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Journal articles on the topic 'Skyline queries'

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Author: Grafiati
Published: 4 June 2021
Last updated: 25 April 2022

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1

Hose, Katja. "Skyline Queries." Datenbank-Spektrum 16, no. 3 (July 6, 2016): 247–51. http://dx.doi.org/10.1007/s13222-016-0229-2.

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2

Afrati, Foto N., Paraschos Koutris, Dan Suciu, and Jeffrey D. Ullman. "Parallel Skyline Queries." Theory of Computing Systems 57, no. 4 (April 16, 2015): 1008–37. http://dx.doi.org/10.1007/s00224-015-9627-3.

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3

Lee, Jongwuk, Gae-won You, Seung-won Hwang, Joachim Selke, and Wolf-Tilo Balke. "Interactive skyline queries." Information Sciences 211 (November 2012): 18–35. http://dx.doi.org/10.1016/j.ins.2012.04.007.

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4

Xin, Junchang, Zhiqiong Wang, Mei Bai, and Guoren Wang. "Reverse Skyline Computation over Sliding Windows." Mathematical Problems in Engineering 2015 (2015): 1–19. http://dx.doi.org/10.1155/2015/649271.

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Reverse skyline queries have been used in many real-world applications such as business planning, market analysis, and environmental monitoring. In this paper, we investigated how to efficiently evaluate continuous reverse skyline queries over sliding windows. We first theoretically analyzed the inherent properties of reverse skyline on data streams and proposed a novel pruning technique to reduce the number of data points preserved for processing continuous reverse skyline queries. Then, an efficient approach, called Semidominance Based Reverse Skyline (SDRS), was proposed to process continuous reverse skyline queries. Moreover, an extension was also proposed to handlen-of-Nand(n1,n2)-of-Nreverse skyline queries. Our extensive experimental studies have demonstrated the efficiency as well as effectiveness of the proposed approach with various experimental settings.
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5

Ruan, Pei Qi, Chuan Wei Xu, Ji Ting Huang, Lun Ke Qing, and Chang Qing Ji. "A Distributed Algorithm for Skyline Query Based on Pre-Clustering." Advanced Materials Research 756-759 (September 2013): 3982–86. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3982.

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With the increasing availability and mobile application of LBS (Location-Based Services), large scale spatial objects remind challenge in cloud environments. In order to retrieve a few data items within a very large structured data set, skyline queries are utilized to optimize a single respectively multiple criteria. In this paper, we develop a new pre-clustering-based skyline queries technique to address the skewed distribution problem. We also present distributed approaches that construct grid index and process skyline queries. We evaluate the effectiveness of our algorithms with extensive experiments using real data sets. The results demonstrate the efficiency and scalability of our skyline queries algorithms based on pre-clustering.
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6

Liu, Jinfei, Juncheng Yang, Li Xiong, Jian Pei, Jun Luo, Yuzhang Guo, Shuaicheng Ma, and Chenglin Fan. "Skyline Diagram: Efficient Space Partitioning for Skyline Queries." IEEE Transactions on Knowledge and Data Engineering 33, no. 1 (January 1, 2021): 271–86. http://dx.doi.org/10.1109/tkde.2019.2923914.

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7

Siddique, Md Anisuzzaman, Hao Tian, Mahboob Qaosar, and Yasuhiko Morimoto. "MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries." Algorithms 12, no. 8 (August 13, 2019): 166. http://dx.doi.org/10.3390/a12080166.

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The skyline query and its variant queries are useful functions in the early stages of a knowledge-discovery processes. The skyline query and its variant queries select a set of important objects, which are better than other common objects in the dataset. In order to handle big data, such knowledge-discovery queries must be computed in parallel distributed environments. In this paper, we consider an efficient parallel algorithm for the “K-skyband query” and the “top-k dominating query”, which are popular variants of skyline query. We propose a method for computing both queries simultaneously in a parallel distributed framework called MapReduce, which is a popular framework for processing “big data” problems. Our extensive evaluation results validate the effectiveness and efficiency of the proposed algorithm on both real and synthetic datasets.
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8

Lougmiri, Zekri. "A New Progressive Method for Computing Skyline Queries." Journal of Information Technology Research 10, no. 3 (July 2017): 1–21. http://dx.doi.org/10.4018/jitr.2017070101.

