Heorhi Raik

Dynamic Adaptation of Fragment-Based and Context-Aware Business Processes

We propose a comprehensive framework for adaptivity of service-based applications, which exploits the concept of process fragments as a way to model reusable process knowledge and to allow for the dynamic, incremental, context-aware composition of such fragments into adaptable service-based applications. The framework provides a set of adaptation mechanisms that, combined through adaptation strategies, are able to solve complex adaptation problems. An implementation of the proposed solution is presented and evaluated on a real world scenario from the logistics domain.

Adaptation of service-based business processes by context-aware replanning

Service-based business processes are typically used by organizations to achieve business goals through the coordinated execution of a set of activities implemented as services and service compositions. Since they are executed in dynamic, open and non-deterministic environments, business processes often need to be adapted to exogenous context changes and execution problems. In this paper we provide an adaptation approach that can automatically adapt business processes to run-time context changes that impede achievement of a business goal. We define a formal framework that adopts planning techniques to automatically derive necessary adaptation activities on demand. The adaptation consists in identifying recovery activities that guarantee that the execution of a business process can be successfully resumed and, as a consequence, the business goals are achieved. The solution proposed is evaluated on a real-world scenario from the logistics domain.

On-the-Fly Adaptation of Dynamic Service-Based Systems: Incrementality, Reduction and Reuse

On-the-fly adaptation is where adaptation activities are not explicitly represented at design time but are discovered and managed at run time considering all aspect of the execution environments. In this paper we present a comprehensive framework for the on-the-fly adaptation of highly dynamic service-based systems. The framework relies on advanced context-aware adaptation techniques that allow for i incremental handling of complex adaptation problems by interleaving problem solving and solution execution, ii reduction in the complexity of each adaptation problem by minimizing the search space according to the specific execution context, and iii reuse of adaptation solutions by learning from past executions. We evaluate the applicability of the proposed approach on a real world scenario based on the operation of the Bremen sea port.

ASTRO-CAptEvo: Dynamic Context-Aware Adaptation for Service-Based Systems

Adaptivity is a key feature of service-based systems to fully exploit the advantages offered by the service paradigm. In this paper we present a comprehensive framework that can be used to define and support highly adaptable context-aware service-based business processes. In our approach, all the adaptation-related tasks, from detecting an adaptation problem to finding a solution and applying it to a running process instance, are performed automatically at run time. We also show how the framework has been implemented and applied to a real-world scenario from the logistics domain.

Modelling and automated composition of user-centric services

User-centric services bring additional constraints to the problem of automated service composition. While in business-centric settings the services are orchestrated in order to accomplish a specific business task, user-centric service composition should allow the user to decide and control which tasks are executed and how. This requires the ability not only to automatically compose different, often unrelated, services on the fly, but also to generate a flexible interaction protocol that allows the user to control and coordinate composition execution. In this paper we present a novel automated composition approach that aims to support user-centric service provisioning. Specifically, we associate the service to so-called service objects and provide a declarative notation to express composition requirements in terms of the evolution of those objects. On top of these objects we also define the user control activities and constraints. Using the automated planning techniques, our approach generates a service composition that orchestrates services in a way it is requested by the user.

An Approach for Collective Adaptation in Socio-Technical Systems

Socio-technical systems are systems where autonomous humans and computational entities collectively collaborate with each other to satisfy their goals in a dynamic environment. To be resilient, such systems need to adapt to unexpected human behaviours and exogenous changes in the environment. In this paper, we describe a framework for the development of social-technical systems where adaptation is itself a collective process driven by the awareness of capabilities, goals, constraints and preferences of humans and entities, and knowledge of the environment. The adaptation is controlled by a multi-criteria decision making function combined with an analytic hierarchic process (AHP). We present our approach, the collective adaptation algorithm, and its application to a smart mobility scenario.

CAptEvo: context-aware adaptation and evolution of business processes

CAptEvo is a framework for the adaptation and evolution of service-based business processes operating in dynamic execution environments. In this demonstration, we apply the CAptEvo to a case study from the logistics domain and show its advantages in handling highly complex dynamic real-world business applications.

A context-aware framework for dynamic composition of process fragments in the internet of services

In the last decade, many approaches to automated service composition have been proposed. However, most of them do not fully exploit the opportunities offered by the Internet of Services (IoS). In this article, we focus on the dynamicity of the execution environment, that is, any change occurring at run-time that might affect the system, such as changes in service availability, service behavior, or characteristics of the execution context. We indicate that any IoS-based application strongly requires a composition framework that supports for the automation of all the phases of the composition life cycle, from requirements derivation, to synthesis, deployment and execution. Our solution to this ambitious problem is an AI planning-based composition framework that features abstract composition requirements and context-awareness. In the proposed approach most human-dependent tasks can be accomplished at design time and the few human intervention required at run time do not affect the system execution. To demonstrate our approach in action and evaluate it, we exploit the ASTRO-CAptEvo framework, simulating the operation of a fully automated IoS-based car logistics scenario in the Bremerhaven harbor.

Collective Adaptation in Process-Based Systems

A collective adaptive system is composed of a set of heterogeneous, autonomous and self-adaptive entities that come into a collaboration with one another in order to improve the effectiveness with which they can accomplish their individual goals. In this paper, we offer a characterization of ensembles, as the main concept around which systems that exhibit collective adaptability can be built. Our conceptualization of ensembles enables to define a collective adaptive system as an emergent aggregation of autonomous and self-adaptive process-based elements. To elucidate our approach to ensembles and collective adaptation, we draw an example from a scenario in the urban mobility domain, we describe an architecture that enables that approach, and we show how our approach can address the problems posed by the motivating scenario.

Data-Flow Requirements for Dynamic Service Composition

Data-flow requirements is an important aspect of service composition. Although several approaches have been proposed to specify data-flow requirements, they cannot be efficiently exploited in dynamic setting, for example, where the composition participants (i.e., component services) need to be dynamically replaced. In this paper, we propose a new modeling methodology for data-flow requirements, in which we explicitly distinguish a data model of a composite service from data models of component services. We show that following our methodology, a composition can be adjusted to various dynamic changes with significantly less effort. We implement our solution using planning techniques and make its basic evaluation on a scenario from the travel domain.