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:

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:

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.

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.

OpenRules POC: Implementation Roadmap

As OpenRules Decision Manager experiences growing popularity, many potential customers want to develop a POC (Proof of Concept) before committing to our product. Here is our typical POC implementation roadmap:

POC Implementation Roadmap

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:

• Deploying decision models as MS Azure Functions
• Building RESTful web services with OpenRules REST
• Building custom AWS Lambda functions to support CI/CD Pipelines
• Creating Docker images
• Automatic generation of JSON test cases and more.

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

Where is Gold?

In this post I describe an OpenRules solution to the puzzle offered as DMCommunity.org June-2021 Challenge:

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.

Comparing Rule Engines Performance

On June 9 at the DecisionCAMP session Dr. Bob Moore presented his performance comparison results for various solutions to http://DMCommunity.org benchmark. Watch his session at YouTube https://youtu.be/yqmoAUNZA98.

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:

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“.

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”

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!

March 5 Webinar

The next webinar “Advanced Decision Modeling with OpenRules“ will run on Friday, March 5, 2021 at 12:00 PM (New York Time). Register for free here. Watch recording

Upcoming OpenRules Webinars

We will run a webinar series about OpenRules products starting on Feb 5, 2021:
“Decision Modeling with OpenRules Explorer – Introduction” – Free Registration
Watch Recording

Next Webinars:

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.

Using Formulas in Glossary

The new OpenRules Decision Manager 8.3.0 along with “Decision Model Explorer“ includes an ability to define some decision variables by using formulas directly in the Glossary. For instance, in the standard sample “PatientTherapy” we can modify the glossary as follows:

New Year Gift for OpenRules Customers

OpenRules team worked hard to prepare a nice New Year gift for our customers and today I am glad to announce OpenRules Decision Model Explorer! Click HERE to see how it looks.

Yes, it is a Graphical Integrated Decision Modeling Environment that includes automatically built Diagrams (DRDs) for business decision models!

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.

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”:

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.

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.

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.

SaaS Rule Learner

Finally, 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.

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

Using Rule Learner to Identify Unfriendly Robots

In this post I will describe how to use just published Rule Learner to solve the DMCommunity Jan-2019 Challenge. Cyber police has these images of Unfriendly and Friendly Robots:

Rule Learner should discover rules that classify any robots as friendly or unfriendly.

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.

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.

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.

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.

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:

OpenRules Decision Manager Release 8.2.0

On June 15 OpenRules, Inc. released OpenRules Decision Manager 8.2.0 that summarizes major advances made in the first half of 2020 in the following areas:

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)

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.

Goal Invocation using “ActionGoal”

OpenRules decision tables use to column of the type “ActionExecute” to invoke another decision table by simply using its name. For example, look at this decision table:

DecisionMicroservices.com

Recently OpenRules, Inc. registered the domain “DecisionMicroservices.com“. Why did we do it? Because OpenRules Decision Manager dramatically simplifies the creation and maintenance of Operational Decision Microservices! Since we made the first SaaS Rule Engine available in AWS Marketplace on March 3, we experience a strong increase in the number of downloads and requests from the existing customers and prospects who want to develop their domain-specific Decision Microservices.

Development and Run-Time Licenses

Our customers deploy OpenRules decision services using multiple cloud-based microservices, on premise servers, or smartphone apps. Multiple production instances can be too expensive and our customers want to have a clear pricing model for as many decision services as they want to maintain independently how these services are being deployed. We listened and came up with a new pricing model that consists of two license types, Development License and Run-Time License:

Big Decision Tables

For years OpenRules was among the fastest rule engines. When last year we moved from run-time interpretation to design-time code generation, we, like our colleagues at Red Hat Drools, managed to further improve the overall performance and provide support for practical decision microservices. As a result, we dramatically minimized start-up time, went from 50-100 milliseconds per transaction to 5-10 milliseconds, made memory footprint small. These are really good results needed by modern enterprise decision-making systems.

However, I knew that we have multi-year customers that use really big (!) decision tables with 10 and even 30 thousands of rules. How to improve their performance?

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!

Inside/Outside Production Planner

Last week I created a “Worker Planner” that has a nice GUI deployed on Apache Tomcat and it works in sync with a remote Scheduling Decision Service deployed as AWS Lambda.  The scheduling service was implemented with JavaSolver and a constraint solver included into JSR331. My objective was to demonstrate that these days with cloud-based deployment it is not so difficult to create an end-to-end full-scale decision optimization service. I also wanted to show how to apply powerful Linear Solvers to crack traditionally complex production scheduling problems. So, two days ago I took a well-known problem that is described in this example:

Building a Live Worker Scheduler

There are already several good responses to the DMCommunity’s April-2020 Challenge “Doctor Planning”. Below I am describing how I tried to use this challenge to create a complete decision optimization service. I ended up with a working Worker Scheduler that shows a solution for this particular challenge in Fig. 1 (click to open):

Deploying Decision Optimization Models as AWS Lambda Microservices

OpenRules offers two open source products to support an integrated use of “Business Decision Modeling with Rule Engines and CP/LP Solvers“:

• OpenRules – for business decision models
• Java Solver – for optimization models.

In this post I will describe how you can deploy decision optimization models as AWS Decision Microservices without programming or complex configuration.

Immediate Assistance with Your Decision Services

In these uncertain times, to do our part we want to help US businesses, especially health organizations to quickly make important operational decisions. OpenRules offers immediate assistance in creation and deployment of your decisions services on-cloud or on-premise.

• If you need a quick implementation of optimization decision services, we will help you to define and resolve your problem with the freely available Java Solver.
• If you need to quickly create a rules-based decision service and deploy it to AWS cloud, we will give you 3 months free access to our SaaS Rule Engine.

Contact us at support@openrules.com and we will setup a call to discuss how we can be in help. As the CTO at OpenRules, I’d allocate my personal time to help you with your urgent decision automation problems for free – email me directly at jacobfeldman@openrules.com. 

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.

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.

Deploying Decision Models as AWS Lambdas

OpenRules business decision models can be deployed as operational decision services utilizing the Serverless architecture provided by AWS.  Deploying 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.

Deploying Business Decision Models with OpenRules Decision Manager 8.1.1

OpenRules Decision Manager takes an Excel-based business decision model and converts it into highly efficient Java code. When you make changes to the business model, the code will be automatically re-generated, so we do not expect you to even look at this code.  The authors of business decision models will always maintain their original models using mainly Excel.

OpenRules Decision Manager offers several integration and deployment facilities for converting your business decision models to operational decision services on-premise or on-cloud.  We want you to be able to go through the deployment process without programming or complex configuration. This process should be easily repeatable as you will need to do it again and again when you modify your original business decision models. So, we tried to simplify the deployment process as much as possible. As our customers started to explore different deployment options, we plan to post several articles that describe these options in more details using the standard examples included into the OpenRules Decision Manager 8.1.1 installation.