(Click on the presenters or scroll down to see presentation abstracts and author biographies)
- Welcome to DecisionCAMP-2021 by Jacob Feldman, DecisionCAMP Chair (Recording)
- Alan Fish (FICO): A Model-Based Taxonomy of Collaborative Decision System Functions (Slides Recording)
- Matteo Mortari, Daniele Zonca (Red Hat): Trustworthy Decision Management: How explainable, predictive decision making can help us trust our AI models (Slides Recording)
- Denis Gagne (Trisotech): What’s in a name? Business Vocabularies, Business Rules and DMN (Slides Recording)
- KEYNOTE: Ryan Urbanowicz (University of Pennsylvania): Interpretable Machine Learning with Rule-Based Modeling (Recording)
- Vedavyas Etikala, Ziboud Van Veldhoven, Alexandre Goossens, Jan Vanthienen (KU Leuven): Communicating with Decision Models (Slides Recording)
- Jacob Feldman (OpenRules): Continuous Digital Decisioning (Slides Recording)
- Geoffrey De Smet (Red Hat): A modern OO/FP constraint solver to solve VRP, rostering or scheduling problems (Slides Recording)
- Seth Meldon (Progress): Transparency in Decision Making – Explainability and the Limits of Usefulness (Slides Recording)
- KEYNOTE: Alon Halevy (Director at Facebook AI): Symbolic AI in a Machine Learning World (Recording)
- Ron Ross (Business Rule Solutions): What Else Should Rule Platforms Be Doing? (Slides Recording)
- Charlotte DeKeyrel (Decision Management Solutions): How Decision Modeling Reduces Complexity in Regulated Industries (Slides Recording)
- Vincent van Dijk (Pharosius), Annemieke Vliegen (Zoinc): Decision Modeling in the context of the new Environmental and Planning Act (Slides Recording)
- Chris Berg (inrule): Good Beginnings – Problem Discovery and Stating Goals (Slides Recording)
- Simon Vandevelde, Joost Vennekens (KU Leuven) : Unlocking the Full Potential of DMN (Slides Recording)
- Carole-Ann Berlioz (Sparkling Logic): Credit Origination Use Case (Slides Recording)
- Jan Purchase (luxmagi.com), Ryan Trollip (DecisionAutomation.org): DMN On-Ramp: A Pragmatic Approach to DMN Standardization (Slides Recording)
- Rimantas Zukaitis (EIS): Handling Business Rule Variability with Decision Tables (Slides Recording)
Interactive Discussions: moderated by Sandy Kemsley
- “Ask a Practitioner”: Sep 13, 15:00 EST (Recording)
- Bob Moore, JETset Business Consulting
- Mark Woods, Allstate
- James Taylor, Decision Management Solutions
- Carole-Ann Berlioz, Sparkling Logic
- Jan Purchase, Lux Magi
- Nick Broom, Baringa Partners
- “Ask a Vendor”: Sep 14, 16:00 EST (Recording)
- Guilhem Molines, IBM
- Alan Fish, FICO
- Gary Hallmark, Oracle
- Carlos Serrano-Morales, Sparkling Logic
- Denis Gagne, Trisotech
- Larry Goldberg, Sapiens DECISION
- Jacob Feldman, OpenRules
- Mario Fusco, Red Hat
- “Ask a Practitioner”: Sep 13, 15:00 EST (Recording)
Presentation Abstracts and Author Biographies
Model-Based Taxonomy of Collaborative Decision System Functions by Dr. Alan Fish (FICO)
Systems to support organisational decision-making must provide various functions including process, case, decision and data management. The requirements for these can be defined using multiple models, following BPM+ standards. Such models are often complex and interconnected, but using abstract decision models a limited number of underlying collaboration patterns may be identified. These provide a taxonomy to simplify the discussion of possible system designs. Keywords: DMN, Decision Model, Template, Credit.
