This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Computing Service Skyline from Uncertain QoWS
January-March 2010 (vol. 3 no. 1)
pp. 16-29
ASCII Text
x 
 
Qi Yu, Athman Bouguettaya, "Computing Service Skyline from Uncertain QoWS," IEEE Transactions on Services Computing, vol. 3, no. 1, pp. 16-29, January-March, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TSC.2010.7,
author = {Qi Yu and Athman Bouguettaya},
title = {Computing Service Skyline from Uncertain QoWS},
journal ={IEEE Transactions on Services Computing},
volume = {3},
number = {1},
issn = {1939-1374},
year = {2010},
pages = {16-29},
doi = {http://doi.ieeecomputersociety.org/10.1109/TSC.2010.7},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Services Computing
TI - Computing Service Skyline from Uncertain QoWS
IS - 1
SN - 1939-1374
SP16
EP29
EPD - 16-29
A1 - Qi Yu,
A1 - Athman Bouguettaya,
PY - 2010
KW - Quality of service
KW - service optimization
KW - service selection
KW - skyline analysis
KW - uncertainty.
VL - 3
JA - IEEE Transactions on Services Computing
ER -
 
Qi Yu, Rochester Institute of Technology, Rochester
Athman Bouguettaya, CSIRO, ICT Center, Acton ACT, Australia
The performance of a service provider may fluctuate due to the dynamic service environment. Thus, the quality of service actually delivered by a service provider is inherently uncertain. Existing service optimization approaches usually assume that the quality of service does not change over time. Moreover, most of these approaches rely on computing a predefined objective function. When multiple quality criteria are considered, users are required to express their preference over different (and sometimes conflicting) quality attributes as numeric weights. This is rather a demanding task and an imprecise specification of the weights could miss user-desired services. We present a novel concept, called p-dominant service skyline. A provider S belongs to the p-dominant skyline if the chance that S is dominated by any other provider is less than p. Computing the p-dominant skyline provides an integrated solution to tackle the above two issues simultaneously. We present a p-R-tree indexing structure and a dual-pruning scheme to efficiently compute the p-dominant skyline. We assess the efficiency of the proposed algorithm with an analytical study and extensive experiments.

[1] F. Barbon, P. Traverso, M. Pistore, and M. Trainotti, "Run-Time Monitoring of Instances and Classes of Web Service Compositions," Proc. Int'l Conf. Web Services (ICWS), 2006.
[2] S. Borzsonyi, D. Kossmann, and K. Stocker, "The Skyline Operator," Proc. Int'l Conf. Data Eng. (ICDE), 2001.
[3] C.Y. Chan, H.V. Jagadish, K.L. Tan, A.K.H. Tung, and Z. Zhang, "Finding k-Dominant Skylines in High Dimensional Space," Proc. SIGMOD, 2006.
[4] R. Cheng, D.V. Kalashnikov, and S. Prabhakar, "Evaluating Probabilistic Queries over Imprecise Data," Proc. SIGMOD, 2003.
[5] R. Cheng, Y. Xia, S. Prabhakar, R. Shah, and J.S. Vitter, "Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data," Proc. Int'l Conf. Very Large Data Bases (VLDB), 2004.
[6] J. Chomicki, P. Godfrey, J. Gryz, and D. Liang, "Skyline with Presorting," Proc. Int'l Conf. Data Eng. (ICDE), 2003.
[7] M. Comuzzi and B. Pernici, "A Framework for QoS-Based Web Service Contracting," ACM Trans. Web, vol. 3, no. 3, pp. 1-52, 2009.
[8] P. Godfrey, R. Shipley, and J. Gryz, "Maximal Vector Computation in Large Data Sets," Proc. Int'l Conf. Very Large Data Bases (VLDB), 2005.
[9] A. Guttman, "R-Trees: A Dynamic Index Structure for Spatial Searching," Readings in Database Systems, pp. 599-609, Morgan Kaufmann, 1988.
[10] R. Jurca, B. Faltings, and W. Binder, "Reliable QoS Monitoring Based on Client Feedback," Proc. Conf. World Wide Web (WWW), 2007.
[11] D. Kossmann, F. Ramsak, and S. Rost, "Shooting Stars in the Sky: An Online Algorithm for Skyline Queries," Proc. Int'l Conf. Very Large Data Bases (VLDB), 2002.
[12] H.P. Kriegel, P. Kunath, M. Pfeifle, and M. Renz, "Probabilistic Similarity Join on Uncertain Data," Proc. Int'l Conf. Database Systems for Advanced Applications (DASFAA), 2006.
[13] H.T. Kung, F. Luccio, and F.P. Preparata, "On Finding the Maxima of a Set of Vectors," J. ACM, vol. 22, no. 4, pp. 469-476, 1975.
[14] D. Papadias, Y. Tao, G. Fu, and B. Seeger, "An Optimal and Progressive Algorithm for Skyline Queries," Proc. SIGMOD, 2003.
[15] J. Pei, B. Jiang, X. Lin, and Y. Yuan, "Probabilistic Skylines on Uncertain Data," Proc. Int'l Conf. Very Large Data Bases (VLDB), 2007.
[16] F.P. Preparata and M.I. Shamos, Computational Geometry: An Introduction. Springer-Verlag, 1985.
[17] A.D. Sarma, O. Benjelloun, A.Y. Halevy, and J. Widom, "Working Models for Uncertain Data," Proc. Int'l Conf. Data Eng. (ICDE), 2006.
[18] U. Srivastava, J. Widom, K. Munagala, and R. Motwani, "Query Optimization over Web Services," Proc. Int'l Conf. Very Large Data Bases (VLDB), 2006.
[19] K.-L. Tan, P.-K. Eng, and B.C. Ooi, "Efficient Progressive Skyline Computation," Proc. Int'l Conf. Very Large Data Bases (VLDB), 2001.
[20] Y. Tao, R. Cheng, X. Xiao, W.K. Ngai, B. Kao, and S. Prabhakar, "Indexing Multi-Dimensional Uncertain Data with Arbitrary Probability Density Functions," Proc. Int'l Conf. Very Large Data Bases (VLDB), 2005.
[21] T. Yu, Y. Zhang, and K.J. Lin, "Efficient Algorithms for Web Services Selection with End-to-End QoS Constraints," ACM Trans. Web, vol. 1, no. 1, 2007.
[22] L. Zeng, B. Benatallah, M. Dumas, J. Kalagnanam, and Q. Sheng, "Quality-Driven Web Service Composition," Proc. Conf. World Wide Web (WWW), 2003.

Index Terms:
Quality of service, service optimization, service selection, skyline analysis, uncertainty.
Citation:
Qi Yu, Athman Bouguettaya, "Computing Service Skyline from Uncertain QoWS," IEEE Transactions on Services Computing, vol. 3, no. 1, pp. 16-29, Jan.-March 2010, doi:10.1109/TSC.2010.7
Usage of this product signifies your acceptance of the Terms of Use.