Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Stabbing the Sky: Efficient Skyline Computation over Sliding Windows
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Maximal vector computation in large data sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Maintaining Sliding Window Skylines on Data Streams
IEEE Transactions on Knowledge and Data Engineering
Skyline Queries Against Mobile Lightweight Devices in MANETs
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Continuous Skyline Queries for Moving Objects
IEEE Transactions on Knowledge and Data 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
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient skyline computation over low-cardinality domains
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Approaching the skyline in Z order
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient computation of reverse skyline queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient processing of top-k dominating queries on multi-dimensional data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Distributed Skyline Retrieval with Low Bandwidth Consumption
IEEE Transactions on Knowledge and Data Engineering
Minimizing the communication cost for continuous skyline maintenance
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Efficient and Progressive Algorithms for Distributed Skyline Queries over Uncertain Data
ICDCS '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems
Efficient and adaptive distributed skyline computation
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Continuous skyline monitoring over distributed data streams
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Efficient execution plans for distributed skyline query processing
Proceedings of the 14th International Conference on Extending Database Technology
PMJoin: optimizing distributed multi-way stream joins by stream partitioning
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Parallelizing skyline queries for scalable distribution
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
Parallel skyline queries over uncertain data streams in cloud computing environments
International Journal of Web and Grid Services
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Two-tier streaming settings are a typical dynamic environment where continuous skylines represent an important semantic indicator for multiple attributes. To monitor skylines over the dynamic data in such settings, one needs to continuously update the skyline query results in order to reflect the new data values. This paper tackles the problem of continuous skyline monitoring on a central query server over dynamic data from multiple data sites. Simply sending the updates of tuple values to the server is cost-prohibitive. Therefore, we propose an approach that allows the central server to collaborate with the data sites to monitor the possible skyline changes. By doing so, the processing load is distributed over all the data sites instead of only on the central server. Furthermore, this collaborative approach minimizes the bandwidth consumption between the server and the data sites, which is often critical in a widely distributed environment such as a wide-area sensor network. We give theoretical upper bounds for the computation costs and communication costs of the proposed collaborative approach. We also conduct extensive experiments on both synthetic and real data sets. The experimental results demonstrate that our collaborative approach is efficient, scalable and well-balanced in terms of communication costs and computation costs.