Dr. Alan Fish is an authority in Decision Modelling and Decision Management, especially in the support and/or automation of organisational decision-making. With over 30 years experience in this field, he has been responsible for many projects at the forefront of current technology. He invented the “Decision Requirements Diagram” (DRD) which exposes the structure of a domain of decision-making, and developed Decision Requirements Analysis (DRA): a methodology for building and using such decision models. He is the author of “Knowledge Automation: How To Implement Decision Management in Business Processes” (Wiley), and a co-author of the OMG specification Decision Model and Notation (DMN).
What’s in a name? Business Vocabularies, Business Rules and DMN by Denis Gagne (Trisotech)
Names are arbitrary labels. So why do we assign so much meaning to names? In certain context, certain names can create confusion. In others, a name can disambiguate the intended meaning being communicated. In the context of business decisions, the names given to various business concepts play a crucial role in providing meaningful and unambiguous business decisions. This becomes even more important in the context of life critical business decisions such as those made in a healthcare clinical context. Medical information systems need to be able to communicate complex and detailed medical data securely and efficiently. This is obviously a difficult task and requires a profound analysis of the structure and the concepts of medical terminologies. In this presentation we will explore the use of disambiguated business terms to express decision requirements and decision logic in DMN. Keywords: DMN, FEEL, Trisotech Digital Enterprise Suite
For over a decade Denis Gagné has been a driving force in the majority of international BPM standards in use today. He is a member of the Workflow Management Coalition (WfMC) Steering Committee, chair of the Business Process Simulation Working Group (BPSWG), and co-Editor of the XPDL 2.2 process definition standard. For the Object Management group (OMG), Denis is the Chair of the BPMN Interchange Working Group (BPMN MIWG), and a member of the Business Process Model and Notation (BPMN), Case Management Model and Notation (CMMN) team and Decision Management (DMN) team.
Communicating with Decision Models by Vedavyas Etikala, Ziboud Van Veldhoven, Alexandre Goossens and Jan Vanthienen (KU Leuven)
The goal of effective decision modeling is to store, process, and execute verifiable decision knowledge in an organization. Decision knowledge when modeled as per Decision Model and Notation (DMN) standard increases the interpretability of that decision. Effective and explainable communication of the decision-making process based on decision models is a challenge to organizations, yet it is necessary in order to improve business value and customer satisfaction.
State-of-the-art Knowledge-based AI technologies and Natural language processing provide us with an opportunity to address this problem. Recent advances in conversational agents could suggest a possible solution by providing a human-like communication of the decision, decision model, and decision-making process. This presentation is about building domain-independent chatbots that understand the user queries, intents, and aids with appropriate decision support with an explainable execution.
Keywords: Knowledge Representation and Reasoning, Decision Model and Notation, DMN, Natural Language Processing, Intelligent Conversational Agents
Prof. Jan Vanthienen received his PhD degree in Applied Economics from KU Leuven, Belgium. He is a full professor of Information Systems at the Department of Decision Sciences and Information Management, KU Leuven and (co-)authored more than 200 full papers in international journals and conference proceedings. His research interests include information and knowledge management, business rules, decisions and processes, and business analysis and analytics. He received an IBM Faculty Award on smart decisions, and the Belgian Francqui Chair at FUNDP. Currently he is department chair at the Department of Decision Sciences and Information Management of KU Leuven.
Vedavyas Etikala is a Ph.D. student in the Faculty of Economics and Business, KU Leuven, researching at the Centre for Information Systems Engineering (LIRIS) in the Department of Decision Sciences and Information Management. His research interests focus on applying Knowledge-based Artificial Intelligence (KBAI) technologies for decision-making in information systems, emphasizing automated knowledge acquisition, modeling, and reasoning. He currently works with Prof. Jan Vanthienen on Project PRODIGY (Process-Decision Integration for KnowledGe-Intensive Process Management).
Ziboud Van Veldhoven is a PhD researcher at KU Leuven, Belgium. His research is focused around digital transformation, digital business models, and the impact of disruptive technologies.
