Lina Barakat

Efficient Correlation-Aware Service Selection

Accounting for quality correlations among web services when performing service composition is essential to obtain more accurate quality estimations of service combinations, thus providing users with better composite solutions. Yet, most current composition approaches fail to address such correlations by assuming independence between services regarding their quality values. In response, this paper presents a correlation-aware composition approach, where quality dependencies among services are modelled and considered during composite service selection. Moreover, to improve selection efficiency, correlation-aware search space reduction techniques are introduced, which prune out uninteresting service compositions prior to selection. The effectiveness of the approach, in terms of time and optimality, is demonstrated via experimental results.

Efficient Multi-granularity Service Composition

Dynamic composition of services provides the ability to build complex distributed applications at run time by combining existing services, thus coping with a large variety of complex requirements that cannot be met by individual services alone. However, with the increasing amount of available services that differ in granularity (amount of functionality provided) and qualities, selecting the best combination of services becomes very complex. In response, this paper addresses the challenges of service selection, and makes a twofold contribution. First, a rich representation of compositional planning knowledge is provided, allowing the expression of multiple decompositions of tasks at arbitrary levels of granularity. Second, two distinct search space reduction techniques are introduced, the application of which, prior to performing service selection, results in significant improvement in selection performance in terms of execution time, which is demonstrated via experimental results.

Reactive service selection in dynamic service environments

Due to the highly dynamic nature of services (web services can enter or leave the system at any time, or change their characteristics), adaptation to change during service composition is necessary to meet user needs. Yet current approaches to change handling detect quality violations and service unavailability only after their occurrence (after executing the corresponding service), resulting in undesired situations at execution time from which recovery (usually through costly replanning) might not always be possible. In response, this paper presents a novel reactive selection algorithm, which adapts to changes in the environment efficiently while performing the selection, ensuring that the selected composite service is executable, satisfactory and optimal prior to execution. The algorithms effectiveness is demonstrated via experimental results.

Adaptive composition in dynamic service environments

Due to distribution, participant autonomy and lack of local control, service-based systems operate in highly dynamic and uncertain environments. In the face of such dynamism and volatility, the ability to manage service changes and exceptions during composite service execution is a vital requirement. Most current adaptive composition approaches, however, fail to address service changes without causing undesirable disruptions in execution or considerably degrading the quality of the composite application. In response, this paper presents a novel adaptive execution approach, which efficiently handles service changes occurring at execution time, for both repair and optimisation purposes. The adaptation is performed as soon as possible and in parallel with the execution process, thus reducing interruption time, increasing the chance of a successful recovery, and producing the most optimal solution according to the current environment state. The effectiveness of the proposed approach is demonstrated both analytically and empirically through a case study evaluation applied in the framework of learning object composition. In particular, the results show that, even with frequent changes (e.g. 20 changes per service execution), or in the cases where interference with execution is non-preventable (e.g., when an executed service delivers unanticipated quality values), our approach manages to recover from the situation with minimal interruption.

Efficient adaptive QoS-based service selection

Distributed service-based systems are becoming increasingly common, with a vast range of resources and functionalities being exposed as services over open networks (e.g. the web and Grid systems). Due to the distribution, participant autonomy and lack of local control, such systems operate in highly dynamic and uncertain environments, in which services can be added, removed or change their characteristics, at any time. Thus, adaptation to change during service composition is essential to meet user needs. Yet, even when service changes occur at an early stage (e.g. at selection time), current adaptive composition approaches delay their detection until after the quality violating or unavailable service is invoked, resulting in a costly recovery during execution and, in some cases, permanently unachievable goals. In response, this paper presents a novel reactive selection algorithm, which adapts to service changes efficiently while performing the selection, ensuring an executable, satisfactory and optimal solution prior to execution. The effectiveness of the algorithm is demonstrated analytically and empirically through a case study evaluation applied in the framework of learning object composition.

