Processing k-skyband, constrained skyline, and group-by skyline queries on incomplete data

Gao, Yunjun; Miao, Xiaoye; Cui, Huiyong; Chen, Gang; Li, Qing

doi:10.1016/j.eswa.2014.02.033

Cited by 36 publications

(23 citation statements)

References 48 publications

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“…Examples include, to name but a few, (i) subspace skyline computation [25,34,39]; (ii) reverse skyline query [12,15,28]; (iii) metric skyline retrieval [9,13]; (iv) continuous skyline query [19,23]; (v) distributed skyline retrieval [8,18,40]; (vi) uncertain skyline query [33,47]; and (vii) skyline computation on data streams [29,35,38] and incomplete data [16], respectively.…”

Section: Related Workmentioning

confidence: 99%

“…2(a) are sorted as p 1 ; p 2 ; p 3 ; p 6 ; p 4 ; p 9 ; p 7 ; p 8 ; p 10 ; p 5 ; p 11 ; p 12 f g . It then recursively finds skyline objects s and computes lðsÞ and sðsÞ until H ¼ £ (lines [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Specifically, for the data objects encountered by the sweep line, SB processes them one by one from bottom to up.…”

Section: Sweep Based Algorithmmentioning

confidence: 99%

“…(i) If e is within a single cell, for each object s in the set S 00 of all the skyline objects dominating e, lðsÞ and sðsÞ are increased by COUNT(eÞ and 1/jS 00 j Â COUNT(e) respectively (lines [11][12][13][14]. (ii) If e crosses multiple cells, the algorithm inserts the child entries of e into the heap H for expansion (lines [16][17]. This traversal terminates once H ¼ £.…”

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confidence: 99%

See 2 more Smart Citations

Efficient algorithms for finding the most desirable skyline objects

Gao

Liu

Chen

et al. 2015

Knowledge-Based Systems

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Section: Related Workmentioning

confidence: 99%

Section: Sweep Based Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

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Efficient algorithms for finding the most desirable skyline objects

Gao

Liu

Chen

et al. 2015

Knowledge-Based Systems

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“…Thus, if a movie dominates many other movies, it is very likely that the movie is rather popular. Note that, MovieLens dataset is widely utilized in many previous works, including [1], [2]. Intuitively, a TKD query could identify the k most popular movies for moviegoers.…”

Section: Introductionmentioning

confidence: 99%

Top-<italic>k</italic> Dominating Queries on Incomplete Data

Miao

Gao

Zheng

et al. 2016

IEEE Trans. Knowl. Data Eng.

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The top-k dominating (TKD) query returns the k objects that dominate the maximum number of objects in a given dataset. It combines the advantages of skyline and top-k queries, and plays an important role in many decision support applications. Incomplete data exists in a wide spectrum of real datasets, due to device failure, privacy preservation, data loss, and so on. In this paper, for the first time, we carry out a systematic study of TKD queries on incomplete data, which involves the data having some missing dimensional value(s). We formalize this problem, and propose a suite of efficient algorithms for answering TKD queries over incomplete data. Our methods employ some novel techniques, such as upper bound score pruning, bitmap pruning, and partial score pruning, to boost query efficiency. Extensive experimental evaluation using both real and synthetic datasets demonstrates the effectiveness of our developed pruning heuristics and the performance of our presented algorithms.

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“…Furthermore, Miao et al (2013;Gao et al, 2014) proposed a set of algorithms with the intention of processing skyline queries in incomplete database. This includes baseline algorithm, Virtual Point based algorithm (VP) and k-iSkyband algorithm (kISB); these algorithms employed the concept of the work proposed in (Khalefa et al, 2008).…”

Section: Related Literature Of Skyline Queries On Incomplete Databasesmentioning

confidence: 99%

Processing Skyline Queries in Incomplete Database: Issues, Challenges and Future Trends

Gulzar

Alwan

Salleh

et al. 2017

Journal of Computer Science

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In many contemporary database applications such as multi-criteria decision-making and real-time decision-support applications, data mining, ecommerce and recommendation systems, users need query operators to process the data aiming at finding the results that best fit with their preferences. Skyline queries are one of the most predominant query operators that privileges to find the query results that return only those data items whose dimension vector is not dominated by any other data item in the database. Because of their usefulness and ubiquity, skyline queries have been incorporated into different types of databases such as complete, incomplete and uncertain. This paper attempts to survey and analyze the previous works proposed to process skyline queries in the incomplete database. The discussion focuses on examining these approaches highlighting the strengths and the weaknesses of each work. Besides, we also discuss in detail the current challenges in processing skyline queries in the incomplete database and investigate the impact of incomplete data on skyline operation. A summary of the most well-known works has been reported to identify the limitations of these works. Some recommendations and future work directions have been drawn to help researchers investigate the unsolved problems related to skyline queries in a database system.

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Processing k-skyband, constrained skyline, and group-by skyline queries on incomplete data

Cited by 36 publications

References 48 publications

Efficient algorithms for finding the most desirable skyline objects

Efficient algorithms for finding the most desirable skyline objects

Top-<italic>k</italic> Dominating Queries on Incomplete Data

Processing Skyline Queries in Incomplete Database: Issues, Challenges and Future Trends

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