Efficient Computation of Skyline Queries on Incomplete Dynamic Data

Skyline query is a typical preference query method. Due to its capacity of extracting interesting information from multi-dimensional datasets abided by multiple criterions, the skyline query has been extensively studied. All of the existing studies assume that the data are complete and available, which may not hold in many real applications because of device exception, privacy protection and other reasons. Datasets with missing attribute values or missing tuples are called incomplete datasets. In this paper, we mainly discussed the case of incomplete attribute values in a dynamic dataset. First, considering the dynamic dataset, we propose the kISkyline algorithm based on the traditional sliding window model with a split bucket strategy. We then propose the sISkyline algorithm based on a real point, virtual point, and shadow point with a split bucket strategy along with the traditional sliding window model. Finally, simulation results are provided to demonstrate the feasibility and effectiveness of these two new algorithms.

[1]  Bernhard Seeger,et al.  An optimal and progressive algorithm for skyline queries , 2003, SIGMOD '03.

[2]  Yunjun Gao,et al.  Top-k Dominating Queries on Incomplete Data , 2016, IEEE Trans. Knowl. Data Eng..

[3]  Beng Chin Ooi,et al.  Efficient Progressive Skyline Computation , 2001, VLDB.

[4]  Jignesh M. Patel,et al.  Efficient Continuous Skyline Computation , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[5]  Karl Aberer,et al.  Evaluating top-k queries over incomplete data streams , 2009, CIKM.

[6]  Chuan-Ming Liu,et al.  An Effective Probabilistic Skyline Query Process on Uncertain Data Streams , 2015, EUSPN/ICTH.

[7]  Hamidah Ibrahim,et al.  An Efficient Approach for Processing Skyline Queries in Incomplete Multidimensional Database , 2016 .

[8]  Hongjun Lu,et al.  Stabbing the sky: efficient skyline computation over sliding windows , 2005, 21st International Conference on Data Engineering (ICDE'05).

[9]  Yongtao Guan,et al.  Practical Issues in Imputation-Based Association Mapping , 2008, PLoS genetics.

[10]  Stathes Hadjiefthymiades,et al.  A time optimized scheme for top-k list maintenance over incomplete data streams , 2015, Inf. Sci..

[11]  Yufei Tao,et al.  Maintaining sliding window skylines on data streams , 2006, IEEE Transactions on Knowledge and Data Engineering.

[12]  Yuan Tian,et al.  Z-SKY: an efficient skyline query processing framework based on Z-order , 2010, The VLDB Journal.

[13]  Anthony K. H. Tung,et al.  Continuous Skyline Queries for Moving Objects , 2006, IEEE Transactions on Knowledge and Data Engineering.

[14]  Wolf-Tilo Balke,et al.  Skyline queries in crowd-enabled databases , 2013, EDBT '13.

[15]  Hamidah Ibrahim,et al.  Deriving skyline points over dynamic and incomplete databases , 2017 .

[16]  Donald Kossmann,et al.  The Skyline operator , 2001, Proceedings 17th International Conference on Data Engineering.

[17]  Jennifer Widom,et al.  Models and issues in data stream systems , 2002, PODS.

[18]  Donald Kossmann,et al.  Shooting Stars in the Sky: An Online Algorithm for Skyline Queries , 2002, VLDB.

[19]  Mohamed F. Mokbel,et al.  Skyline Query Processing for Incomplete Data , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[20]  Gang Chen,et al.  Processing k-skyband, constrained skyline, and group-by skyline queries on incomplete data , 2014, Expert Syst. Appl..

[21]  Lei Zou,et al.  Dynamic Skyline Queries in Large Graphs , 2010, DASFAA.

[22]  Hamidah Ibrahim,et al.  A Model for Processing Skyline Queries over a Database with Missing Data , 2015 .

[23]  P. Sreenivasa Kumar,et al.  Finding Skylines for Incomplete Data , 2013, ADC.