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Skyline queries are important in many fields, especially for decision making. In this context, objects or tuples of databases are defined according to some numerical and non numerical attributes. The skyline operator acts on the numerical ones. The algorithms that implements this skyline operator are genrally of progressive or non progressive. The progressive ones return the skyline operator during its execution while non preogressive alogrithms return the result at the end of its execution. This paper presents a new progressive algorithm for computing the skyline points. This algorithm is based on sorting as a preprocessing of the input. The authors present new theorems for deducing promptly the first skyline points and reducing the candidate space. A new version of Divide-and-Conquer algorithm is used for computing the final skyline. Intensive experimentations on both real and synthetic datasets show that our algorithm presents best performance comparatively to other methods.
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9

Bavirthi, Swathi Sowmya, and Supreethi K. P. "Systematic Review of Indexing Spatial Skyline Queries for Decision Support." International Journal of Decision Support System Technology 14, no. 1 (January 2022): 1–15. http://dx.doi.org/10.4018/ijdsst.286685.

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Residing in the data age, researchers inferred that huge amount of geo-tagged data is available and identified the importance of Spatial Skyline queries. Spatial or geographic location in conjunction with textual relevance plays a key role in searching Point of Interest (POI) of the user. Efficient indexing techniques like R-Tree, Quad Tree, Z-order curve and variants of these trees are widely available in terms of spatial context. Inverted file is the popular indexing technique for textual data. As Spatial skyline query aims at analyzing both spatial and skyline dominance, there is a necessity for a hybrid indexing technique. This article presents the review of spatial skyline queries evaluation that include a range of indexing techniques which concentrates on disk access, I/O time, CPU time. The investigation and analysis of studies related to skyline queries based upon the indexing model and research gaps are presented in this review.
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10

Ciaccia, Paolo, and Davide Martinenghi. "Reconciling skyline and ranking queries." Proceedings of the VLDB Endowment 10, no. 11 (August 2017): 1454–65. http://dx.doi.org/10.14778/3137628.3137653.

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11

Chomicki, Jan, Paolo Ciaccia, and Niccolo' Meneghetti. "Skyline queries, front and back." ACM SIGMOD Record 42, no. 3 (October 17, 2013): 6–18. http://dx.doi.org/10.1145/2536669.2536671.

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12

Chen, Yi-Chung, and Chiang Lee. "The σ-neighborhood skyline queries." Information Sciences 322 (November 2015): 92–114. http://dx.doi.org/10.1016/j.ins.2015.06.015.

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13

You, Gae-won, Mu-Woong Lee, Hyeonseung Im, and Seung-won Hwang. "The Farthest Spatial Skyline Queries." Information Systems 38, no. 3 (May 2013): 286–301. http://dx.doi.org/10.1016/j.is.2012.10.001.

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14

Son, Wanbin, Seung-won Hwang, and Hee-Kap Ahn. "MSSQ: Manhattan Spatial Skyline Queries." Information Systems 40 (March 2014): 67–83. http://dx.doi.org/10.1016/j.is.2013.10.001.

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15

Choi, Jong-Hyeok, Fei Hao, and Aziz Nasridinov. "HI-Sky: Hash Index-Based Skyline Query Processing." Applied Sciences 10, no. 5 (March 2, 2020): 1708. http://dx.doi.org/10.3390/app10051708.

