a significant achievement for the Bank and OpenRules. The announcement of winning entries will take place on October 16.

OpenRules Presentations at DecisionCAMP and LuxLogAI-2018

OpenRules was well presented at the major technical business decision management event DecisionCAMP-2018. Here are the presentations directly related to OpenRules:

• Goal-Oriented Business Decision Modeling, Jacob Feldman (OpenRules)
• Automatic judgment of decision authority using OpenRules, Yoshihito Nakayama (Intra-Mart)
• Decision Modeling at AXA CH, Wilfried Kurth (AXA CH)

I also published a paper “Building and Analyzing Goal-Oriented Decision Models” at the Proceedings of the RuleML+RR 2018 Challenge – it explains our goal-oriented approach to decision modeling using a Credit Card Application decision model. In my closing notes I tried to demonstrate how a smarter decision engine can simplify decision modeling using concrete examples from DMCommunity.org challenges. After considering different decision modeling approaches, we will add these capabilities to standard OpenRules features.

DMCommunity Challenge July-2018 “Zoo, Buses, and Kids”

The July’s challenge “Zoo, Buses, and Kids” deals with a very simple optimization problem:
“300 kids need to travel to the London zoo. The school may rent 40 seats and 30 seats buses for 500£ and 400£. How many buses of each to minimize cost?”
Naturally, a constraint solver nicely and easily solves this and more complex constraint satisfaction problems as shown in Philippe Laborie’s solution. When today I saw  a pure SQL solution provided by Damir Sudarevic, I thought that it’s time to model this problem as a business decision model. It would not be as compact as provided solutions, but it should be oriented to business users. 

Decision Automation and Explanations

Prof. Gene Freuder wrote a position paper “Complete Explanations” for The Second Workshop on Progress Towards the Holy Grail that will be held on Aug 27, 2018 during CP 2018 in Lille, France. Gene writes: “As AI becomes more ubiquitous there is a renewed interest in computers being able to provide explanations, and the European GPDR provides special impetus.” Geen’s paper concentrates on constraint satisfaction problems (CSPs), but, as I’d shown in my 2016 paper, we may consider a decision model as a CSP, and everything Gene is talking about directly applies to business decision modeling.

Goal-Oriented Decision Modeling with OpenRules-7

The major Release 7.0.0 is making OpenRules even more user-friendly without losing any of the sophistication of already proven decision modeling technology. Our customers don’t have anymore to define an execution sequence of their decision tables in special tables of the type “Decision” – these tables can be automatically generated. Now it is sufficient for a human decision modeler to define only Glossary and Decision Tables that specify business logic for all included goals and subgoals. Then for any selected business goal, OpenRules Engine will execute all related decision tables in an automatically calculated order to determine the goal’s value. You may watch this short video that uses simple examples to explain the goal-oriented approach to decision modeling supported by OpenRules-7.

OpenRules will Present and Exhibit at BBC-2018 in November

This year OpenRules, Inc. again will be a sponsor, exhibitor, and a presenter at BBC-2018  that will be held on Nov. 6-9, 2018, JW MARRIOTT SAN ANTONIO HILL COUNTRY RESORT & SPA, SAN ANTONIO, TX. The presentation Replacing Legacy Bank Account Management System Using Business Rules will be presented on Nov. 8 together with our customer, one of the largest CA banks.

Keep DMN Simple

People who started decision modeling standardization years ago were all agreed that the DMN standard is supposed to be oriented to subject matter experts, should be simple, friendly enough while powerful to cover the common denominator in operational decision modeling. It was a good start for DMN in 2013 and an enthusiastic acceptance since then. However, today DMN is full of complex, programmer-oriented features which seem to get even more complex in new releases. There are plenty examples of unnecessary complexity being proudly promoted for their “elegance”.  Here are a few examples to compare.

Old Decision Model “Credit Card Application” Implemented with New OpenRules-7

The latest June-2018 DMCommunity Challenge”Credit Card Application” gives me a good chance to demonstrate the new upcoming OpenRules Release 7.0.0. I remember that Nick Broom was one of the first who published a real-world decision model right after the DMN introduction. I also remember that at the same day I implemented and published  Nick’s model using only Excel and OpenRules  available at that time (it was release 6.2.6). It would be interesting to compare this old implementation with a new one below that takes advantages of new OpenRules-7.

DMCommunity Challenge April-2018 “Up-Selling Rules”

In this post we will provide a response to the DMCommunity.org Apr-2018 Challenge “Up-Selling Rules“  that deals with two decision tables: 1) up-selling rules that offer new products to a customer based on the customer profile and the products which the customer already has; 2) customer profiling rules. We will use for the first time the latest OpenRules version (not released yet to public) that removes a need for ordering of multiple decision tables. 

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.

DMCommunity Challenge Feb-2018

There are two interesting responses to the DMCommunity.org Feb-2018 Challenge provided by Mike Parish and Bob Moore. Mike effectively used Corticon to validate and fix incompleteness, redundancy, and ambiguity problems he found in the following, relatively simple decision table:

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.

A New Look of OpenRules.com

Our website www.OpenRules.com has been redesigned. Now it provides a better support for mobile and tablets and is loaded with cool features which hopefully will be appreciated by our customers and visitors. If you notice any issues, please report them to support@openrules.com.