Adding Priority to Event Structures

Event Structures (ESs) are mainly concerned with the representation of causal relationships between events, usually accompanied by other event relations capturing conflicts and disabling. Among the most prominent variants of ESs are Prime ESs, Bundle ESs, Stable ESs, and Dual ESs, which differ in their causality models and event relations. Yet, some application domains require further kinds of relations between events. Here, we add the possibility to express priority relationships among events. We exemplify our approach on Prime, Bundle, Extended Bundle, and Dual ESs. Technically, we enhance these variants in the same way. For each variant, we then study the interference between priority and the other event relations. From this, we extract the redundant priority pairs-notably differing for the types of ESs-that enable us to provide a comparison between the extensions. We also exhibit that priority considerably complicates the definition of partial orders in ESs.Comment: In Proceedings EXPRESS/SOS 2013, arXiv:1307.690

Dynamic Causality in Event Structures

Event Structures (ESs) address the representation of direct relationships between individual events, usually capturing the notions of causality and conflict. Up to now, such relationships have been static, i.e., they cannot change during a system run. Thus, the common ESs only model a static view on systems. We make causality dynamic by allowing causal dependencies between some events to be changed by occurrences of other events. We first model and study the case in which events may entail the removal of causal dependencies, then we consider the addition of causal dependencies, and finally we combine both approaches in the so-called Dynamic Causality ESs. For all three newly defined types of ESs, we study their expressive power in comparison to the well-known Prime ESs, Dual ESs, Extended Bundle ESs, and ESs for Resolvable Conflicts. Interestingly, Dynamic Causality ESs subsume Extended Bundle ESs and Dual ESs but are incomparable with ESs for Resolvable Conflicts

Über die Grundlagen dynamischer Koalitionen : Modellieren von Veränderungen und Evolution von Arbeitsabläufen in Szenarien aus dem Gesundheitswesen

