With the increasing availability of Web services and adoption of services oriented paradigm, there is a growing need to dynamically compose services for realizing complex user tasks. While service composition is itself an important problem, a key issue is also how to support users in selecting the most appropriate compositions of services to fulfill a task. In existing dynamic services selection approaches, combinations of services are repeatedly discovered (e.g., using ontology-based matching techniques) and selected by users whenever needed. To improve their effectiveness, we propose a new technique that provides an efficient access to what is named a "task memory". A task memory is used to provide users with a context-aware service selection by recommending combinations of services that are most appropriate in a given context. A task memory is formed using the service composition history and their metadata. We present an incremental approach for building the task memory in which we monitor how users use and rank the services. The continuous updates of the task memory over time will result in more fine-tuned recommendations for composite services.

With the proliferation of Web services, it is becoming increasingly important to support the users in selecting the most appropriate compositions of services for a task. We propose a new service discovery and selection framework that utilises the concept of task memories and a social network of task memories. A task memory captures the service composition history and their meta-data such as associated context and user rating. A network of task memories is formed to realise an effective task memory sharing platform among the users.

The growing number of online accessible services call for effective techniques to support users in discovering, selecting, and aggregating services. We present WS-Advisor, a framework for enabling users to capture and share task memories. A task memory represents knowledge (e.g., context and user rating) about services selection history for a given task. WS-Advisor provides a declarative language that allows users to share task definitions and task memories with other users and communities. The service selection component of this framework enables a user agent to improve its service selection recommendations by leveraging task memories of other user agents with which the user share tasks in addition to the local task memories.