Progressive skyline computation in database systems

Papadias, Dimitris; Tao, Yufei; Fu, Greg; Seeger, Bernhard

doi:10.1145/1061318.1061320

Cited by 766 publications

(663 citation statements)

References 26 publications

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“…In fact the basic operation of collecting maxima during a single scan of the input data can be found at the core of several state-of-the-art skyline algorithms [5,8,9,10,11] and is illustrated in Figure 2. The -block‖ in its name refers to the fact that it linearly scans over the data set and continuously maintains a block (or window) of data elements containing the maximal elements with respect to the data read so far.…”

Section: The Design Space For Skyline Query Evaluationmentioning

confidence: 99%

Highly Scalable Multiprocessing Algorithms for Preference-Based Database Retrieval

Selke

Lofi

Balke

2010

Database Systems for Advanced Applications

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Abstract. Until recently algorithms continuously gained free performance improvements due to ever increasing processor speeds. Unfortunately, this development has reached its limit. Nowadays, new generations of CPUs focus on increasing the number of processing cores instead of simply increasing the performance of a single core. Thus, sequential algorithms will be excluded from future technological advances. Instead, highly scalable parallel algorithms are needed to fully tap new hardware potentials. In this paper we establish a design space for parallel algorithms in the domain of personalized database retrieval, taking skyline algorithms as a representative example. We will investigate the spectrum of base operations of different retrieval algorithms and various parallelization techniques to develop a set of highly scalable and high-performing skyline algorithms for different retrieval scenarios. Finally, we extensively evaluate these algorithms to showcase their superior characteristics.

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Section: The Design Space For Skyline Query Evaluationmentioning

confidence: 99%

Highly Scalable Multiprocessing Algorithms for Preference-Based Database Retrieval

Selke

Lofi

Balke

2010

Database Systems for Advanced Applications

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“…[2][3] [9] proposed algorithms that could terminate earlier based on sorting functions. [4][5] [6] proposed index algorithms that could progressively report results. Since these approaches focus on single relation, they consider skyline computation independent from join phase, which renders the query execution to be blocking.…”

Section: Related Workmentioning

confidence: 99%

“…The reason is that for a tuple to be selected as being in the skyline, it would need to be compared against all other tuples to ensure that no other tuples [12] for computing skylines on relational databases focus on reducing the number of tuple pair comparisons. This is achieved using indexing [4][5] [6], or partitioning data into subsets [11] where some subsets would contain points that can quickly be determined to be in or pruned from the skyline result.…”

Section: Introductionmentioning

confidence: 99%

“…Identifying such tuples would allow pruning them from advancing to the skyline phase and avoid the expensive dominance checks needed for skyline computation on those tuples. Alternative techniques to the JFSL approach [4][5] [6][11] [12][16] employ specialized indexing and storage schemes which are not typical in RDF data and require preprocessing or storage in multiple formats.…”

Section: Introductionmentioning

confidence: 99%

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Efficiently Evaluating Skyline Queries on RDF Databases

Chen

Gao

Anyanwu

2011

The Semanic Web: Research and Applications

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Abstract. Skyline queries are a class of preference queries that compute the pareto-optimal tuples from a set of tuples and are valuable for multi-criteria decision making scenarios. While this problem has received significant attention in the context of single relational table, skyline queries over joins of multiple tables that are typical of storage models for RDF data has received much less attention. A naïve approach such as a join-first-skyline-later strategy splits the join and skyline computation phases which limit opportunities for optimization. Other existing techniques for multi-relational skyline queries assume storage and indexing techniques that are not typically used with RDF which would require a preprocessing step for data transformation. In this paper, we present an approach for optimizing skyline queries over RDF data stored using a vertically partitioned schema model. It is based on the concept of a "Header Point" which maintains a concise summary of the already visited regions of the data space. This summary allows some fraction of non-skyline tuples to be pruned from advancing to the skyline processing phase, thus reducing the overall cost of expensive dominance checks required in the skyline phase. We further present more aggressive pruning rules that result in the computation of near-complete skylines in significantly less time than the complete algorithm. A comprehensive performance evaluation of different algorithms is presented using datasets with different types of data distributions generated by a benchmark data generator.

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“…Recently, many researchers from database research community pay much attention to skyline queries [1][2][3][4][5][6][7][8] due to its wide applications related to multi-criteria decision making. Specifically, given a d-dimensional data set D, a skyline query [3] retrieves all the data objects which are not dominated by other objects in D. A data object p dominates another data object p′ if p is not worse than p′ in all dimensions and strictly better than p′ in at least one dimension.…”

Section: Introductionmentioning

confidence: 99%

Reverse k-skyband query based on reuse technology

Jiang¹,

Zhang²,

Chen³

et al. 2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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Abstract-In this paper, we introduce a new approach to finish reverse k-skyband (RkSB) query which returns all the points in given dataset P whose dynamic k-skyband contains specific query object q. The main ideas include reuse technology and early stopping. The former save the information of node accesses during R-tree search into an auxiliary heap so that dramatically decreases the I/O cost and improves the CPU efficiency. This is because RkSB needs to execute global k-skyband and window query to finish the computation and the reuse information can be used in the phase of window query. The latter improves the efficiency of RkSB computation. The experimental results conducted on real datasets and synthetic datasets show that our proposed algorithm is effective and has a better performance both I/O cost and CPU efficiency than the algorithm based on reverse BBS (Branch and Bound Skyline) method.

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Progressive skyline computation in database systems

Cited by 766 publications

References 26 publications

Highly Scalable Multiprocessing Algorithms for Preference-Based Database Retrieval

Highly Scalable Multiprocessing Algorithms for Preference-Based Database Retrieval

Efficiently Evaluating Skyline Queries on RDF Databases

Reverse k-skyband query based on reuse technology

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