## 1 Introduction  
With the increasing proliferation of web services, selecting the best one for a consumer's needs can be a challenging and often overwhelming task. Service consumers naturally expect automated and context-aware service discovery and selection techniques. Current mechanisms based on information retrieval (IR) techniques, particularly string similarity, are of limited use because the service descriptions on which the similarity is calculated are often short and ambiguous. A promising alternative is to evaluate the suitability of each service using quality of service (QoS) attributes. This is challenging in practice, because QoS attributes can be difficult to measure, frequently fluctuate, are context-sensitive and depend on environmental factors, such as network availability. We use observatory telescopes as services to illustrate our context-sensitive service discovery techniques. In this domain, geographical position, time of day and weather conditions affect the quality of service selection; these elements contribute to the context in which a picture of the celestial sphere is taken. For instance, one of the attributes specific to this domain is the visibility of the sky, a weather property; another is the relative position of a specific star with respect to the telescope and the darkness of the sky, properties of both geographical position and time. In contrast, the range of brightness in an image is another important QoS attribute for telescopes; the significance of the contrast attribute varies from user to user. In addition, users may request a picture with a specific composition: the number of objects captured in a photograph, and whether the image is to be in color or black and white. On certain days, particularly those when the telescope is offline due to maintenance, it may be impossible to determine a value for some attributes. In other words, there will be days where photographs are unavailable or the quality of the photographs is too low to recognize celestial properties. This telescope example illustrates why smart evaluation based on QoS attributes is useful and necessary to improve service selection. One approach is to ignore the fact that attribute values fluctuate and merely assume that all functionally-equivalent services are equally valuable. The user then has to manually sift through the available services, even in cases when one service outperforms or matches another on every measurement. This frustrates users and lowers the adoption rate of a service discovery system. A real-world case study for dynamic service discovery is the selection of personalized healthcare services: how to identify a relevant and qualitative set of healthcare services among the available ones. The services must be chosen according to context information gathered from bio-sensors and monitoring devices, in spite of large data volumes and transmission losses. Yet, the right information needs to be delivered to the right person, at the right time, in the right place, in the right manner. Service selection has to proceed in spite of missing or incomplete information [1,2]. The best techniques used in practice come in two flavours--those that do not consider QoS attributes [3, 4, 5, 6] and those that take a snapshot of the values into account [7, 8, 9]. In the former case, the real potential of QoS attributes is squandered. In the latter case, services are misrepresented by temporary fluctuations of QoS values or harshly penalized when the mechanism for obtaining those values fails. Both cases will inevitably arise in practice. In this paper, we investigate two approaches originating in the database community to deliver a well-chosen subset of services, each with its own advantages: context-sensitive ranking by aggregation [10]; and context-agnostic selection by skyline [11, 12]. While both approaches are applicable to our services setting, they require reliable, non-null values for each QoS attribute and, therefore, break down in the presence of dynamic attributes.