Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
A model for web services discovery with QoS
ACM SIGecom Exchanges
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Service -Oriented Computing: Concepts, Characteristics and Directions
WISE '03 Proceedings of the Fourth International Conference on Web Information Systems Engineering
QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
QoS computation and policing in dynamic web service selection
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Efficient Continuous Skyline Computation
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Efficient and adaptive discovery techniques of Web Services handling large data sets
Journal of Systems and Software
Towards an Approach forWeb services Substitution
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
Efficient algorithms for Web services selection with end-to-end QoS constraints
ACM Transactions on the Web (TWEB)
Discovering the best web service
Proceedings of the 16th international conference on World Wide Web
Adaptive Service Composition in Flexible Processes
IEEE Transactions on Software Engineering
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Techniques to support Web Service selection and consumption with QoS characteristics
Journal of Network and Computer Applications
Angle-based space partitioning for efficient parallel skyline computation
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Combining global optimization with local selection for efficient QoS-aware service composition
Proceedings of the 18th international conference on World wide web
Selecting skyline services for QoS-based web service composition
Proceedings of the 19th international conference on World wide web
Computing Service Skyline from Uncertain QoWS
IEEE Transactions on Services Computing
Recommendation on Uncertain Services
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
Adapting skyline computation to the MapReduce framework: algorithms and experiments
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
Titan: a system for effective web service discovery
Proceedings of the 21st international conference companion on World Wide Web
MapReduce Based Skyline Services Selection for QoS-aware Composition
IPDPSW '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum
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With the development of web technologies and cloud computing, more and more services which provide similar functionality but differ in QoS are deployed on the Internet via cloud platforms. Recently, skyline analysis is adopted to select candidate services with better QoS to facilitate the process of QoS-aware service composition. However, the fast increasing number of services, multiple QoS attributes to be considered, and dynamic service environment pose a big challenge to skyline service selection.In this paper, we present a parallel skyline service selection method to improve the efficiency by upgrading the MapReduce paradigm. An angle-based dataspace partitioning approach is employed in our MapReduce based skyline service selection. In particular, we explore the dominance power of local skyline services to improve the efficiency of selection, and present two detailed algorithms. To handle the dynamic nature of service environment, we employ Paper-Tape (PT) model which is used to rapidly locate varying services, and present a dynamic skyline service selection algorithm based on PT model. By experimenting over both real and synthetical datasets, we demonstrate the efficiency of our proposed methods.