Decision Model And Notation Research Articles

Process-Modelling of Cross-Sector Healthcare Quality Indicators.

Quality indicators (QI) are a common method to ensure quality in healthcare. This paper is based on the so-called QI-KA project, which defined cross-sector QI for the Austrian healthcare system. However, to allow for automated conformance checking, the QI must be modelled in a formal way. The aim of this paper is to gather requirements on modelling languages and tools in healthcare, create models for one of the QI-KA project's QI and finally evaluate them. The QI-2 is modelled in the Business Process Model and Notation (BPMN) together with the Decision Model and Notation (DMN) to showcase and evaluate their application and suitability. The generated models show that BPMN and DMN are mostly appropriate for this use case and serve as a basis for automated conformance checking. We successfully showed that BPMN and DMN can be used to model cross-sector QI in a formal way to prepare for conformance checking. The field of application can be extended to other medical areas. To further improve quality in healthcare, outcomes from models and conformance-checking should be discussed in interdisciplinary teams.

AI-Enhanced Hybrid Decision Management

The Decision Model and Notation (DMN) modeling language allows the precise specification of business decisions and business rules. DMN is readily understandable by business users involved in decision management. However, as the models get complex, the cognitive abilities of humans threaten manual maintainability and comprehensibility. Proper design of the decision logic thus requires comprehensive automated analysis of e.g., all possible cases the decision shall cover; correlations between inputs and outputs; and the importance of inputs for deriving the output. In the paper, the authors explore the mutual benefits of combining human-driven DMN decision modeling with the computational power of Artificial Intelligence for DMN model analysis and improved comprehension. The authors propose a model-driven approach that uses DMN models to generate Machine Learning (ML) training data and show, how the trained ML models can inform human decision modelers by means of superimposing the feature importance within the original DMN models. An evaluation with multiple real DMN models from an insurance company evaluates the feasibility and the utility of the approach.

MCDM and FaaS to Automate the Eligibility of Business Rules in the Decision-Making Task during Business Processes

Serverless Computing, also named Function as a Service (FaaS) in the Azure cloud provider, is a new feature of cloud computing. This is another brick, after managed and fully managed services, allowing to provide on-demand services instead of provisioned resources and it is used to strengthen the company’s ability in order to master its IT system and consequently to make its business processes more profitable. Knowing that decision making is one of the important tasks in business processes, the improvement of this task was the concern of both the industry and the academy communities. Those efforts have led to several models, mainly the two Object Management Group (OMG) models: Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) in order to support this need. The DMN covers the decision-making task in business processes mainly the eligibility of business rules. This eligibility can be automated in order to help designers in the mastering of this important task by the running of an algorithm or a method such as the Multiple Criteria Decision Making (MCDM). This feature can be designed and implemented and deployed in various architectures to integrate it in existing Business Process Management Systems (BPMS). It could then improve supporting several business areas such as the Business Intelligence (BI) process. In this paper, our main contribution is the enrichment of the DMN model by the automation of the business rules eligibility through Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using FaaS to further streamline the decision-making task in business processes. Results show to strengthen business-IT alignment and reduce the gap between the real world and associated IT solutions.

ELI: an IoT-aware big data pipeline with data curation and data quality.

The complexity of analysing data from IoT sensors requires the use of Big Data technologies, posing challenges such as data curation and data quality assessment. Not facing both aspects potentially can lead to erroneous decision-making (i.e., processing incorrectly treated data, introducing errors into processes, causing damage or increasing costs). This article presents ELI, an IoT-based Big Data pipeline for developing a data curation process and assessing the usability of data collected by IoT sensors in both offline and online scenarios. We propose the use of a pipeline that integrates data transformation and integration tools and a customisable decision model based on the Decision Model and Notation (DMN) to evaluate the data quality. Our study emphasises the importance of data curation and quality to integrate IoT information by identifying and discarding low-quality data that obstruct meaningful insights and introduce errors in decision making. We evaluated our approach in a smart farm scenario using agricultural humidity and temperature data collected from various types of sensors. Moreover, the proposed model exhibited consistent results in offline and online (stream data) scenarios. In addition, a performance evaluation has been developed, demonstrating its effectiveness. In summary, this article contributes to the development of a usable and effective IoT-based Big Data pipeline with data curation capabilities and assessing data usability in both online and offline scenarios. Additionally, it introduces customisable decision models for measuring data quality across multiple dimensions.