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The skyline query has recently attracted a considerable amount of research interest in several fields. The query conducts computations using the domination test, where “domination” means that a data point does not have a worse value than others in any dimension, and has a better value in at least one dimension. Therefore, the skyline query can be used to construct efficient queries based on data from a variety of fields. However, when the number of dimensions or the amount of data increases, naïve skyline queries lead to a degradation in overall performance owing to the higher cost of comparisons among data. Several methods using index structures have been proposed to solve this problem but have not improved the performance of skyline queries because their indices are heavily influenced by the dimensionality and data amount. Therefore, in this study, we propose HI-Sky, a method that can perform quick skyline computations by using the hash index to overcome the above shortcomings. HI-Sky effectively manages data through the hash index and significantly improves performance by effectively eliminating unnecessary data comparisons when computing the skyline. We provide the theoretical background for HI-Sky and verify its improvement in skyline query performance through comparisons with prevalent methods.
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16

Park, Yoonjae, Jun-Ki Min, and Kyuseok Shim. "Parallel computation of skyline and reverse skyline queries using mapreduce." Proceedings of the VLDB Endowment 6, no. 14 (September 2013): 2002–13. http://dx.doi.org/10.14778/2556549.2556580.

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17

Zhang, Ming, and Reda Alhajj. "Skyline queries with constraints: Integrating skyline and traditional query operators." Data & Knowledge Engineering 69, no. 1 (January 2010): 153–68. http://dx.doi.org/10.1016/j.datak.2009.10.001.

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18

Yang, Yong Tao, Yi Jie Wang, Min Guo, and Xiao Yong Li. "Continuous Probabilistic Reverse Skyline Monitoring over Uncertain Data Streams." Applied Mechanics and Materials 380-384 (August 2013): 2681–86. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2681.

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Reverse skyline is useful for supporting many applications, such as marketing decision,environmental monitoring. Since the uncertainty of data is inherent in many scenarios, there is a needfor processing probabilistic reverse skyline queries. In this paper, we study the problem of efficientlyprocessing these queries on uncertain data streams. We first show the formal definitions of reverseskyline probability and probabilistic reverse skyline. Then we propose a new algorithm called CPRSto maintain the most recent N uncertain data elements and to process continuous queries on them.CPRS is based on R-tree, and efficient pruning techniques, one of which is based on a new structurenamed Characteristic Rectangle, are incorporated into it to handling the extra computing complexityarising from the uncertainty of data. Finally, extensive experiments demonstrate that our techniquesare very efficient in handling uncertain data streams.
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19

Shamsul Arefin, Mohammad. "Skyline Sets Queries for Incomplete Data." International Journal of Computer Science and Information Technology 4, no. 5 (October 31, 2012): 67–80. http://dx.doi.org/10.5121/ijcsit.2012.4506.

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20

Soudani, Nasrin Mazaheri, and Ahmad Baraani Dastgerdi. "The Spatial Nearest Neighbor Skyline Queries." International Journal of Database Management Systems 3, no. 4 (November 30, 2011): 65–79. http://dx.doi.org/10.5121/ijdms.2011.3406.

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21

Mindolin, Denis, and Jan Chomicki. "Preference elicitation in prioritized skyline queries." VLDB Journal 20, no. 2 (March 8, 2011): 157–82. http://dx.doi.org/10.1007/s00778-011-0227-9.

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22

Ren, Weilong, Xiang Lian, and Kambiz Ghazinour. "Skyline queries over incomplete data streams." VLDB Journal 28, no. 6 (October 17, 2019): 961–85. http://dx.doi.org/10.1007/s00778-019-00577-6.

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23

Gomaa, Ibrahim, and Hoda M. O. Mokhtar. "Continuous skyline queries in distributed environment." International Journal of Data Science 4, no. 1 (2019): 45. http://dx.doi.org/10.1504/ijds.2019.098360.

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24

Gomaa, Ibrahim, and Hoda M. O. Mokhtar. "Continuous skyline queries in distributed environment." International Journal of Data Science 4, no. 1 (2019): 45. http://dx.doi.org/10.1504/ijds.2019.10019816.

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25

De Matteis, Tiziano, Salvatore Di Girolamo, and Gabriele Mencagli. "Continuous skyline queries on multicore architectures." Concurrency and Computation: Practice and Experience 28, no. 12 (May 19, 2016): 3503–22. http://dx.doi.org/10.1002/cpe.3866.