Alexandre Goossens is a Ph.D. student at the Faculty of Economics and Business, KU Leuven, Belgium. His current works deal with decision automation, decision extraction and automated decision modeling.
Trustworthy Decision Management: How explainable, predictive decision making can help us trust our AI models by Matteo Mortari and Daniele Zonca (Red Hat)
The adoption of Machine Learning in conjunction with traditional Decision Management is increasing over the last few years: user data can be easily collected and processed so it is crucial now to leverage similar information to build Intelligent Applications where Machine Learning and Decision Management live together.
Similar integrations can also be achieved by using many different technologies, proprietary or based on open standards. For example DMN (Decision Model and Notation) and PMML (Predictive Modelling Markup Language) are well established standards for the representation of decisions and predictive models and work well together to enable a similar scenario.
In the past, another big barrier of entry for adoption was the cost and the complexity of similar highly data intensive applications; therefore only big enterprises already had the infrastructure to support them, while today’s cloud technologies (public, private and hybrid) make it possible for every company to leverage similar solutions.
Nowadays, the increased demand for transparent, explainable decision making, that is accurate, consistent and effective, has never been greater. Legislations like GDPR are just a result of increasing concerns about privacy, safety and transparency in general. While AI/ML solutions are great at making sense of high volumes of data, the reasoning process for most of the generated analytic models is usually quite opaque.
Explainable AI is a research field that aims to make Machine Learning models more transparent and explainable. In reality the same approaches/techniques can be generalized and applied to Decision Automation solutions in general to provide insights and increase the trustworthiness of the system.
During this presentation, attendees will have the opportunity to learn more about the Explainable AI, learn main concepts/definitions and see how they can be applied to Decision Managed and Hyperautomation solutions that run natively in the cloud.
Keywords: eXplainable Artificial Intelligence, Trustworthy, Machine Learning, Decision Management, DMN
Matteo Mortari is a Software Engineer at Red Hat, where he contributes in Drools development and support for the DMN standard. Matteo graduated from Engineering with focus on enterprise systems with a thesis involving rule engines which sparked his interests and influenced his professional career since. He believes there is a whole new range of unexplored applications for Expert Systems (AI) within the Corporate business; additionally, he believes defining the Business Rules on the BRMS system not only enables knowledge inference from raw data but, most importantly, helps to shorten the distance between experts and analysts, between developers and end-users, business stakeholders.
Daniele Zonca is the architect of Red Hat Decision Manager and TrustyAI initiative where he contributes to open source projects Drools and Kogito focusing in particular on predictive model runtime support (PMML), ML explainability, runtime tracing and decision monitoring. Before that he led the Big Data development team in one of the major European banks designing and implementing analytical engines..
Continuous Digital Decisioning or Operational Decision Microservices in the CI/CD World by Dr. Jacob Feldman (OpenRules)
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. In this presentation we will share 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, we explain 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
Dr. Jacob Feldman is the CTO of OpenRules, Inc., a US corporation that created and maintains the highly popular Business Rules and Decision Management System commonly known as “OpenRules”. He has extensive experience in development of decision-making engines using business rules, optimization, and machine learning technologies for real-world mission-critical applications. Jacob is the DecisionCAMP’s Chair, the manager of DMCommunity.org, and an active contributor to BR&DM forums. He is also the Specification Lead for the optimization standard JSR-331. Dr. Feldman is an author of two books “DMN in Action with OpenRules“ and “Goal-Oriented Approach to Decision Modeling“. He has 5 patents and many publications in the digital decisioning domain.
Decision modeling in the context of the new Environmental and Planning Act by Vincent van Dijk (Pharosius) and Annemieke Vliegen (Zoinc)
In 2022, a new Environment and Planning Act will take effect in the Netherlands. This Act centralizes and standardizes all regulations that apply to the environment from 400 authorities (municipalities, regional and central government). A smart solution – the Digital System for the Environment Act (DSO) – is being created for citizens, businesses and authorities in accessing these regulations to inform, to check the legitimacy of plans or to apply for a permit for planned activities.