Extracting Decision Model and Notation models from text using deep learning techniques

Companies and organizations often use manuals and guidelines to communicate and execute operational decisions. Decision Model and Notation (DMN) models can be used to model and automate these decisions. Modeling a decision from a textual source, however, is a time intensive and complex activity hence a need for shorter modeling times. This paper studies how NLP deep learning techniques can extract decision models from text faster. In this paper, we study and evaluate an automatic sentence classifier and a decision dependency extractor using NLP deep learning models (BERT and Bi-LSTM-CRF). A large labeled and tagged dataset was collected from real use cases to train these models. We conclude that BERT can be used for the (semi)-automatic extraction of decision models from text.

Identifying the Current State and Improvement Opportunities in the Information Flows Necessary to Manage Professional Athletes: A Case Study in Rugby Union.

In sporting environments, the knowledge necessary to manage athletes is built on information flows associated with player management processes. In current literature, there are limited case studies available to illustrate how such information flows are optimized. Hence, as the first step of an optimization project, this study aimed to evaluate the current state and the improvement opportunities in the player management information flow executed within the High-Performance Unit (HPU) at a professional rugby union club in England. Guided by a Business Process Management framework, elicitation of the current process architecture illustrated the existence of 18 process units and two core process value chains relating to player management. From the identified processes, the HPU management team prioritized 7 processes for optimization. In-depth details on the current state (As-Is) of the selected processes were extracted from semi-structured, interview-based process discovery and were modeled using Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) standards. Results were presented for current issues in the information flow of the daily training load management process, identified through a thematic analysis conducted on the data obtained mainly from focus group discussions with the main stakeholders (physiotherapists, strength and conditioning coaches, and HPU management team) of the process. Specifically, the current state player management information flow in the HPU had issues relating to knowledge creation and process flexibility. Therefore, the results illustrate that requirements for information flow optimization within the considered environment exist in the transition from data to knowledge during the execution of player management decision-making processes.

Exploration of the Decision Model and Notation (DMN) Standard for Representation of Knowledge Bases Encoded in the Arden Syntax.

Arden Syntax encodes knowledge as Medical Logic Modules (MLMs). Current work involves improvement of its representation of business processes. Assess how DMN can represent decision-making in MLMs. A sample of 340 MLMs was tabulated for DMN representation. While DMN can represent all MLMs at the decision requirements level, some are not supported at the decision logic level. DMN facilitates representation of business processes but would not address all decision logic.

Integrated privacy decision in BPMN clinical care pathways models using DMN

Personal data is highly affected by the witnessed digital transformation of healthcare processes. This process relies deeply on the connectivity and decentralization of healthcare systems and data repositories. In this context, value creation and quality enhancement are obviously leveraged, however both health providers and individuals could be exposed to many risks ranging from privacy violations to medical identity theft and personal harm. Hence, it is essential that healthcare stakeholders ensure privacy protection and systemic compliance to personal data regulations such as HIPPA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation). Taking clinical processes as a starting point is very important to highlight the personal data in use and to assess whether such usage is justifiable and subsequently allow privacy management decisions to be made. In this paper we combine BPMN (Business Process Model and Notation) and DMN (Decision Model and Notation) to model clinical care pathways as standard business processing constituting the hospital information system. Business process modelling presents a useful mean to model clinical care pathways. It allows a complete discovery of data processing scenarios. DMN (Decision Model and Notation) is implemented in BPMN models to present the rules that lead to a decision in easy-to-read tables which are executed directly by a decision engine. In addition, the integration of verifiable security labels of the manipulated data, we make sure compliance to legislation is ensured at the level of decision rules for each decision table of the DMN.