Gedruckt erschienen im Universitätsverlag der TU Berlin, ISBN 978-3-7983-2856-3 (ISSN 2199-5249)Dynamic Coalitions denote a temporary collaboration between different entities to achieve a common goal. A key feature that distinguishes Dynamic Coalitions from static coalitions is Dynamic Membership, where new members can join and others can leave after a coalition is set. This thesis studies workflows in Dynamic Coalitions, by analyzing their features, highlighting their unique characteristics and similarities to other workflows, and investigating their relation with Dynamic Membership. For this purpose, we use the formal model of Event Structures and extend it to faithfully model scenarios taken as use cases from healthcare. Event Structures allow for workflows modeling in general, and for modeling Dynamic Membership in Dynamic Coalitions as well through capturing the join and leave events of members. To this end, we first extend Event Structures with Dynamic Causality to address the dynamic nature of DCs. Dynamic Causality allows some events to change the causal dependencies of other events in a structure. Then, we study the expressive power of the resulting Event Structures and show that they contribute only to a specific kind of changes in workflows, namely the pre-planned changes. Second, we present Evolving Structures in order to support ad-hoc and unforeseen changes in workflows, as required by the use cases. Evolving Structures connect different Event Structures with an evolution relation which allows for changing an Event Structure during a system run. We consider different approaches to model evolution and study their equivalences. Furthermore, we show that the history of a workflow should be preserved in our case of evolution in Dynamic Coalitions, and we allow for extracting changes from an evolution to support Process Learning. Third, to capture the goals of DCs, we equip Evolving Structures with constraints concerning the reachability of a set of events that represents a goal. The former extensions allow for examining the changes and evolutions caused by members, and examining members’ contributions to goal satisfaction, through their join and leave events. Finally, we highlight many modeling features posed as requirements by the domain of our Dynamic-Coalition use cases, namely the healthcare, which are independent from the nature of Dynamic Coalitions, e.g. timing. We examine the literature of Event Structures for supporting such features, and we identify that the notion of Priority is missing in Event Structures. To this end, we add Priority to various kinds of Event Structures from the literature. Furthermore, we study the relation between priority on one side, and conjunctive causality, disjunctive causality, causal ambiguity and various kinds of conflict on the other side. Comparing to Adaptive Workflows, which are concerned with evolutions of workflows that occur as a response to changes, e.g. changes in the business environment or exceptions, this thesis shows that Dynamic-Coalition workflows are not only Adaptive but also Goal-Oriented. Besides, it adds one extra trigger for evolution in workflows—unique to Dynamic Coalitions—namely the join of new members who contribute to goal satisfaction in a Dynamic Coalition. Finally the thesis contributes to bridging the gap in modeling between theory and domain experts by supporting step-by-step modeling applied regularly in healthcare and other domains.Dynamische Koalitionen (DKen) bezeichnen eine temporäre Kollaboration zwischen verschiedenen Entitäten zum Erreichen eines gemeinsamen Ziels. Ein Schüsselaspekt, welcher dynamische Koalitionen von statischen Koalitionen unterscheidet ist die dynamische Mitgliedschaft, durch die neue Mitglieder hinzukommen und andere die Koalitionen verlassen können, nachdem sie entstanden ist. Diese Arbeit studiert Workflows in dynamische Koalitionen durch eine Analyse ihrer Eigenschaften, das Herausstellen ihrer einzigartigen Charakteristika und Ähnlichkeiten zu anderen Workflows und durch eine Untersuchung ihrer Beziehung zu dynamischer Mitgliedschaft. In diesem Sinne nutzen wir das formales Model der Ereignisstukturen (ESen) und erweitern es, um Fallstudien aus der Medizin angemessen zu modellieren. ESen erlauben sowohl eine generelle Workflow Modellierung als auch eine Darstellung von Eintritt- und Austrittereignissen von Mitgliedern. Zu diesem Zweck erweitern wir ESen zuerst um Dynamische Kausalität, um die dynamische Natur von DKs abzubilden. Dynamische Kausalität erlaubt bestimmten Ereignissen die kausalen Abhängigkeiten anderer Ereignissen in einer Struktur zu verändern. Dann untersuchen wir die Ausdrucksstärke der resutierenden ESen und zeigen, dass sie nur eine spezifische Art der Veränderung abbilden, die sogenannten vorgeplanten Veränderungen. Als Zweites präsentieren wir Evolving in ESen um ad-hoc- und unvorhergesehene Veränderungen zu unterstützen, wie es durch unsere Fallstudien benötigt wird. Evolving in ESen verbinden verschiedene ESen mit einer Relation, welche eine Veränderung einer ES während eines Ablaufes erlaubt. Wir ziehen verschiedene Ansätze der Modelevolution in Betracht und untersuchen ihre Äquivalenzen. Des Weiteren zeigen wir, dass in unserem Fall der Evolution in DKen die Geschichte eines Workflows erhalten bleiben muss und wir ermöglichen das Extrahieren von Veränderungen einer Evolution, um Process Learning zu unterstützen. Drittens: Um die Ziele von DKen abzubilden, fügen wir den Evolving in ESen mit Einschränkungen bezüglich der Erreichbarkeit einer Menge von Ereignissen hinzu, welche das Ziel repräsentieren. Die genannten Erweiterungen erlauben es sowohl die Änderungen und Evolutionen, die vom Mitgliedern verursacht werden als auch die Beiträge der Mitglieder zur Zielerreichung durch deren Entritt- und Austrittereignissen zu untersuchen. Schlussendlich, stellen wir viele Modellierungseigenschaften dar, welche von den DK-Fallstudien aus der Medizin benötigt werden und unabhängig von der Natur der DKen sind, wie z.B. Timing. Wir untersuchen die Literatur zu ESen bezüglich Unterstützung für solche Eigenschaften und stellen fest, dass der Begriff Priorität in ESen fehlt. Daher fügen wir Priorität zu verschiedenen ESen aus der Literatur hinzu. Des Weiteren untersuchen wir die Beziehungen von Priorität auf zu Konjunktiver Kausalität, disjunktiver Kausalität, kausal Uneindeutigkeit und verschiedenen Formen von Konflikt. Im Vergleich zu Adaptive Workflows, welche sich mit der Evolution von Workflows beschäftigt, die als Reaktion auf Veränderungen entsteht, wie z.B. Veränderungen im Business Environment oder Exceptions, zeigt diese Arbeit das DKen nicht nur adaptiv sondern auch zielorientiert sind. Außerdem fügt sie einen zusätzlichen Auslöser für Evolution in Workflows hinzu, welcher ausschließlich DKen eigen ist: das Hinzukommen neuer Mitglieder welche zur Zielerreichung der DK beitragen. Zuletzt trägt diese Arbeit bei, die Lücke der Modellierung zwischen der Theorie und den Domänenexperten zu überbrücken, in dem sie eine Schritt-für-Schritt Modellierung unterstützt, welche regelmäßig in der Medizin und anderen Bereichen angewandt wird

Dynamic Causality in Event Structures

Part 2: Formal Models of Concurrent and Distributed SystemsInternational audienceEvent Structures (ESs) address the representation of direct relationships between individual events, usually capturing the notions of causality and conflict. Up to now, such relationships have been static, i.e. they cannot change during a system run. Thus the common ESs only model a static view on systems. We dynamize causality such that causal dependencies between some events can be changed by occurrences of other events. We first model and study the case in which events may entail the removal of causal dependencies, then we consider the addition of causal dependencies, and finally we combine both approaches in the so-called Dynamic Causality ESs. For all three newly defined types of ESs, we study their expressive power in comparison to the well-known Prime ESs, Dual ESs, Extended Bundle ESs, and ESs for Resolvable Conflicts. Interestingly Dynamic Causality ESs subsume Extended Bundle ESs and Dual ESs but are incomparable with ESs for Resolvable Conflicts

Dynamic Causality in Event Structures

Event Structures (ESs) address the representation of direct relationships between individual events, usually capturing the notions of causality and conflict. Up to now, such relationships have been static, i.e., they cannot change during a system run. Thus, the common ESs only model a static view on systems. We make causality dynamic by allowing causal dependencies between some events to be changed by occurrences of other events. We first model and study the case in which events may entail the removal of causal dependencies, then we consider the addition of causal dependencies, and finally we combine both approaches in the so-called Dynamic Causality ESs. For all three newly defined types of ESs, we study their expressive power in comparison to the well-known Prime ESs, Dual ESs, Extended Bundle ESs, and ESs for Resolvable Conflicts. Interestingly, Dynamic Causality ESs subsume Extended Bundle ESs and Dual ESs but are incomparable with ESs for Resolvable Conflicts