This lecture will be about the DSO. It is an ecosystem of components and services based on a model-driven architecture. The core of the system is formed by decision models of the central and various local authorities working together and leading to a coordinated, coherent decision.
The decision models are based on the new DSO standard for decision models, the “Standard Applicable Rules (STTR)”, which is an extension of the DMN specification. STTR is used for modeling decisions, defining data from different sources to be used as input and controlling user interaction. This is known as the STTR layer model for decision logic.
In this session you will learn:
– Why DMN has been extended to STTR;
– How rules of 400 authorities work together;
– How STTR is implemented using the Drools rule engine;
– What challenges we had to overcome to make authorities responsible for setting up their decision models.
This innovative direction for authorities using a model-driven solution is of interest to both business experts and a more technical audience interested in implementation of DMN.
Keywords: Environmental and Planning Act, Decision modeling, Decision model standards, DMN, STTR, Layer model, DMN extension elements, Drools, Business rule engine
Vincent van Dijk is a senior advisor on rule driven solutions and has more than 20 years of experience in the triangle composed of rules, processes and data. His legal background and extensive experience with IT projects with a rule driven approach help Vincent to understand both the business domain and implementation aspects in organizations with a challenge in implementing legislation in their enterprise architecture. In the context of DSO – and thus the topic of the presentation – he has the role of product owner for the business rules management solution.
Annemieke Vliegen is an expert in business rules, both for industry as well as government. She is an experienced decision and process modeler. She has a lot of real-world experience of translating legislation into business rules in various software solutions. Since 2017, she works with several Dutch local governments to model their share of the environment and planning act into business rules, using STTR / DMN.
Good Beginnings – Problem Discovery and Stating Goals by Chris Berg (inrule)
Every project must have a north star. Getting everyone on-board and oriented is an enduring effort that might continue long after a decision management effort underway. How do you get started? How do you know the team is focused on the best opportunities and most urgent problems? What will be the impact to the organization? How do we package our content and justification for stakeholders? This session explores the practices of decision discovery outside the typical boundaries of a model. When employed at the beginning of a project, decision discovery aligns the team, establishes scope, timing and ultimately the business value of the effort.
Keywords: team building, core values, design thinking, strategy, playbacks
Chris Berg Director, Product Strategy and Design Chris has been leading people, products, design and technology for over 15 years in the enterprise software space. He has led significant design explorations (BPM, PaaS, API Management and DevOps) while at IBM and covers more than 10 years of product leadership in the Decision Management space. In much of this time, he focused on transforming business behavior and empowering business users. In his role at InRule Technology, Chris is responsible for product strategy, design and alignment with the market.
What Else Should Rule Platform Be Doing? by Ronald G. Ross (Business Rule Solutions)
It’s obvious that many rules can be broken. Think laws, regulations, contracts, agreements, MOUs, certifications, warranties, etc. Such rules are often one-off and sourced directly in natural language. Rules that people or organizations can break are called behavioral rules. It’s a mystery why two decades into the 21st century rule platforms don’t support them.
A great impedance mismatch exists between DMN / decision rules vs. SBVR / behavioral business rules based on whether evaluation of rules is modeler-invoked or state-based. The burden of validating states of affairs in modeler-invoked evaluation is shouldered by coders. Modeler-invoked evaluation does not work well for behavioral business rules. Not catching violations ASAP causes snowballing errors downstream and thus extensive rework. No wonder business software remains so complex and brittle.
Flash points are the specific events when a rule needs to be evaluated based only on its semantics – i.e., using no external model or specification (e.g., procedural model or decision model). Flash points for the same rule can occur in multiple processes, procedures, use cases, etc., or at various points in ad hoc (unmodeled) business activity. Invoking flash points automatically requires ‘stateful’ platforms. The opportunity for rule platforms in this decade is not just to get smarter, but to eliminate programmer workload – and maybe even programmers(!).