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26

Zhiyong Huang, Hua Lu, Beng Chin Ooi, and A. K. H. Tung. "Continuous Skyline Queries for Moving Objects." IEEE Transactions on Knowledge and Data Engineering 18, no. 12 (December 2006): 1645–58. http://dx.doi.org/10.1109/tkde.2006.185.

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27

Lei Chen and Xiang Lian. "Efficient Processing of Metric Skyline Queries." IEEE Transactions on Knowledge and Data Engineering 21, no. 3 (March 2009): 351–65. http://dx.doi.org/10.1109/tkde.2008.146.

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28

Abidi, Amna, Sayda Elmi, Mohamed Anis Bach Tobji, Allel HadjAli, and Boutheina Ben Yaghlane. "Skyline queries over possibilistic RDF data." International Journal of Approximate Reasoning 93 (February 2018): 277–89. http://dx.doi.org/10.1016/j.ijar.2017.11.005.

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29

Lee, Jongwuk, Hyeonseung Im, and Gae-won You. "Optimizing skyline queries over incomplete data." Information Sciences 361-362 (September 2016): 14–28. http://dx.doi.org/10.1016/j.ins.2016.04.048.

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30

Chung, Yu-Chi, I.-Fang Su, and Chiang Lee. "Efficient computation of combinatorial skyline queries." Information Systems 38, no. 3 (May 2013): 369–87. http://dx.doi.org/10.1016/j.is.2012.10.005.

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31

Choi, Hyunsik, HaRim Jung, Ki Yong Lee, and Yon Dohn Chung. "Skyline queries on keyword-matched data." Information Sciences 232 (May 2013): 449–63. http://dx.doi.org/10.1016/j.ins.2012.01.045.

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32

Chen, Yi-Chung, and Chiang Lee. "Skyline Path Queries With Aggregate Attributes." IEEE Access 4 (2016): 4690–706. http://dx.doi.org/10.1109/access.2016.2602702.

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33

Son, Wanbin, Fabian Stehn, Christian Knauer, and Hee-Kap Ahn. "Top- k Manhattan spatial skyline queries." Information Processing Letters 123 (July 2017): 27–35. http://dx.doi.org/10.1016/j.ipl.2017.03.003.

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34

Jiang, Tao, Yunjun Gao, Bin Zhang, Dan Lin, and Qing Li. "Monochromatic and bichromatic mutual skyline queries." Expert Systems with Applications 41, no. 4 (March 2014): 1885–900. http://dx.doi.org/10.1016/j.eswa.2013.08.085.

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35

Ting, Kuo-Cheng, Ruei-Ping Wang, Yi-Chung Chen, Don-Lin Yang, and Hsi-Min Chen. "Finding m-similar users in social networks using the m-representative skyline query." Information Discovery and Delivery 45, no. 3 (August 21, 2017): 121–29. http://dx.doi.org/10.1108/idd-04-2017-0030.

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Purpose Using social networks to identify users with traits similar to those of the target user has proven highly effective in the development of personalized recommendation systems. Existing methods treat all dimensions of user data as a whole, despite the fact that most of the information related to different dimensions is discrete. This has prompted researchers to adopt the skyline query for such search functions. Unfortunately, researchers have run into problems of instability in the number of users identified using this approach. Design/methodology/approach We thus propose the m-representative skyline queries to provide control over the number of similar users that are returned. We also developed an R-tree-based algorithm to implement the m-representative skyline queries. Findings By using the R-tree based algorithm, the processing speed of the m-representative skyline queries can now be accelerated. Experiment results demonstrate the efficacy of the proposed approach. Originality/value Note that with this new way of finding similar users in the social network, the performance of the personalized recommendation systems is expected to be enhanced.
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36

Haddache, Mohamed, Allel Hadjali, and Hamid Azzoune. "Skyline refinement exploiting fuzzy formal concept analysis." International Journal of Intelligent Computing and Cybernetics 14, no. 3 (April 29, 2021): 333–62. http://dx.doi.org/10.1108/ijicc-11-2020-0181.

