Integrating BPMN and DMN: Modeling and Analysis

AbstractThe operational backbone of modern organizations is the target of business process management, where business process models are produced to describe how the organization should react to events and coordinate the execution of activities so as to satisfy its business goals. At the same time, operational decisions are made by considering internal and external contextual factors, according to decision models that are typically based on declarative, rule-based specifications that describe how input configurations correspond to output results. The increasing importance and maturity of these two intertwined dimensions, those of processes and decisions, have led to a wide range of data-aware models and associated methodologies, such as BPMN for processes and DMN for operational decisions. While it is important to analyze these two aspects independently, it has been pointed out by several authors that it is also crucial to analyze them in combination. In this paper, we provide a native, formal definition of DBPMN models, namely data-aware and decision-aware processes that build on BPMN and DMN S-FEEL, illustrating their use and giving their formal execution semantics via an encoding into Data Petri nets (DPNs). By exploiting this encoding, we then build on previous work in which we lifted the classical notion of soundness of processes to this richer, data-aware setting, and show how the abstraction and verification techniques that were devised for DPNs can be directly used for DBPMN models. This paves the way towards even richer forms of analysis, beyond that of assessing soundness, that are based on the same technique

What we know and what we do not know about DMN

The recent Decision Model and Notation (DMN) establishes business decisions as first-class citizens of executable business processes. This research note has two objectives: first, to describe DMN's technical and theoretical foundations; second, to identify research directions for investigating DMN's potential benefits on a technological, individual and organizational level. To this end, we integrate perspectives from management science, cognitive theory and information systems research

Refining Obstacle Perception Safety Zones via Maneuver-Based Decomposition

A critical task for developing safe autonomous driving stacks is to determine whether an obstacle is safety-critical, i.e., poses an imminent threat to the autonomous vehicle. Our previous work showed that Hamilton Jacobi reachability theory can be applied to compute interaction-dynamics-aware perception safety zones that better inform an ego vehicle's perception module which obstacles are considered safety-critical. For completeness, these zones are typically larger than absolutely necessary, forcing the perception module to pay attention to a larger collection of objects for the sake of conservatism. As an improvement, we propose a maneuver-based decomposition of our safety zones that leverages information about the ego maneuver to reduce the zone volume. In particular, we propose a "temporal convolution" operation that produces safety zones for specific ego maneuvers, thus limiting the ego's behavior to reduce the size of the safety zones. We show with numerical experiments that maneuver-based zones are significantly smaller (up to 76% size reduction) than the baseline while maintaining completeness.Comment: * indicates equal contribution. Accepted into the IEEE Intelligent Vehicles Symposium 202

10431 Abstracts Collection -- Software Engineering for Self-Adaptive Systems

From 24.10. to 29.10.2010, the Dagstuhl Seminar 10431 ``Software Engineering for Self-Adaptive Systems\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Symbolic Soundness Verification of Data Petri Net

Le Data Petri Net sono ancora un argomento di nicchia e questo è dimostrato dalla poca documen- tazione che si trova online. Tuttavia possono avere applicazioni importanti soprattutto nell’ambito dei sistemi concorrenti e a eventi discreti. Tuttavia un DPN generato deve rispettare le proprietà di Soundness, ovvero quelle proprietà che garantiscono il buon funzionamento del sistema. In un sistema possono verificarsi una serie di eventi in un certo ordine e per essere corretto dobbiamo essere sicuri che nessuna possibile combinazione di eventi possa portare a risultati inconsistenti. Per poter lavorare con le Data Petri Net è fondamentale aver compreso al meglio il funzionamento delle Petri Net standard e quindi senza dati. Solo dopo è possibile studiare la sua estensione con dati e importando i concetti della programmazione a vincoli. Mentre nelle reti di petri di base le transizioni si limitano a consumare token dal place entrante e aggiungerne al place uscente, nelle DPN nella transizione sarà rappresentato un vincolo e compito del nostro programma è verificare se questo vincolo sarà sempre rispettato, non sarà mai rispettato oppure se in alcuni casi sarà rispettato e in altri no. Nella programmazione logica i vincoli possono essere di molti tipi, ma in questo programma ci si occuperà solamente di vincoli logici che usano uno dei sei operatori relazionali (=,̸=,>,<, ≥, ≤) che metteranno in relazione una variabile con una costante, oppure una variabile con un’altra variabile. Il dominio di queste variabili è rappresentato dall’insieme dei numeri reali R , tuttavia nel programma si è deciso di gestire anche variabili booleane e che quindi possono assumere valori true o false. Dopo aver definito il campo di applicazione è fondamentale trovare un modo per verificare la correttezza del sistema e per fare questo si provvede come di norma viene fatto per i problemi di programmazione logica: con la costruzione del Constraint Graph. Attraverso l’implementazione degli algoritmi che vengono approfonditi nella stesura di questa tesi è possibile verificare la correttezza di un DPN, sapendo che DPN e Constraint Graph sono in relazione tra loro attraverso la obs-simulation, per cui se il Constraint Graph è data-aware sound, allora anche il DPN è data-aware sound.Data Petri Nets are still a niche topic and this is demonstrated by the little documentation that is available online. However, they may have important applications especially within concurrent and discrete-event systems. However, a generated DPN must respect the Soundness properties, i.e. those properties that guarantee the proper functioning of the system. In a system a series of events can occur in a certain order and to be correct we must be sure that no possible combination of events can lead to inconsistent results. In order to work with Data Petri Nets, it is essential to have a better understanding of how standard Petri Nets work, and therefore without data. Only then is it possible to study its extension with data and by importing the concepts of constraint programming. While in basic petri nets the transitions are limited to consuming tokens from the incoming place and adding them to the outgoing place, in the DPN a constraint will be represented in the transition and the task of our program is to verify whether this constraint will always be respected, will never be respected or whether in some cases it will be respected and in others not. In logic programming, constraints can be of many types, but in this program we will only deal with logical constraints that use one of the six relational operators (=,̸=,>,<, ≥, ≤) which will relate a variable to a constant, or a variable to another variable. The domain of these variables is represented by the set of real numbers R , however in the program it was decided to also manage boolean variables and which therefore can assume true or false values. After defining the field of application it is essential to find a way to verify the correctness of the system and to do this we proceed as is normally done for logic programming problems: with the construction of the Constraint Graph. Through the im- plementation of the algorithms that are detailed in the drafting of this thesis it is possible to verify the correctness of a DPN, knowing that DPN and Constraint Graph are related to each other through the obs-simulation, so if the Constraint Graph is data-aware sound, then the DPN is also data-aware sound