Keywords: behavioral rules, violations, decision rules, DMN, SBVR, rule platforms, flash points, the future for rules
Ronald G. Ross is Co-Founder and Principal of Business Rule Solutions, LLC (www.BRSolutions.com). BRS provides consulting, training and mentoring in support of policy analysis, business rules, concept modeling, decision analysis, and business knowledge engineering. BRS clients have included many 100s of top businesses and government bodies world-wide. Ron is the author of the 2020 groundbreaking book “Business Knowledge Blueprints: Enabling Your Data to Speak the Language of the Business”, featuring concept models, business vocabularies and disambiguation. It is his 9th professional book.
Ron is Chair of the annual Building Business Capability (BBC), official conference of the IIBA®. He is also Executive Editor of BRCommunity.com and its flagship on-line publication, Business Rules Journal. Ron has keynoted dozens of conferences and given seminars to many thousands of people worldwide. Ron co-develops the landmark BRS methodology featuring numerous innovative techniques including the popular RuleSpeak® (free on RuleSpeak.com). These are the latest offerings in a 45-year career that has consistently featured creative, business-driven solutions. Ron is recognized internationally as the ‘father of business rules.’ In 2017 he was co-author with John Zachman and Roger Burlton of the Business Agility Manifesto (www.busagilitymanifesto.org). Mr. Ross holds an M.S. in information science from the Illinois Institute of Technology and a B.A. from Rice University. .
A modern OO/FP constraint solver to solve VRP, rostering or scheduling problems by Geoffrey De Smet (Red Hat)
Traditional constraint solvers don’t fit well with Object Oriented Programming (OOP) or Functional Programming (FP). An OO/FP constraint solver is different: Users input data in their OO domain model, instantiated from their own classes, using annotations to define optimization variables. They are not limited to numeric variables with symptoms of numerical instability. Users write their constraints in a OO/FP code, reusing existing code. They don’t need to translate them into mathematical equations with linear or quadratic limitations. And they can unit test them. In this presentation I‘ll compare traditional solvers to an OO/FP constraint solver like OptaPlanner.
Keywords: artificial intelligence, operations research, constraint solver, java, open source
Geoffrey De Smet is the founder and lead of OptaPlanner (www.optaplanner.org), the open source AI constraint solver in Java that is used across the globe to automatically solve employee rostering, vehicle routing, task assignment, maintenance scheduling, and other planning problems
Organizations in highly regulated industries like healthcare, finance, insurance, and energy are constantly wrestling with how to balance risk and profitability. Compliance and transparency are always top priorities, but so is the bottom line. How do you automate and simplify operations and engage in data-driven decision-making that aligns with regulatory demands and advances positive business outcomes?
Decision modeling can help business teams articulate their most important decisions to help drive continuous improvement and greater efficiencies at scale.
Charlotte DeKeyrel, an ace decision modeler, will explain how it’s done in the real world and share feedback from real clients. You’ll gain insights on how business stakeholders can build easy-to-explain models in a visual format by leveraging the Decision Management methodology.
Keywords: Decision modeling, Decision Management, Regulated industries, Reduce complexity, Data-driven, Continuous improvement
Charlotte is a long-time decision modeler with Decision Management Solutions, a consulting firm focused on the DecisionsFirst™ approach, where she consults with global clients and subject matter experts to elicit business requirements through hands-on modeling sessions and decision management solutions. She has helped companies across all industries leverage technology and their business experts to implement practical efficiencies and transform those organizations into more effective, agile versions of themselves while improving key metrics through continuous improvement. Charlotte is not only an experienced Decision Modeler but also an experienced Engineer in the Defense industry with a degree in Mathematics. She is also a Pragmatic Institute Certified Product Manager.
