Abstract
Quality web service discovery requires narrowing the search space from an overwhelming set of services down to the most relevant ones, while matching the consumer’s request. Today, the ranking of services only considers static attributes or snapshots of current attribute values, resulting in low-quality search results. To satisfy the user’s need for timely, well-chosen web services, we ought to consider quality of service attributes. The problem is that dynamic attributes can be difficult to measure, frequently fluctuate, are context-sensitive and depend on environmental factors, such as network availability at query time. In this paper, we propose the Dynamis algorithm to address these challenges effectively. Dynamis is based on well-established database techniques, such as skyline and aggregation. We illustrate our approach using observatory telescope web services and experimentally evaluate it using stock market data. In our evaluation, we show significant improvement in service selection over existing techniques.
Highlights
With the increasing proliferation of web services, selecting the best one for a consumer’s needs can be a challenging and often overwhelming task
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
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
Summary
With the increasing proliferation of web services, selecting the best one for a consumer’s needs can be a challenging and often overwhelming task. 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. They are described by attributes and typically represented by tuples in a database table. Services are described by (QoS) attributes and can be represented by tuples, and the aggregation and skyline paradigms can be applied to the service selection problem. The aggregation and skyline approaches encounter practical difficulties in the presence of services’ dynamic attributes. The remainder of this section describes both approaches (using a services context) and the root causes of those difficulties
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