Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

BeSpaceD: Towards a Tool Framework and Methodology for the Specification and Verification of Spatial Behavior of Distributed Software Component Systems

In this report, we present work towards a framework for modeling and checking behavior of spatially distributed component systems. Design goals of our framework are the ability to model spatial behavior in a component oriented, simple and intuitive way, the possibility to automatically analyse and verify systems and integration possibilities with other modeling and verification tools. We present examples and the verification steps necessary to prove properties such as range coverage or the absence of collisions between components and technical details

Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining "vague" when there is not enough "evidence" in the data or standard modeling constructs do not "fit". Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.Comment: 25 pages, 12 figure

Model-based machine learning to identify clinical relevance in a high-resolution simulation of sepsis and trauma

Introduction: Sepsis is a devastating, costly, and complicated disease. It represents the summation of varied host immune responses in a clinical and physiological diagnosis. Despite extensive research, there is no current mediator-directed therapy, nor a biomarker panel able to categorize disease severity or reliably predict outcome. Although still distant from direct clinical translation, dynamic computational and mathematical models of acute systemic inflammation and sepsis are being developed. Although computationally intensive to run and calibrate, agent-based models (ABMs) are one type of model well suited for this. New analytical methods to efficiently extract knowledge from ABMs are needed. Specifically, machine-learning techniques are a promising option to augment the model development process such that parameterization and calibration are performed intelligently and efficiently. Methods: We used the Keras framework to train an Artificial Neural Network (ANN) for the purpose of identifying critical biological tipping points at which an in silico patient would heal naturally or require intervention in the Innate Immune Response Agent-Based Model (IIRABM). This ANN, determines simulated patient “survival” from cytokine state based on their overall resilience and the pathogenicity of any active infections experienced by the patient, defined by microbial invasiveness, toxigenesis, and environmental toxicity. These tipping points were gathered from previously generated datasets of simulated sweeps of the 4 IIRABM initializing parameters. Results: Using mean squared error as our loss function, we report an accuracy of greater than 85% with inclusion of 20% of the training set. This accuracy was independently validated on withheld runs. We note that there is some amount of error that is inherent to this process as the determination of the tipping points is a computation which converges monotonically to the true value as a function of the number of stochastic replicates used to determine the point. Conclusion: Our method of regression of these critical points represents an alternative to traditional parameter-sweeping or sensitivity analysis techniques. Essentially, the ANN computes the boundaries of the clinically relevant space as a function of the model’s parameterization, eliminating the need for a brute-force exploration of model parameter space. In doing so, we demonstrate the successful development of this ANN which will allows for an efficient exploration of model parameter space

Resource-based enactment and adaptation of workflows from activity diagrams

Workflow management deals with different types of dependencies among tasks, in particular data- and policy-driven. The ability to reason on dependencies of different type allows workflow designers to consider different alternatives, or to define customized flows, reducing non-determinism. We propose a resource-centered view, in which both data-dependency between tasks and plan-dependent ordering of tasks are expressed as production and consumption of resources. This view is translated into a rule-based formalism, expressed in terms of multi-set rewriting for workflow enactment. In turn, rules are themselves seen as resources, so that they are prone to the same rewriting process, in order to redefine process schemas. We show how workflows expressed as activity diagrams can be translated to the proposed formalism, exploiting enforced generative patterns applied to triple graph grammars, and how redefinition of workflow processes can occur through typical patterns of adaptation. We also discuss possible concrete syntaxes for the obtained rules

Operating guidelines for services

In the paradigm of service-oriented computing, companies organize their core competencies as services and may request other functionalities from services of other companies. Services provide high flexibility, platform independent loose coupling, and distributed execution. They may thus help to reduce the complexity of dynamically binding and integrating heterogenous processes within and across organizations. The vision of service-oriented architectures is to provide a framework for publishing new services, for on demand searching for and discovery of existing services, and for dynamically binding services to achieve common business goals. That way, each individual organization gains more flexibility to dynamically react on new challenges. As services may be created or modified, or collaborations may be restructured at any point in time, a new challenge arises in this setting—the challenge for deciding the compatibility of the composed services before their actual binding. Recent literature distinguishes four different aspects of service compatibility: syntactical, behavioral, semantical, and non-functional compatibility. In this thesis, we focus on behavioral compatibility and abstract from the other aspects. Potential behavioral incompatibilities between services include deadlocks (two services wait for a message of each other), livelocks (two services keep exchanging messages without progressing), and pending messages that have been sent but cannot be received anymore. For stateful services that interact via asynchronous message passing, deciding behavioral compatibility is far from trivial. Local changes to one service may introduce errors in some or even all other services of an interaction. The verification of behavioral compatibility suffers from state explosion problems and is restricted by privacy issues. That is, the parties of an interaction are essentially autonomous and may be competitors in other business fields. Consequently, they do not want to reveal the internals of their processes to the other participants in order to hide trade secrets. To systematically approach this challenge, we introduce a formal framework based on Petri nets and automata for service modeling and formalize behavioral compatibility as deadlock freedom of the composition of the services. The main contribution of this thesis is to introduce the concept of the operating guideline of a service. Operating guidelines provide a formal characterization of the set of all behaviorally compatible services R for a given service S. Usually, this set is infinite. However, the operating guideline OGS of a service S serves as a finite representation of this infinite set. Furthermore, the operating guideline of S reveals only internals that are inevitably necessary to decide behavioral compatibility with S. We provide a construction method of operating guidelines for finite-state services with bounded communication. Operating guidelines can be used in many applications in the context of serviceoriented computing. The most fundamental application is to support the discovery of behaviorally compatible services. To this end, we develop a matching procedure that efficiently decides whether a given service R is characterized by the operating guideline OGS of a service S. If R matches, then both services R and S are behaviorally compatible and can be bound together to interact with each other. If R does not match with OGS, then the services are behaviorally incompatible and may run into severe behavioral errors and not reach their common business goal. Operating guidelines can furthermore be applied in the novel research areas of service substitutability and the generation of adapter services, for instance. To this end, we develop methods to compare the sets of services characterized by the operating guidelines OGS and OGS0 . If OGS0 characterizes more services than OGS, then the service S can be substituted by the service S0 without loosing any behaviorally compatible interaction partner R. Furthermore, we show how to synthesize a service R from the operating guideline OGS such that R is behaviorally compatible to S by construction. All results presented in this thesis are implemented in our service analysis tool Fiona. Fiona may compute operating guidelines for services modeled as Petri nets. It may match a service with an operating guideline, compare operating guidelines for equivalence or an inclusion relation, and synthesize service adapters for behaviorally incompatible services. Together with the tool BPEL2oWFN— which translates web services specified in BPEL into Petri net models of the services—we can immediately apply our results to services that stem from practic