Parallel Distributed Processing of Constrained Skyline Queries by Filtering

Cui, Bin; Lu, Hua; Xu, Quanqing; Chen, Lijiang; Dai, Yafei; Zhou, Yongluan

doi:10.1109/icde.2008.4497463

Cited by 61 publications

(69 citation statements)

References 13 publications

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“…In the list, the best choice usually comes from the skyline, i.e., one of {r 1 , r 3 , r 4 } (See Figure 1 (b)). A number of efficient algorithms for computing skyline have been reported in the literature [1,2,3,4,5,6,7,8,9,10]. All of these works have several limitations.…”

Section: Introductionmentioning

confidence: 99%

“…Using this normal vector, we can find new tangent point o 258 , which is (10, 6). The new tangent point expands the initial facet into two facets, which are the facet surrounded by P 1 = (4, 23) and (10,6) and the facet surrounded by (10,6) and P 2 = (23, 3).…”

Section: Algorithm Data Preprocessingmentioning

confidence: 99%

“…The core of their scheme is to visit more entries simultaneously and to enable effective pruning strategies. Cui et al [10] introduced skyline queries in large-scale distributed environments without the assumption of any overlay structures and propose PaDSkyline algorithm. PaDSkyline is an algorithm that significantly reduces the response time by performing parallel processing over site groups produced by a partition algorithm.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Skyline Sets Queries for Incomplete Data

Arefin¹,

Morimoto²

2012

IJCSIT

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Section: Introductionmentioning

confidence: 99%

Section: Algorithm Data Preprocessingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Skyline Sets Queries for Incomplete Data

Arefin¹,

Morimoto²

2012

IJCSIT

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“…Many works consider parallelized methods that utilize multiple processors or obtain useful partitions of the dataset for parallel processing [23][24][25][26]. We focus on effectively processing skyline query on a special type of data.…”

Section: Related Workmentioning

confidence: 99%

Efficiently Processing Skyline Query on Multi-Instance Data

Chiu¹,

Hsu²

2017

J Inf Process Syst

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Related to the maximum vector problem, a skyline query is to discover dominating tuples from a set of tuples, where each defines an object (such as a hotel) in several dimensions (such as the price and the distance to the beach). A tuple, an instance of an object, dominates another tuple if it is equally good or better in all dimensions and better in at least one dimension. Traditionally, skyline queries are defined upon singleinstance data or upon objects each of which is associated with an instance. However, in some cases, an object is not associated with a single instance but rather by multiple instances. For example, on a review website, many users assign scores to a product or a service, and a user's score is an instance of the object representing the product or the service. Such data is an example of multi-instance data. Unlike most (if not all) others considering the traditional setting, we consider skyline queries defined upon multi-instance data. We define the dominance calculation and propose an algorithm to reduce its computational cost. We use synthetic and real data to evaluate the proposed methods, and the results demonstrate their utility.

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“…The houses in the skyline set are those that are not worse than (not dominated by) any other house in both price and distance. Skyline queries have been studied both in centralized and distributed environments [3][4][5]7,10,13,14,16,18,[20][21][22][23][24]. However, planning the execution of skyline queries on different (potentially overlapping) data fractions, in order to obtain the skyline set of the entire dataset efficiently, has not received adequate attention in the related work.…”

Section: Introductionmentioning

confidence: 99%

Efficient execution plans for distributed skyline query processing

Rocha-Junior

Vlachou

Doulkeridis

et al. 2011

Proceedings of the 14th International Conference on Extending Database Technology

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In this paper, we study the generation of efficient execution plans for skyline query processing in large-scale distributed environments. In such a setting, each server stores autonomously a fraction of the data, thus all servers need to process the skyline query. An execution plan defines the order in which the individual skyline queries are processed on different servers, and influences the performance of query processing. Querying servers consecutively reduces the amount of transferred data and the number of queried servers, since skyline points obtained by one server prune points in the subsequent servers, but also increases the latency of the system. To address this trade-off, we introduce a novel framework, called SkyPlan, for processing distributed skyline queries that generates execution plans aiming at optimizing the performance of query processing. Thus, we quantify the gain of querying consecutively different servers. Then, execution plans are generated that maximize the overall gain, while also taking into account additional objectives, such as bounding the maximum number of hops required for the query or balancing the load on different servers fairly. Finally, we present an algorithm for distributed processing based on the generated plan that continuously refines the execution plan during in-network processing. Our framework consistently outperforms the state-of-the-art algorithm.

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Parallel Distributed Processing of Constrained Skyline Queries by Filtering

Cited by 61 publications

References 13 publications

Skyline Sets Queries for Incomplete Data

Skyline Sets Queries for Incomplete Data

Efficiently Processing Skyline Query on Multi-Instance Data

Efficient execution plans for distributed skyline query processing

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