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PurposeThe study of the skyline queries has received considerable attention from several database researchers since the end of 2000's. Skyline queries are an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different, and often contradictory criteria are to be taken into account. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in the sense of Pareto) objects from a set of data. Skyline computation methods often lead to a set with a large size which is less informative for the end users and not easy to be exploited. The purpose of this paper is to tackle this problem, known as the large size skyline problem, and propose a solution to deal with it by applying an appropriate refining process.Design/methodology/approachThe problem of the skyline refinement is formalized in the fuzzy formal concept analysis setting. Then, an ideal fuzzy formal concept is computed in the sense of some particular defined criteria. By leveraging the elements of this ideal concept, one can reduce the size of the computed Skyline.FindingsAn appropriate and rational solution is discussed for the problem of interest. Then, a tool, named SkyRef, is developed. Rich experiments are done using this tool on both synthetic and real datasets.Research limitations/implicationsThe authors have conducted experiments on synthetic and some real datasets to show the effectiveness of the proposed approaches. However, thorough experiments on large-scale real datasets are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.Practical implicationsThe tool developed SkyRef can have many domains applications that require decision-making, personalized recommendation and where the size of skyline has to be reduced. In particular, SkyRef can be used in several real-world applications such as economic, security, medicine and services.Social implicationsThis work can be expected in all domains that require decision-making like hotel finder, restaurant recommender, recruitment of candidates, etc.Originality/valueThis study mixes two research fields artificial intelligence (i.e. formal concept analysis) and databases (i.e. skyline queries). The key elements of the solution proposed for the skyline refinement problem are borrowed from the fuzzy formal concept analysis which makes it clearer and rational, semantically speaking. On the other hand, this study opens the door for using the formal concept analysis and its extensions in solving other issues related to skyline queries, such as relaxation.
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37

Swidan, Marwa B., Ali A. Alwan, Sherzod Turaev, and Yonis Gulzar. "A Model for Processing Skyline Queries in Crowd-sourced Databases." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (May 1, 2018): 798. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp798-806.

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Nowadays, in most of the modern database applications, lots of critical queries and tasks cannot be completely addressed by machine. Crowd-sourcing database has become a new paradigm for harness human cognitive abilities to process these computer hard tasks. In particular, those problems that are difficult for machines but easier for humans can be solved better than ever, such as entity resolution, fuzzy matching for predicates and joins, and image recognition. Additionally, crowd-sourcing database allows performing database operators on incomplete data as human workers can be involved to provide estimated values during run-time. Skyline queries which received formidable attention by database community in the last decade, and exploited in a variety of applications such as multi-criteria decision making and decision support systems. Various works have been accomplished address the issues of skyline query in crowd-sourcing database. This includes a database with full and partial complete data. However, we argue that processing skyline queries with partial incomplete data in crowd-sourcing database has not received an appropriate attention. Therefore, an efficient approach processing skyline queries with partial incomplete data in crowd-sourcing database is needed. This paper attempts to present an efficient model tackling the issue of processing skyline queries in incomplete crowd-sourcing database. The main idea of the proposed model is exploiting the available data in the database to estimate the missing values. Besides, the model tries to explore the crowd-sourced database in order to provide more accurate results, when local database failed to provide precise values. In order to ensure high quality result could be obtained, certain factors should be considered for worker selection to carry out the task such as workers quality and the monetary cost. Other critical factors should be considered such as time latency to generate the results.
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38

Tang, Yingfeng, and Shiping Chen. "Supporting Continuous Skyline Queries in Dynamically Weighted Road Networks." Mathematical Problems in Engineering 2018 (September 10, 2018): 1–14. http://dx.doi.org/10.1155/2018/6749650.

