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

Atousa Pahlevan, Jean-Luc Duprat, Alex Thomo and Hausi Müller
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Atousa Pahlevan: Department of Computer Science, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada
Jean-Luc Duprat: Department of Computer Science, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada
Alex Thomo: Department of Computer Science, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada
Hausi Müller: Department of Computer Science, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada

Future Internet, 2015, vol. 7, issue 2, 1-30

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.

Keywords: service discovery; service selection; quality of service (QoS); Dynamis; aggregation; skyline; histogram; top k (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2015
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