Healthcare enterprises involve complex processes that span diverse groups and organisations. These processes involve clinical and administrative tasks, large volumes of data, and large numbers of patients and personnel. The tasks can be performed either by humans or by automated systems. In the latter case, the tasks are supported by a variety of software applications and information systems which are very often heterogeneous, autonomous, and distributed. The development of systems to manage and automate these processes has increasingly played an important role in improving the efficiency of healthcare enterprises. In this paper we look at four healthcare and medical applications that involve investigative, clinical, and administrative functions. Based on these applications, we derive the requirements for developing enterprise applications that involve the coordination of a variety of tasks performed by humans, information systems, and legacy applications.

We present methods for optimally adapting Web processes to exogenous events while preserving inter-service constraints that necessitate coordination. For example, in a supply chain process, orders placed by a manufacturer may get delayed in arriving. In response to this event, the manufacturer has the choice of either waiting out the delay or changing the supplier. Additionally, there may be compatibility constraints between the different orders, thereby introducing the problem of coordination between them if the manufacturer chooses to change the suppliers. We focus on formulating the decision making models of the managers, who must adapt to external events while satisfying the coordination constraints, using Markov decision processes. Our methods range from being centralized and globally optimal in their adaptation but not scalable, to decentralized that is suboptimal but scalable to multiple managers. We also develop a hybrid approach that improves on the performance of the decentralized approach with a minimal loss of optimality. 1

Abstract. Discovering and composing individual Web Services into more complex yet new and more useful Web Processes is an important challenge. In this paper, we present three techniques for (semi) automatically composing Web Services into Web Processes by using their ontological descriptions and relationships to other services. In Interface-Matching Automatic composition technique, the possible compositions are obtained by checking semantic similarities between interfaces of individual services. Then these compositions are ranked considering their Quality of Services (QoS) and an optimum composition is selected. In Human-Assisted composition the user selects a service from a ranked list at certain stages. We also address automatic compositions in a Peer-to-Peer network.

Ambient intelligence promises to enable humans to smoothly interact with their environment, mediated by computer technology. In the literature on ambient intelligence, empirical scientists are not often mentioned. Yet they form an interesting target group for this technology. In this position paper, we describe a project aimed at realising an ambient intelligence environment for face-to-face meetings of researchers with different academic backgrounds involved in molecular biology “omics ” experiments. In particular, microarray experiments are a focus of attention because these experiments require multidisciplinary collaboration for their design, analysis, and interpretation. Such an environment is characterised by a high degree of complexity that has to be mitigated by ambient intelligence technology. By experimenting in a real-life setting, we will learn more about life scientists as a user group. 1

As Web services and service oriented architectures become pervasive in the business and scientific environments, there has been a growing focus on representing business and scientific processes using Web service based processes or Web processes. While workflow and other automation technologies have existed for a couple of decades, tools and frameworks in this space do not provide adequate support for the dynamism and adaptability required to represent and execute real world processes. With technological advances (e.g., RFID) that help in generating real time data, the next generation of Web process frameworks must evolve to provide capabilities for handling and reacting to such events. In addition, the large scale standardization of all aspects of businesses has set the stage for businesses to configure their processes on the fly with new or pre-existing business partners. This thesis is one of the first attempts to create a comprehensive framework for dynamic configuration and adaptation of Web processes. While we have evaluated this framework in the context of a supply chain, we believe that this framework can also be applied to other business and scientific processes. Our work is based on using a semantic framework that uses ontologies and semantic descriptions of Web services as an enabler of the two capabilities. The semantic descriptions of Web services are based on our recent W3C member submission WSDL-S. Much work has been done in operations research for business process optimization. However, there is a lot of domain knowledge that is used in conjunction with operations research techniques by experts for decision making. We explore adding greater automation to this decision making by capturing this domain knowledge in ontologies and using it in conjunction with Integer Linear Programming for dynamic process configuration. The other problem we address is that of process adaptation. While other approaches exist for process adaptation, none of them have considered uncertainly about when the event may occur. We present adaptation as a stochastic decision making problem and present an approach that uses Markov Decision Processes. Both configuration and adaptation have been evaluated comprehensively and our results clearly demonstrate their benefits.

We present methods for optimally adapting Web processes to exogenous events while preserving inter-service dependencies. For example, in a supply chain process, orders placed by the manufacturer may get delayed in arriving. In response to this event, the manufacturer has the choice of either waiting out the delay or changing the supplier. Additionally, there may be compatibility constraints between the different orders, thereby introducing the problem of coordination between them if the manufacturer chooses to change the suppliers. We present our methods within the framework of autonomic Web processes. This framework seeks to add properties of self-configuration, adaptation, and self-optimization to the traditional processes resulting in more dynamic and agile Web processes. We adopt the paradigm that an abstract Web process flow is pre-specified, and service managers are tasked with interacting with the actual Web services. We present two approaches for adapting the Web processes with dependencies.

Abstract A paradigm shift has occurred from centralized to distributed computing as the baseline upon which information systems are designed and implemented. This has raised the prospect of cooperation by coordination of computational, rather than physical, processes becoming the prevailing mode for large-scale systems. Just as, in business, e-business, soon, in science, e-science. The analogue of a business process in scientific contexts is an in vitro experiment, insofar as a major output of scientific practice is the knowledge obtained by the experimental method. Hence the concept of in silico experiments as the core component of a computational model of scientific activity now being referred to as e-science. Surprisingly, though, while the challenges presented by the ambition to coordinate e-business processes at very-coarse grains (often entire supply chains) are being seriously addressed, no comparable activity levels are yet discernible in e-science. This paper describes a platform for in silico experiments that is centred around a process coordination language called ISXL. ISXL projects to working scientists a model of in silico experiments that approximates more closely their in vitro practices than comparable efforts. An ISXL-specified experiment is (1) cast in a conceptual model that helps enforce the essence of the research method as applied to the empirical sciences; (2) may include explicit hypothesis formulation and validation (rather than simply the core functionality of evidence gathering); and (3) is long-lived, in that there is built-in support for denoting experiment specifications that evolve, as well as the results of past runs, which are, in turn, annotated and kept in an underlying persistent store. Here the focus is on (1) and (2), (3) is discussed in [7]. 1

Web services promise to revolutionise the way computational resources and business processes are offered and invoked in open, distributed systems, such as the Internet. These services are described using machine-readable meta-data, which enables consumer applications to automatically discover and provision suitable services for their workflows at run-time. However, current approaches have typically assumed service descriptions are accurate and deterministic, and so have neglected to account for the fact that services in these open systems are inherently unreliable and uncertain. Specifically, network failures, software bugs and competition for services may regularly lead to execution delays or even service failures. To address this problem, the process of provisioning services needs to be performed in a more flexible manner than has so far been considered, in order to proactively deal with failures and to recover workflows that have partially failed. To this end, we devise and present a heuristic strategy that varies the provisioning of services according to their predicted performance. Using simulation, we then benchmark our algorithm and show that it leads to a 700 % improvement in average utility, while successfully completing up to eight times as many workflows as approaches that do not consider service failures.