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:
- Write a simple generator of data instances with various combinations of known attributes
- Run the existing decision table using OpenRules to produce labeled instances
- Feed the labeled instances to Rule Learner (or SaaS Rule Learner) to automatically discover a new decision table and evaluate its performance.
Continue reading →
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
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. Continue reading →
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 →
Listening several recent presentations of Pragmatic Dave Thomas (see this and this), I cannot help getting back to the fundamental decision modeling problem: Rules vs. Tacit Knowledge (or Intuition). Continue reading →
Question: Should we worry that we’re building systems whose increasingly accurate decisions are based on incomprehensible foundations?
I’ve just posted an article with the same name at the LinkedIn Pulse that addresses this question. It is especially important in the context of rules-based decision making when rules that govern our decisions have been automatically generated using predictive analytics techniques. I shared two examples from OpenRules experience that explain why the automatically generated business rules should be comprehensible. The first one talks about the use of our Rule Learner at IRS. The second example deals with our Rule Compressor.
“Big Data” have brought “Predictive Analytics” (long-time available but hidden in the academic world under the names “Machine Learning” and “Data Mining”) to the spotlight of the modern Business Analytics. These days you will find many examples when analytics enables business decisions by supporting a path from data to decisions and actions. Below I will briefly talk about nowadays positioning of the Business Analytics and more about OpenRules own experience in this area including OpenRules Rule Learner. Continue reading →
Modern decision management techniques enable business decisions by supporting a path from data to decisions and actions. Wherever people use stand-alone Business Rules, Complex Event Processing, Predictive Analytics, Optimization systems or their combinations, they prefer to put in charge subject matter experts and not software developers. Actually, all these systems tend to be declarative and allow customers to feed their systems with externally maintained business knowledge, e.g. historical data and/or already known business rules. Nowadays people in a way want to educate a general purpose system with their domain-specific knowledge avoiding traditional programming. Continue reading →
This year RuleML-2014 will be held in Prague on Aug 18-20. For the first time it will include a special track called “Learning Business Rules from Data”. As a member of the organizing committee, I posted the proper announcement here. It promises to become a very interesting event when the decision management practitioners meet their academic partners. Continue reading →
With a new release 6.2.6 of OpenRules® you may generate Excel files with multiple decision tables using a simple Java API. It includes class DecisionBook that corresponds to one Excel workbook (an xls-file), to which you may add multiple OpenRules® decision tables. Continue reading →
The integration of different decision making techniques finally is finding its home under the roof of the Decision Management movement. I am glad that an integrated Constraint Programming (CP), Business Rules (BR), and Machine Learning (ML) approach is gaining in popularity as well. An interesting workshop “CoCoMiLe 2013 – Continue reading →
James Taylor posted several articles devoted to OpenRules BDMS:
– General Overview
– Rule Solver
– Rule Learner.
I just want to add a news which we have not shared with James yet: our latest release of Rule Learner can also present automatically discovered rules in the PMML format.
On January 8, 2010 after the notorious “underwear bomber” attack Tom Davenport wrote:
“How easy is it to connect the dots? Granted, there were numerous indications of Abdul Mutallab’s evil intent. But it would have been difficult to put them together before the flight. Combining disparate pieces of information about people – whether they are customers or terrorists – is akin to solving a complex jigsaw puzzle.” Continue reading →