Processing relaxed skylines in PDMS using distributed data summaries

Hose, Katja; Lemke, Christian; Sattler, Kai-Uwe

doi:10.1145/1183614.1183676

Cited by 45 publications

(47 citation statements)

References 12 publications

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“…Skyline computation [4] has recently attracted considerable attention and has been studied in a variety of distributed systems, including web information systems [3], parallel systems [17], peer-to-peer systems [5,10,13,18,19,[21][22][23], mobile ad-hoc networks [14,20], as well as more generic distributed systems [7,16,24]. In the following, we provide an overview of existing approaches for distributed skyline computation.…”

Section: Related Workmentioning

confidence: 99%

“…Hose et al [13] use distributed data summaries for efficient processing of approximate skyline queries and provide guarantees for the completeness of the result. Skyline computation over a superpeer architecture has been studied in [18,19].…”

Section: Related Workmentioning

confidence: 99%

“…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%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…There has been a growing interest in distributed [1,7,12,18,19] and parallel [5,6,21] skyline computation lately. In [1,12], skyline processing is studied over distributed web sources.…”

Section: Related Workmentioning

confidence: 99%

“…Parallel query processing attempts to alleviate some of the deficiencies of such problematic situations and has been explored by the database research community for other operators such as joins, nearest neighbor queries, etc. Although the skyline operator has been thoroughly investigated in centralized settings [2,4,10,14,17], only recently parallel [5,6,21] and distributed [1,7,18,19] skyline query processing has attracted attention.…”

Section: Introductionmentioning

confidence: 99%

Angle-based space partitioning for efficient parallel skyline computation

Vlachou

Doulkeridis

Kotidis

2008

Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data

119

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Recently, skyline queries have attracted much attention in the database research community. Space partitioning techniques, such as recursive division of the data space, have been used for skyline query processing in centralized, parallel and distributed settings. Unfortunately, such grid-based partitioning is not suitable in the case of a parallel skyline query, where all partitions are examined at the same time, since many data partitions do not contribute to the overall skyline set, resulting in a lot of redundant processing.In this paper we propose a novel angle-based space partitioning scheme using the hyperspherical coordinates of the data points. We demonstrate both formally as well as through an exhaustive set of experiments that this new scheme is very suitable for skyline query processing in a parallel sharenothing architecture. The intuition of our partitioning technique is that the skyline points are equally spread to all partitions. We also show that partitioning the data according to the hyperspherical coordinates manages to increase the average pruning power of points within a partition. Our novel partitioning scheme alleviates most of the problems of traditional grid partitioning techniques, thus managing to reduce the response time and share the computational workload more fairly. As demonstrated by our experimental study, our technique outperforms grid partitioning in all cases, thus becoming an efficient and scalable solution for skyline query processing in parallel environments.

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Efficient skyline computation over distributed interval data

Ren

2017

Concurrency and Computation

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Summary The increasing volume of uncertain data has resulted in a dire need for supporting efficient uncertain data management. The skyline query as an important aspect of data management has received considerable attention in recent years, because of its importance in making intelligent decisions over complex data. Moreover, data collection and storage have become increasingly distributed, which makes the central assembly of data for storage and query infeasible and inefficient. Although many research efforts have been conducted to address the skyline query problem in various distributed scenarios, we still lack algorithms to address the queries over interval data, which is a special kind of attribute‐level uncertain data that widely exists in many applications. In this paper, we extensively study the skyline query over distributed interval data. We model the skyline query problem and define the distributed skyline query over interval data. Particularly, 2 efficient algorithms are proposed to retrieve the skylines progressively from distributed local sites with a highly optimized feedback framework. Moreover, we exploit 2 strategies for further improving the queries. Extensive experiments on synthetic and real datasets with real deployment are conducted to validate the effectiveness and efficiency of our proposals.

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Processing relaxed skylines in PDMS using distributed data summaries

Cited by 45 publications

References 12 publications

Efficient execution plans for distributed skyline query processing

Efficient execution plans for distributed skyline query processing

Angle-based space partitioning for efficient parallel skyline computation

Efficient skyline computation over distributed interval data

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