Most DMN tools only support basic input-output calculation: by starting at the bottom inputs of the DRD, they work their way up through the decision tables. This bottom-to-top approach is considered the standard usage by most DMN practitioners. In this presentation, we show that DMN models can actually be used for much more than this bottom-to-top approach, provided they are supported by a powerful and flexible reasoning engine. Multidirectional reasoning is one of the alternative uses for DMN models. Others include reasoning on sub-decisions, optimization, reasoning with incomplete information, and combinations thereof. We will demo a full-fledged DMN tool combining a DMN editor together with a user friendly interface for our reasoning engine. This interface supports reasoning with incomplete information, reasoning on sub-decisions, multidirectional reasoning and explanation of decisions.
Keywords: DMN, Knowledge Representation and Reasoning, Symbolic AI
Simon Vandevelde is a PhD researcher at KU Leuven campus De Nayer in Sint-Katelijne-Waver, Belgium. His research is focused around user-friendly knowledge representation languages. He is interested in learning more about combining DMN together with powerful knowledge driven reasoning tools. As his first project, he worked together with researcher Bram Aerts to develop the cDMN framework.
Joost Vennekens is an associate professor at KU Leuven Campus De Nayer in Sint-Katelijne-Waver, Belgium. His research is concerned with AI technology (both Knowledge Representation and Machine Learning) and its industrial applications. He belongs to the research group EAVISE, which focuses on AI, computer vision and embedded systems, and to the research group DTAI, which studies declarative languages and AI. He is a member of the board of the Benelux Association for Artificial Intelligence and of the board of the Leuven.AI institute.
Credit Origination Use Case by Carole-Ann Berlioz (Sparkling Logic)
When applying for a credit card or financing a purchase, a full-blown risk assessment must be done. Once the applicant is vetted against ID fraud rules, the credit origination decision leverages self-disclosures and credit bureau data to approve or decline the applicant, and establish the terms of the credit product when approved. In this presentation, we will explore best practices and lessons learned from real-life projects.
Keywords: Credit Origination, Financial Services, Business Rules and Decision Modeling, Case Studies / Lessons Learned, Best Practices, Design Patterns, Tips & Tricks
Carole-Ann is co-founder and CPO of Sparkling Logic, a leader in Decision Management technology. Over her 15+ years in Decision Management, she has consistently brought innovation in the business rules and decision management space, which has been recognized by leading industry analysts Gartner and Forrester. She holds several patents in decision management and adaptive modeling. Carole-Ann started her career building Expert Systems and Business Intelligence dashboards, then specialized in Business Rules / Optimization at ILOG and finally led the vision and direction for Blaze Advisor & Decision Management tools at FICO. She is a passionate & renowned blogger and speaker.
Transparency in Decision Making—Explainability and the limits of Usefulness by Seth Meldon (Progress)
In a 30 hour stretch that spanned the 7th and 8th of February 1904, more than 1,500 buildings were leveled by a fire in the US city of Baltimore, Maryland. Baltimore firefighters were assisted by units from Washington D.C., Philadelphia, Wilmington, and New York City, but the reinforcements had no choice but to stand idly by and watch—none of their fire hoses fit the couplings in use by the city’s hydrants. At that point in the US, there were over 600 variations of fire hose couplings, no city wanted to abandon their investments in their current infrastructure, and manufacturers patented their couplings to prevent competition.
This story is, understandably, touted today on the websites of standards-setting-organizations like the ISTO as their raison d’être, but the narrative is still seen in other forms today. Digitization and intellectual property rights provide the means for contemporary technology vendors to shield highly consequential decision logic from external eyes. Yet with little exception, the domains of technological innovation which emphasize open standard, explainable AI are likewise those which are most innovative, democratic, and utilitarian. This presentation will explore recent case studies which demonstrate the clear value that can be derived from explainable AI, and how today’s versions of fire hose coupling manufacturers seek to obscure that fact.
