Mohammed Zarifi Eslami

Decision as a Service: Separating Decision-making from Application Process Logic

In a service-oriented architecture, adaptive and evolvable applications should be able to select, configure and compose different existing application services to deal with the changes which can arise from runtime contextual changes or the change of user requirements and preferences. To support this, hybrid service composition approaches have been proposed, in which the core of application logic, which is rather stable, is specified in terms of processes while rules are employed to specify the conditions and constraints to adapt the application behaviour. The rules are then exposed as a decision service which can be employed by the process to make adaptation decision with respect to runtime circumstances. The interaction between processes and decision services are generally performed in synchronous request-response manner. We argue that such an interaction is not efficient to support different types of adaptation at runtime and therefore asynchronous interaction should also be supported. In this paper, we present an adaptive service provisioning architecture and a decision service template allowing both synchronous request-response interaction and asynchronous notification. To motivate the proposed architecture and the decision service template, we use a blood pressure monitoring scenario from the homecare domain. We also explain the implementation of the proposed approach based on commercially available rule and process engines. Finally, we discuss: 1) what is the efficient way (synchronous request-response interaction vs. asynchronous notification) of calling decision service to execute different types of decision rules? and 2) to what extent the use of decision service facilitates dealing with the unforeseen changes?

Personalized Service Creation by Non-technical Users in the Homecare Domain

One of the conditions for the successful introduction of ICT-based homecare services is to allow non-technical persons such as home nurses to personalize these services. We refer to this process of homecare service personalization as service tailoring. Service tailoring can be done by configuring and composing previously developed and deployed service building blocks. In this paper, we describe an approach that employs predefined information of care-receivers, called user profile, to hide most of the technical details from care-givers who do the service tailoring. First, we define the information to be included in a user profile and patterns that represent composition structures corresponding to common homecare tasks experienced in homecare. Then, we define how the service tailoring process can exploit information contained in the predefined user profiles. After that, we illustrate the approach with a tailoring scenario.

Dynamic homecare service provisioning architecture

The realization of homecare services is difficult because of dynamicity requirements and constraints that exist in this domain. These requirements call for a dynamic service provisioning, i.e., adaptivity and adaptability of the (composition of) homecare services in response to a) frequently occurring changes like change in the location or vital signs, or b) slowly developing changes like extent of impairments of a care-receiver. In this paper, we explain our understanding of a dynamic service provisioning platform, its requirements and constraints. As such, we design an architecture based on an existing hybrid service provisioning approach (a combination of process and rule) and related architectural patterns. Then, we implement this approach using the commercially available process and rule engines. We demonstrate how a homecare application can be deployed, executed and how the application can adapt itself to the frequently occurring changes at runtime. We also demonstrated how a care-giver can modify the behaviour of the application to adapt the slowly occurring changes. Finally, we discuss the pros and cons of the approach and explain our future plan.

Flexible home care automation adapting to the personal and evolving needs and situations of the patient

Health monitoring and healthcare provisioning at home (i.e., outside the hospital) have received increasingly attention as a possible and partial solution for addressing the problems of an aging population. There are still many technological issues that need to be solved before home healthcare systems can be really cost-effective and efficient. However, in this paper we will highlight another category of issues which we call architectural challenges. Each patient is unique, and each patient has a unique lifestyle, living environment and course of life. Therefore it should be possible to personalize the services provided by home healthcare systems according to the needs and preferences of each individual patient, and it should be possible to make incremental adaptations at later points in time if this is necessary due to, for example, a changing health condition. The architectural challenges and solution directions related to this has been discussed in this paper.

Assumption-based risk identification method (ARM) in dynamic service provisioning

In this paper we consider service-oriented applications composed of component services provided by different, economically independent service providers. As in all composite applications, the component services are composed and configured to meet requirements for the composite application. However, in a field experiment of composite service-oriented applications wef found that, although the services as actually delivered by the service providers meet their requirements, there is still a mismatch across service providers due to unstated assumptions, and that this mismatch causes an incorrect composite application to be delivered to end-users. Identifying and analyzing these initially unstated assumptions turns requirements engineering for service-oriented applications into risk analysis. In this paper, we describe a field experiment with an experimental service-oriented homecare system, in which unexpected behavior of the system turned up unstated assumptions about the contributing service providers. We then present an assumptions-driven risk identification method that can help identifying these risks, and we show how we applied this method in the second iteration of the field experiment. The method adapts some techniques from problem frame diagrams to identify relevant assumptions on service providers. The method is informal, and takes the “view from nowhere” in that it does not result in a specification of the component services, but for every component service delivers a set of assumptions that the service must satisfy in order to contribute to the overall system requirements. We end the paper with a discussion of generalizability of this method.