Tackling the DM Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning

AbstractKnowledge-based AI typically depends on a knowledge engineer to construct a formal model of domain knowledge – but what if domain experts could do this themselves? This paper describes an extension to the Decision Model and Notation (DMN) standard, called Constraint Decision Model and Notation (cDMN). DMN is a user-friendly, table-based notation for decision logic, which allows domain experts to model simple decision procedures without the help of IT staff. cDMN aims to enlarge the expressiveness of DMN in order to model more complex domain knowledge, while retaining DMNs goal of being understandable by domain experts. We test cDMN by solving the most complex challenges posted on the DM Community website. We compare our own cDMN solutions to the solutions that have been submitted to the website and find that our approach is competitive. Moreover, cDMN is able to solve more challenges than any other approach.

Knowledge-based decision support for machine component design: A case study

This paper describes research conducted during a project with a multinational company that focuses on product design. The project tackles two different goals: providing sales staff with a tool that allows them to autonomously handle routine requests, and providing the company’s engineers with a decision support system to help them design products for more challenging application areas. For the first goal, we make use of a deterministic decision process, represented in the recent Decision Model and Notation (DMN) standard. For the second goal, we propose a constraint-based method. There, we use the IDP system to provide a number of interactive functionalities based on a logical representation of the relevant constraints. To ensure that the system is maintainable, we want the constraints to be updated by the engineers themselves. The IDP language is not ideal for this. Instead, we propose the cDMN notation, which extends the user-friendly DMN to constraints.

DMN4DQ: When data quality meets DMN

To succeed in their business processes, organizations need data that not only attains suitable levels of quality for the task at hand, but that can also be considered as usable for the business. However, many researchers ground the potential usability of the data on its quality. Organizations would benefit from receiving recommendations on the usability of the data before its use. We propose that the recommendation on the usability of the data be supported by a decision process, which includes a context-dependent data-quality assessment based on business rules. Ideally, this recommendation would be generated automatically. Decision Model and Notation (DMN) enables the assessment of data quality based on the evaluation of business rules, and also, provides stakeholders (e.g., data stewards) with sound support for the automation of the whole process of generation of a recommendation regarding usability based on data quality.The main contribution of the proposal involves designing and enabling both DMN-driven mechanisms and a guiding methodology (DMN4DQ) to support the automatic generation of a decision-based recommendation on the potential usability of a data record in terms of its level of data quality. Furthermore, the validation of the proposal is performed through the application of a real dataset.

Natural language techniques supporting decision modelers

Decision Model and Notation (DMN) has become a relevant topic for organizations since it allows users to control their processes and organizational decisions. The increasing use of DMN decision tables to capture critical business knowledge raises the need for supporting analysis tasks such as the extraction of inputs, outputs and their relations from natural language descriptions. In this paper, we create a stepping stone towards implementing a Natural Language Processing framework to model decisions based on the DMN standard. Our proposal contributes to the generation of decision rules and tables from a single sentence analysis. This framework comprises three phases: (1) discourse and semantic analysis, (2) syntactic analysis and (3) decision table construction. To the best of our knowledge, this is the first attempt devoted to automatically discovering decision rules according to the DMN terminology from natural language descriptions. Aiming at assessing the quality of the resultant decision tables, we have conducted a survey involving 16 DMN experts. The results have shown that our framework is able to generate semantically correct tables. It is convenient to mention that our proposal does not aim to replace analysts but support them in creating better models with less effort.

Report on the 3rd International Joint Conference on Rules and Reasoning (RuleML+RR 2019)

