ABSTRACT. Abstract:

In Knowledge Automation (2012) I proposed a decision-modelling methodology (DRAW) for defining automated decision making. This approach, although successful, focusses on the functionality of decision services. It models the surrounding business processes in only enough detail to provide context for the required decision making, and models user interactions quite crudely. This predisposes analysts to specify systems with relatively simplistic human interactions, neglecting the rich possibilities of collaboration between people and computers in decision making.

Using the OMG “Triple Crown” (with other standards) it is possible to model the totality of functional requirements for a decision-making system, including complex interactions between automated business processes and human case workers. BPMN models the processes, CMMN models the user participation, and DMN models the automated decision making. However, it is not always clear how these standards should be applied together.

I suggest a set of principles for partitioning functional requirements between these three modelling domains so that there is no omission or duplication of functionality between them, and so that all interactions – between the domains and with external components – are explicitly modelled. These principles use only existing features in the “Triple Crown” standards, supported by other UML-based models, particularly use case models, object models and state models, and are therefore already supported by existing toolsets.

While modelling can be approached from many directions, an “Outside-In” approach would adopt the sequence: case modelling, business process modelling, decision modelling. This may be most appropriate when specifying systems with complex human interactions. The presentation uses examples from real-world modelling problems.

Key take-away: A clear, simple method of specifying the complete functionality of organisational decision management systems.

Audience: Process stakeholders, business analysts, system architects.

Key technologies: Business Process Management Systems, Case Management Systems and Decision Management Systems.

ABSTRACT. The Decision Model and Notation (DMN) offers the perfect solution to specifying Business Decisions. Symbolic and sub-symbolic artificial Intelligence (AI) approaches can effectively be used jointly within DMN, delivering explainable decision automation that is desired if not mandatory in any business context. The resulting DMN Decision Models are aso the perfect architecture for the creation of decision support dashboards.

In this session we will demonstrate how line of business people can define business decisions that are explainable, auditable, and traceable. These business decisions can be assembled and consumed as services via a modern platform API architecture and visualized via graphical dashboards. The resulting dashboards help visualize information and knowledge that that are critical for the operations of any type of business.

ABSTRACT. This presentation examines to which extent some of the important Decision Model and Notation (DMN) features and concepts are supported by tooling. It is not a tool comparison, and no product names are revealed, but the analysis tries to give an indication of which elements of decision requirements diagrams, decision logic specifications and the (S)FEEL expression language are commonly present in current decision modeling (and execution) tools.

This analysis complements the DMN Technology Compatibility Kit (https://github.com/agilepro/dmn-tck) (TCK), as attention is also paid to tools which can only be examined manually or which do not obey the exact specification of the standard. The approach does however give an indication of which modeling features and concepts are considered important by tool providers.

ABSTRACT. Goal-oriented business decision modeling is driven by the need to simplify communication between business analysts and operational business decision models while extending the capabilities of traditional business rules and decision management systems. Decision models created in accordance with the current DMN standard usually address only one question and expect to determine a single answer given different input data. The proposed goal-oriented approach aims to creation of business decision models that cover certain business domains and are capable to reach not one but multiple business goals by providing answers to various questions in terms of automatically calculated decision variables. Such decision models can be designed by defining the hierarchy of business goals and sub-goals with relationships between them described in business-friendly decision tables. Without asking a human decision modeler to specify knowledge and information requirements, these models should be able to automatically calculate an execution path within the decision model that leads to a selected goal. In this presentation we will demonstrate the goal-oriented approach using well-known decision models published at DMCommunity.org. We will show a new interactive web interface that allows a business analyst to execute and analyze goal-oriented decision models.

ABSTRACT. The burgeoning scale of automated decision-making in developing economies, such as that required by financial fraud and customer personalization in China, will create a demand for high performance decision execution several orders of magnitude higher than today’s workhorses. Multiplying this by the new scale of data imposed by the Internet of Things and the accountability required by increasingly rigorous compliance regulations will demand unprecedented volumes of complex decision-making; volumes requiring not just scalable hardware, but software purpose-built for the execution of compiled decisions.

