Abstract
Dynamic skyline queries are practical in many applications. For example, if no data exist to fully satisfy a query q in an information system, the data “closer” to the requirements of q can be retrieved as answers. Finding the nearest neighbors of q can be a solution; yet finding the data not dynamically dominated by any other data with respect to q, i.e. the dynamic skyline regarding q can be another solution. A data point p is defined to dynamically dominate another data point s, if the distance between each dimension of p and the corresponding dimension of q is no larger than the corresponding distance regarding s and q and at least in one dimension, the corresponding distance regarding p and q is smaller than that regarding s and q. Some approaches for answering dynamic skyline queries have been proposed. However, the existing approaches only consider the query as a point rather than a range in each dimension, also frequently issued by users. We make the first attempt to solve a problem of computing dynamic skylines considering range queries in this paper. To deal with this problem, we propose an efficient algorithm based on the grid index and a novel variant of the well-known Z-order curve. Moreover, a series of experiments are performed to evaluate the proposed algorithm and the experiment results demonstrate that it is effective and efficient.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Borzsonyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of the 17th International Conference on Data Engineering, ICDE 2001, Heidelberg, Germany, pp. 421–430 (2001)
Bartolini, I., Ciaccia, P., Patella, M.: SaLSa: Computing the skyline without scanning the whole sky. In: Proceedings of the 2006 ACM International Conference on Information and Knowledge Management, CIKM 2006, Arlington, Virginia, USA, pp. 405–414 (2006)
Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: Proceedings of the 19th International Conference on Data Engineering, ICDE 2003, Bangalore, India, pp. 717–719 (2003)
Chen, L., Lian, X.: Efficient processing of metric skyline queries. IEEE Trans. Knowl. Data Eng. 21(3), 351–365 (2009)
Dellis, E., Seeger, B.: Efficient Computation of Reverse Skyline Queries. In: Proceedings of the 33nd International Conference on Very Large Data Bases, VLDB 2007, Vienna, Austria, pp. 291–302 (2007)
Deng, K., Zhou, X., Shen, H.T.: Multi-source skyline query processing in road networks. In: Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, Istanbul, Turkey, pp. 796–805 (2007)
Fuhry, D., Jin, R., Zhang, D.: Efficient skyline computation in metric space. In: Proceedings of the 12th International Conference on Extending Database Technology, EDBT 2009, Saint-Petersburg, Russia, pp. 1042–1051 (2009)
Godfrey, P., Shipley, R., Gryz, J.: Maximal vector computation in large data sets. In: Proceedings of the 31st International Conference on Very Large Data Bases, VLDB 2005, Trondheim, Norway, pp. 229–240 (2005)
Lee, K.C.K., Zheng, B., Li, H., Lee, W.C.: Approaching the skyline in Z order. In: Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB 2007, Vienna, Austria, pp. 279–290 (2007)
Orenstein, J.A., Merret, T.H.: A class of data structures for associate searching. In: Proceedings of the 3rd ACM SIGACT-SIGMOD Symposium on Principles of Database Systems, PODS 1984, Waterloo, Canada, pp. 294–305 (1984)
Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. ACM Trans. Database Syst. 30(1), 41–82 (2005)
Random dataset generator for SKYLINE operator evaluation, http://randdataset.projects.postgresql.org/
Sacharidis, D., Bouros, P., Sellis, T.K.: Caching dynamic skyline queries. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 455–472. Springer, Heidelberg (2008)
Sharifzadeh, M., Shahabi, C.: The spatial skyline queries. In: Proceedings of the 32nd International Conference on Very Large Data Bases, VLDB 2006, Seoul, Korea, pp. 751–762 (2006)
Su, H.Z., Wang, E.T., Chen, A.L.P.: Continuous Probabilistic Skyline Queries over Uncertain Data Streams. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010. LNCS, vol. 6261, pp. 105–121. Springer, Heidelberg (2010)
Tan, K.L., Eng, P.K., Ooi, B.C.: Efficient progressive skyline computation. In: Proceedings of the 27th International Conference on Very Large Data Bases, VLDB 2001, Roma, Italy, pp. 301–310 (2001)
Zhang, S., Mamoulis, N., Cheung, D.W.: Scalable skyline computation using object-based space partitioning. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, SIGMOD 2009, Providence, Rhode Island, pp. 483–494 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, WC., Wang, E.T., Chen, A.L.P. (2011). Dynamic Skylines Considering Range Queries. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20152-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-20152-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20151-6
Online ISBN: 978-3-642-20152-3
eBook Packages: Computer ScienceComputer Science (R0)