The annual International Joint Conference on Rules and Reasoning (RuleML+RR) is an international conference on research, applications, languages and standards for rule technologies, rule-based programming and rule-based systems including production rules systems, logic programming rule engines, as well as business-rule engines and management systems; Semantic Web rule languages and rule standards, including RuleML (e.g., Datalog+ RuleML, Reaction RuleML and LegalRuleML), SWRL, RIF, Common Logic, PRR, decision rules and Decision Model and Notation (DMN), as well as Semantics of Business Vocabulary and Business Rules (SBVR); rule-based Event Processing Languages (EPLs) and technologies; and foundational research on inference rules, transformation rules, decision rules, production rules, and Event-Condition-Action (ECA) rules. In 2017, RuleML+RR joined the efforts of two wellestablished conference series: the International Web Rule Symposia (RuleML) and the Web Reasoning and Rule Systems (RR) conferences, and it is now the leading conference to build bridges between academia and industry in the field of Web rules and its applications, especially as part of the Semantic Technology stack. RuleML+RR is commonly listed together with and related to other major high-impact Artificial Intelligence conferences worldwide, starting with IJCAI in 2011 and 2016, ECAI in 2012, AAAI in 2013, ECAI in 2014, the AI Summit London in 2017, and GCAI in 2018 and 2019. The RuleML symposia and RR conferences have been held since 2002 and 2007, respectively. The RR conferences have been a forum for discussion and dissemination of new results on Web reasoning and rule systems, with an emphasis on rule-based approaches and languages. The RuleML symposia have been devoted to disseminating research, applications, languages, and standards for rule technologies, with attention to both theoretical and practical developments, to challenging new ideas, and to industrial applications. Building on the tradition of both, RuleML and RR, the joint conference series RuleML+RR aims at bridging academia and industry in the field of rules, and at fostering the cross-fertilization between the different communities focused on the research, development, and applications of rule-based systems. RuleML+RR aims at being the leading conference series for all subjects concerning theoretical advances, novel technologies, and innovative applications about knowledge representation and reasoning with rules.

Decision model change patterns for dynamic system evolution

In the modern digital era, information systems must operate in increasingly interconnected and dynamic environments, which force them to be changeable yet consistent. Such modern information systems are usually decision- and knowledge-intensive. A recently introduced standard, the decision model and notation (DMN), has been adopted in both industry and academia as a suitable method for modelling decisions and decision rules. Noteworthy is that, despite the dynamic nature of modern knowledge-intensive systems, DMN was only studied and implemented in a static fashion, as decision schema change patterns have not received any attention so far. This paper identifies and analyses the change patterns that can occur in a DMN decision model. A change in the decision model can require the triggering of other changes in order to safeguard consistency. As such, this paper will also investigate for each change pattern which further changes should be performed to ensure model consistency. The patterns presented in this paper will not only facilitate the understanding of decision change management and within-model consistency, but can also be capitalised on for developing and implementing flexible decision management systems. To illustrate this, we present a modelling environment prototype that provides modelling support when applying the proposed change patterns.

Decision as a Service (DaaS): A Service-Oriented Architecture Approach for Decisions in Processes

Separating decision modelling from the processes modelling concern recently gained significant support in literature, as incorporating both concerns into a single model impairs the scalability, maintainability, flexibility, and understandability of both processes and decisions. Most notably the introduction of the Decision Model and Notation (DMN) standard by the Object Management Group provides a suitable solution for externalising decisions from processes and automating decision enactments for processes. This article introduces a systematic way of tackling the separation of the decision modelling concern from process modelling by providing a Decision as a Service (DaaS) layered Service-Oriented Architecture (SOA) which approaches decisions as automated and externalised services that processes need to invoke on demand to obtain the decision outcome. The DaaS mechanism is elucidated by a formalisation of DMN constructs and the relevant layer elements. Furthermore, DaaS is evaluated against the fundamental characteristics of the SOA paradigm, proving its contribution in terms of abstraction, reusability, loose coupling, and other pertinent SOA principles. Additionally, the benefits of the DaaS design on process-decision modelling and mining are discussed. Finally, the DaaS design is illustrated on a real-life event log of a bank loan application and approval process, and the SOA maturity of DaaS is assessed.

From decision knowledge to e-government expert systems: the case of income taxation for foreign artists in Belgium

Since the introduction of the Decision model and notation (DMN), the standard has successfully been adopted in both industry and academia. However, no clear modelling guidelines can be found regarding the development of DMN decision models. For approaching this gap, this paper discusses modelling methodologies for the DMN standard, both at the decision requirements level as well as at the decision logic level. For that purpose, we capitalise on existing guidelines in related fields, such as decision table modelling, information systems engineering, and software engineering. We adapt, expand, and restructure the guidelines to conform to DMN model development. Additionally, we provide a real-life case that was modelled using the suggested modelling strategies and consequently, we deploy the model as a government e-service for tax management. The real-life case is concerned with clarifying tax regulations for visiting performing artists in Belgium, and it was carried out in cooperation with oKo, a Belgian industry federation for the arts, and the Ministry of Culture. The decision model was built in the Avola tool and implemented as an e-service in order to demonstrate the suitability of the DMN standard for the deployment of expert system e-government services.