This presentation looks at implementation strategies for supporting very-high-performance, DMN-based decision-making using modest hardware, and outlines the results. It examines the use of a single-pass compiler for decision tables based on a bitwise maintenance of rule masks to minimize execution time and enable real-world decisions to be made in microseconds. It also articulates the challenges of creating a DMN XML parser in XSLT.

Attendees of this session will discover which of DMN’s features posed the biggest challenges, both in terms of satisfying the TCK tests and during performance optimization. Further, we discuss some proposed revisions to DMN’s type system to improve performance with no practical impact to its flexibility. During a demonstration, several demanding decision models will be compiled, benchmarked, verified and executed. In addition, the presentation will highlight some of the technical challenges imposed by practical application of the DMN standard such as null propagation and hit policy enforcement.

ABSTRACT. Accord Project is the leading community of legal and technical professionals, creating the standards and software for the formation and execution of blockchain-agnostic smart legal contracts, including a domain specific language, execution engine, and templating system.

https://www.accordproject.org

In his presentation, Dan outlines the goals of the Accord Project, similarities, and differences with previous efforts in expert systems, business rules, and decision management.

ABSTRACT. Many believe blockchain will be a disruptive technology that will facilitate digital economy in a trusted way. We hope that promise will be kept, because it will be a major boost to the business rules / decision management community. For such an economy to actually work, many of the legal interactions between parties have to be digitised. And this requires Interpretation , Analysis an Modelling of legal texts and translating those models into smart contracts or other implementations that can be accessed via blockchain solutions. And the business rules community has the goodies in place to make this work. Our goal is not only to demonstrate the way this process works, but also what is needed to make the resulting smart contracts trustworthy for the community that uses such smart contracts. In our view this requires transparency through documentation of the interpretation, analysis and modelling process and repeatable code generation. We propose to publish all the information and capabilities on the blockchain so the blockchain community can actually check if the smart contracts they use are in line with that documentation.

P.S. we uploaded a blank paper for now. If the abstract is interesting enough we will draw up a final paper.

ABSTRACT. The Decision Modeling and Notation (DMN) is a modeling language for decisions. DMN is an industry standard maintained by OMG. Successful Domain Specific Languages (DSLs) must be simple, easy to understand and use, with a higher level of abstraction and supported by a mature language workbench (e.g. editors and executions engines).

This paper presents jDMN an open-source execution engine for DMN implemented in Java. The attendees will have the opportunity to understand the internals of jDMN and the benefits of adopting DMN based solutions and executing them in jDMN.

jDMN provides support for DMN models validation, transformation, evaluation or translation to Java followed by an execution on JVM. The provided framework is flexible and configurable. For example, the users can define their own DMN transformers, validators and translators.

A jDMN dialect is a collection of certain DMN features, for example the built-in library and the mapping of the FEEL types to native types. The main purpose of a jDMN dialect is to be able to support DMN features variation. The jDMN dialects are organized as a taxonomy. jDMN supports several dialects, for example Signavio and Java 8 dialects. The framework is extensible, for example users can define their own dialect and execute DMN models accordingly.

Several code generation optimizations supported by jDMN such as: tree and DAG models execution, linked decisions and lazy evaluation for sparse decision tables are also presented.

ABSTRACT. In decision making on sales activities, the dependence on individual skills becomes a problem, as human judgment from past experiences play a primary role. It is important to visualize the regularity between decision-making processes and their outcomes to support sales staff and enhance sales activities. To solve this problem, we are developing a business decision support system using a machine learning model. For that, it is necessary to learn the process of sales activities with high probability of obtaining orders; therefore, the technology of process discovery that extracts regularity from the decision-making process is essential. However,it is difficult to apply the process discovery method of conventional process mining in the decision making process of sales activities,because the rules are not known in advance and the input information is unstructured data, such as business diaries. In this study, we provide an activity estimation system based on unstructured data, and a process discovery method for stochastic expression of regularity in an atypical process.