Skyline queries with constraints: Integrating skyline and traditional query operators
Data & Knowledge Engineering
Randomized multi-pass streaming skyline algorithms
Proceedings of the VLDB Endowment
Top-k queries on temporal data
The VLDB Journal — The International Journal on Very Large Data Bases
Prominent streak discovery in sequence data
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic skylines on uncertain data: model and bounding-pruning-refining methods
Journal of Intelligent Information Systems
Continuous distance-based skyline queries in road networks
Information Systems
Proceedings of the VLDB Endowment
Parallel skyline queries over uncertain data streams in cloud computing environments
International Journal of Web and Grid Services
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In many applications, we need to analyze a large number of time series. Segments of time series demonstrating dominating advantages over others are often of particular interest. In this paper, we advocate interval skyline queries, a novel type of time series analysis queries. For a set of time series and a given time interval [i : j], an interval skyline query returns the time series which are not dominated by any other time series in the interval. We illustrate the usefulness of interval skyline queries in applications. Moreover, we develop an on-the-fly method and a view-materialization method to online answer interval skyline queries on time series. The on-the-fly method keeps the minimum and the maximum values of the time series using radix priority search trees and sketches, and computes the skyline at the query time. The view-materialization method maintains the skylines over all intervals in a compact data structure. Through theoretical analysis and extensive experiments, we show that both methods only require linear space and are efficient in query answering as well as incremental maintenance.