A service-based system for malnutrition prevention and self-management

Malnutrition is considered one of the root causes for the occurrence of other diseases. It is particularly common in the ageing population, where it requires more efficient handling and management to enable longer home independent living. However, to achieve this, a number of related challenges need to be overcome, especially those related to management of health and disease let alone other social and logistical barriers. This paper presents the design of a distributed system that enables homecare management in the context of self-feeding and malnutrition prevention through balanced nutritional intake. The design employs a service-based system that incorporates a number of services including monitoring of activities, nutritional reasoning for assessing feeding habits, diet recommendation for food planning, and marketplace invocation for automating food shopping to meet dietary requirements. The solution is deployed in a small pilot in 12 elder adult houses that, in early results, demonstrates its holistic user-centred scalable approach for malnutrition self-management. A Service-based System holistic approach to enable malnutrition selfmanagement, through identification and monitoring.Nutrition-aware services to enable short and long-term reasoning of nutrition to work within an nonintrusive older adults environment.Semantic knowledge driven services approach to achieve reasoning over complex set of diet related QoS factors for (food) service selection

Context-Driven Assessment of Provider Reputation in Composite Provision Scenarios

Service-oriented computing has become the de-facto way of developing distributed applications and, in such systems, an accurate assessment of reputation is essential for selecting between alternative providers. Existing methods typically assess reputation on a combination of direct experiences by the client being provided with a service and third party recommendations, but they exclude from consideration a wealth of information about the context of providers’ previous actions. Such information is particularly important in composite service provision scenarios, where providers may delegate sub-tasks to others, and thus their success or failure needs to be interpreted in this context and reputation assessed according to responsibility. In response, to enable richer, more accurate reputation mechanisms, this paper models the delegation knowledge underlying a composite service provision, and incorporates such knowledge into the reputation assessment process, adjusting the contributions of past interactions with the composite service provider according to delegation context relevance. Experimental results demonstrate the effectiveness of the proposed approach.

An Agent-Based Service Marketplace for Dynamic and Unreliable Settings

In order to address the unreliable nature of service providers, and the dynamic nature of services (their quality values could change frequently over time due to various factors), this paper proposes a probabilistic, multi-valued quality model for services, capable of capturing uncertainty in their quality values by assigning each quality attribute with multiple potential values (or ranges of values), along with a corresponding probability distribution over these values. The probability distribution indicates the most likely quality value for an attribute at the current time step, but also notifies discovery applications of the possibility of other, possibly worse outcomes, thus ultimately facilitating more reliable service selection and composition via avoiding services with high uncertainty. Such uncertainty-aware, multi-valued quality models of services are maintained via an agent-based service marketplace, where each service is associated with a software agent, capable of learning the time-varying probability distributions of its quality values through applying online learning techniques, based on the service’s past performance information. The experiments conducted demonstrate the effectiveness of the proposed approach.

A Distributed Service-Based System for Homecare Self-Management

Aging is becoming a critical issue for Europe and many countries around the world. It is imposing significant burden on societies and their national services. Enabling longer home independent living is seen one of the most promising ways to overcome this issue. However, to achieve a number of challenges need to be overcome, especially those related to management of health and disease let alone other social and logistical barriers. One of these challenges is malnutrition, which is considered one of the root causes for the occurrence of other diseases. This paper presents the design of a distributed system that enables homecare in the context of management of self-feeding through balanced nutritional intake. The design employs a service-based system that incorporates a number of services including monitoring of activities, nutritional reasoning for assessing feeding habits, diet recommending for food planning, and marketplace invocation for automating food shopping to meet dietary requirements.

Stereotype reputation with limited observability

Assessing trust and reputation is essential in multi-agent systems where agents must decide who to interact with. Assessment typically relies on the direct experience of a trustor with a trustee agent, or on information from witnesses. Where direct or witness information is unavailable, such as when agent turnover is high, stereotypes learned from common traits and behaviour can provide this information. Such traits may be only partially or subjectively observed, with witnesses not observing traits of some trustees or interpreting their observations differently. Existing stereotype-based techniques are unable to account for such partial observability and subjectivity. In this paper we propose a method for extracting information from witness observations that enables stereotypes to be applied in partially and subjectively observable dynamic environments. Specifically, we present a mechanism for learning translations between observations made by trustor and witness agents with subjective interpretations of traits. We show through simulations that such translation is necessary for reliable reputation assessments in dynamic environments with partial and subjective observability.