Keywords: automation, open standards, explainable ai, algorithms, intellectual property
Seth Meldon is the senior solution engineer supporting the Corticon business rules engine in North America at Progress, supporting new and existing users throughout all phases of designing rule-intensive applications. Melding this professional experience with his areas of personal research interest, he has designed and written about solutions leveraging the FHIR standard to improve patient access to their medical data, implementing decision logic for incentive-based community adoption of renewable energy sources, and running decision services as cloud-based functions and in mobile applications. His independent, serial investigative articles and longform environmental history report, The Watershed, are accessible from sethmeldon.com
DMN On-Ramp: A Pragmatic Approach to DMN Standardization by Dr. Jan Purchase (luxmagi.com), Ryan Trollip (DecisionAutomation.org)
During this session, we will discuss the launch of a new DMN-onramp conformance system. This launch is led by the DMN-onramp committee which is a consortium of vendors, academics, and practitioners such as IBM, Sparkling Logic, InRule, Dr. Jan Purchase, Prof Vanthienen and others. The DMN-onramp conformance system gives clarity to what components of the standard would be desirable to accomplish various DMN-related activities, such as: Decision Inventory, Subject Matter Expert Decision Elicitation, Data-Driven Decisions, etc.
The intention of the DMN-onramp conformance system is two-fold. Firstly, to encourage and enable vendors to more easily, and consistently incorporate DMN-standards. And secondly, to provide customers a way to clearly, and visually evaluate a vendor’s DMN features against their needs. For more information on the committee please see: https://www.decisionautomation.org/dmn-on-ramp-group
Keywords: DMN, Interchange, Standards, DMN conformance
Dr. Jan Purchase has been working in investment banking for two decades. He has worked with nine of the world’s top 40 banks by market capitalization. In the last 13 years, he has focused exclusively on helping clients with automated Business Decisions, Machine Learning and Decision Modelling (in DMN). Dr. Purchase specializes in delivering, training and mentoring these concepts to financial organizations and improving the integration of predictive analytics and machine learning within compliance-based operational decisions. Author of a DMN book and training course, he is also the current chair of the DMN On-Ramp Committee.
Ryan Trollip has spent over 25 years leading digital transformations and large complex decision automation initiatives.
Handling Business Rule Variability with Decision Tables by Rimantas Zukaitis (EIS)
One of the use cases for the business rules approach in enterprise information systems is rule-based data validation. It allows externalization of complex validation logic from the code into individual atomic business rule definitions, which can be rearranged and reused depending on the use case. This is especially true for the insurance domain, where a significant part of insurance product-specific customizations could be represented as such business rules.
However, it is often the case that the same business rule, enforcing a certain concept, might have different implementations based on some parameter (e.g. rule validating if vehicle driver meets minimum driver age limit might be different based on state). Depending on which parameter value is supplied in runtime, a certain rule variation must be evaluated. When the number of such parameters (or rule dimensions) increases and different rules have different variability patterns, it will result in an explosive increase of rule complexity and/or size of the static rule set.
In this presentation, we will examine the challenges of defining and handling multidimensional rule sets and will demonstrate how they can be addressed by moving definitions of validation business rules from static rulesets into Decision Tables. We will also review how a Decision Table Engine can be integrated with the Kraken validation rule engine, allowing it to resolve effective rule sets for each validation call dynamically, based on parameter values. Lastly, we will explore the challenges of implementing such an approach in modern cloud-based architectures, and validation of such rules in rich web applications.
Keywords: rule variability, rule versioning, decision tables, data validation rules
With a background in fundamental Computer Science and Software Engineering, Rimantas Zukaitis is a Lead Software Architect for EIS. For the last 15 years, he has been focusing on the use case of applying business rules approach for data validation as well as development processes, models, and tools to facilitate this approach. Over his career, he worked on several generations of rule engine implementations, and currently is leading the team working on Kraken Rule Engine – a lightweight business rule validation engine for distributed architectures, allowing the application of the same business rules both on the backend and frontend.