Fast linear expected-time alogorithms for computing maxima and convex hulls
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 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
Robust Cardinality and Cost Estimation for Skyline Operator
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Progressive skylining over web-accessible databases
Data & Knowledge Engineering
Processing relaxed skylines in PDMS using distributed data summaries
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th 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 sort-based skyline evaluation
ACM Transactions on Database Systems (TODS)
Parallel Skyline Computation on Multicore Architectures
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
BSkyTree: scalable skyline computation using a balanced pivot selection
Proceedings of the 13th International Conference on Extending Database Technology
Scalable skyline computation using a balanced pivot selection technique
Information Systems
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Skyline queries have gained attention for supporting multi-criteria analysis of large-scale datasets. While a lot of skyline algorithms have been proposed, most of the algorithms build upon pre-computed index structures that cannot generally be supported over sensor data of dynamically changing attribute values. We aim to design a scalable non-index skyline computation algorithm for sensor data. More specifically, we propose Algorithm Sky Tree constructing a dynamic lattice that divides a specific region into several subregions based on a pivot point maximizing dominance region. Such structure enables to perform region-wise dominance tests, which eliminates unnecessary point-wise dominance tests. In addition, we ensure the progressiveness that has not been supported by any non-index algorithm, where we can identify k points maximizing the sum of dominance regions as the greedy approximation method. The k points are used to reduce communication cost between sensors in computing global skyline. Our evaluation results validate the efficiency of Algorithm SkyTree, both in terms of response time and communication overhead, over existing algorithms.