Modeling a Clinical Pathway for Contraception.

The Centers for Disease Control and Prevention (CDC) produced a 72-page document titled "U.S. Selective Practice Recommendations for Contraceptive Use" in 2016. This document contains the medical eligibility criteria (MEC) for contraceptive initiation or continuation based on a patient's current health status. Notations such as Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) might be useful to model such recommendations. Our objective was to use BPMN and DMN to model and standardize the processes and decisions involved in initiating birth control according to the CDC's MEC for birth control initiation. This model could then be incorporated into an electronic health records system or other digital platform. Medical terminology, processes, and decisions were modeled in coordination with the CDC to ensure correctness. Challenges in terminology bindings were identified and categorized. A model was successfully produced. Integration of clearly defined data elements proved to be the biggest challenge. BPMN and DMN have strengths and weaknesses when modeling medical processes; however, they can be used to successfully create models for clinical pathways.

Holistic discovery of decision models from process execution data

The analysis of business processes is a multifaceted problem that is comprised of analysing both activities’ workflow, as well as the decisions that are made throughout that workflow. In process mining, the automated discovery of process models from event data, a strong emphasis can be found towards discovering this workflow, as well as how data influences that workflow, i.e., decision point analysis. Nonetheless, the data that is pertaining to the activities in the workflow does not necessarily correlate with the control flow. Decisions that influence variables that are used by activities can also impact other variables used later in the workflow without interfering with the order in which activities are executed. Discovering this has not been addressed in literature, as current decision mining techniques still rely on control flow.To address this, Process Mining Integrating Decisions (P-MInD) is proposed. It relies on uncovering the influence of activities on their variables and connects them by making use of sequence dependencies present in the data. Furthermore, it allows to find autocorrelations, as well to incorporate case variables. This allows to establish a holistic image of the decision layer, captured with Decision Model and Notation (DMN), that is consistent with the discovered control flow. Furthermore, decision model conformance checking, i.e., the matching of event logs with holistic models, is proposed to offer a way to verify whether the models are corresponding with the behaviour that is present in the current system. P-MInD is implemented and used on real-life data to verify its effectiveness.

From BPMN process models to DMN decision models

The interplay between process and decision models plays a crucial role in business process management, as decisions may be based on running processes and affect process outcomes. Often process models include decisions that are encoded through process control flow structures and data flow elements, thus reducing process model maintainability. The Decision Model and Notation (DMN) was proposed to achieve separation of concerns and to possibly complement the Business Process Model and Notation (BPMN) for designing decisions related to process models. Nevertheless, deriving decision models from process models remains challenging, especially when the same data underlie both process and decision models. In this paper, we explore how and to which extent the data modeled in BPMN processes and used for decision-making may be represented in the corresponding DMN decision models. To this end, we identify a set of patterns that capture possible representations of data in BPMN processes and that can be used to guide the derivation of decision models related to existing process models. Throughout the paper we refer to real-world healthcare processes to show the applicability of the proposed approach.

Complexity metrics for DMN decision models

Complexity impairs the maintainability and understandability of conceptual models. Complexity metrics have been used in software engineering and business process management (BPM) to capture the degree of complexity of conceptual models. The recent introduction of the Decision Model and Notation (DMN) standard provides opportunities to shift towards the Separation of Concerns paradigm when it comes to modelling processes and decisions. However, unlike for processes, no studies exist that address the representational complexity of DMN decision models. In this paper, we provide an initial set of complexity metrics for DMN models. We gather insights from the process modelling and software engineering fields to propose complexity metrics for DMN decision models. Additionally, we provide an empirical complexity assessment of DMN decision models. For the decision requirements level of the DMN standard 19 metrics were proposed, while 7 metrics were put forward for the decision logic level. For decision requirements, the model size-based metrics, the Durfee Square Metric (DSM) and the Perfect Square Metric (PSM) prove to be the most suitable. For the decision logic level of DMN the Hit Policy Usage (HPU) and the Total Number of Input Variables (TNIV) were evaluated as suitable for measuring DMN decision table complexity.