On supporting dynamic web service selection with histogramming

Pahlevan, Atousa; Chester, Sean; Thomo, Alex; Müller, Hausi A.

doi:10.1109/mesoca.2011.6049032

Cited by 2 publications

(1 citation statement)

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“…We examined context-sensitive QoS attributes [26] and their use in selecting services using aggregation and skyline-based algorithms [27]. Table 1 shows an example with SDDS-determined attributes, where the values are computed in a variety of ways; for example, visibility is obtained from weather services, such as AccuWeather http://www.accuweather.com/.…”

Section: Related Workmentioning

confidence: 99%

Dynamis: Effective Context-Aware Web Service Selection Using Dynamic Attributes

et al. 2015

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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.

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Section: Related Workmentioning

confidence: 99%