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The paper focuses on the design of an optimum method for handling the continuous skyline query problem in road networks. Existing studies on processing the continuous skyline query focus exclusively on static road networks, which are limited because the state of roads in road networks is constantly changing. Therefore, to apply current methods for dynamically weighted road networks, a distributed skyline query method based on a grid partition method has been proposed in this paper. The method adopts the concepts of a distributed computing framework and road network preprocessing computations in which multiple parallel computing nodes are allocated and organized in grids. Using this approach, the road network map is simplified to a hub graph with much smaller scale such that the query load of the central node can be significantly reduced. The theoretical analysis and experimental results both indicate that, by using the proposed method, the system can achieve quick response time for users as well as a good balance between response times and accuracy. Therefore, it can be concluded that using the proposed method is beneficial for handling continuous skyline queries in a dynamically weighted road network.
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39

He, Guoliang, Lu Chen, Chen Zeng, Qiaoxian Zheng, and Guofu Zhou. "Probabilistic skyline queries on uncertain time series." Neurocomputing 191 (May 2016): 224–37. http://dx.doi.org/10.1016/j.neucom.2015.12.104.

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40

Park, Yoonjae, Jun-Ki Min, and Kyuseok Shim. "Processing of probabilistic skyline queries using MapReduce." Proceedings of the VLDB Endowment 8, no. 12 (August 2015): 1406–17. http://dx.doi.org/10.14778/2824032.2824040.

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41

Lin, Xin, Jianliang Xu, and Haibo Hu. "Range-Based Skyline Queries in Mobile Environments." IEEE Transactions on Knowledge and Data Engineering 25, no. 4 (April 2013): 835–49. http://dx.doi.org/10.1109/tkde.2011.229.

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Park, Yoonjae, Jun-Ki Min, and Kyuseok Shim. "Efficient Processing of Skyline Queries Using MapReduce." IEEE Transactions on Knowledge and Data Engineering 29, no. 5 (May 1, 2017): 1031–44. http://dx.doi.org/10.1109/tkde.2017.2654459.

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43

Zhu, Haoyang, Xiaoyong Li, Qiang Liu, and Zichen Xu. "Top-k Dominating Queries on Skyline Groups." IEEE Transactions on Knowledge and Data Engineering 32, no. 7 (July 1, 2020): 1431–44. http://dx.doi.org/10.1109/tkde.2019.2904065.

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44

Gulzar, Yonis, Ali A. Alwan, Radhwan Mohamed Abdullah, Qin Xin, and Marwa B. Swidan. "SCSA: Evaluating skyline queries in incomplete data." Applied Intelligence 49, no. 5 (December 7, 2018): 1636–57. http://dx.doi.org/10.1007/s10489-018-1356-2.

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45

Xiao, Ying-Yuan, and Yue-Guo Chen. "Efficient Distributed Skyline Queries for Mobile Applications." Journal of Computer Science and Technology 25, no. 3 (May 2010): 523–36. http://dx.doi.org/10.1007/s11390-010-9342-0.

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46

Huang, Zhen-Hua, Jian-Kui Guo, Sheng-Li Sun, and Wei Wang. "Efficient Optimization of Multiple Subspace Skyline Queries." Journal of Computer Science and Technology 23, no. 1 (January 2008): 103–11. http://dx.doi.org/10.1007/s11390-008-9112-4.

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47

Miao, Xiaoye, Yunjun Gao, Gang Chen, and Tianyi Zhang. "k -dominant skyline queries on incomplete data." Information Sciences 367-368 (November 2016): 990–1011. http://dx.doi.org/10.1016/j.ins.2016.07.034.

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48

Lee, Mu-Woong, Wanbin Son, Hee-Kap Ahn, and Seung-won Hwang. "Spatial skyline queries: exact and approximation algorithms." GeoInformatica 15, no. 4 (December 4, 2010): 665–97. http://dx.doi.org/10.1007/s10707-010-0119-y.

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49

Alwan, Ali A., Hamidah Ibrahim, Nur Izura Udzir, and Fatimah Sidi. "Processing skyline queries in incomplete distributed databases." Journal of Intelligent Information Systems 48, no. 2 (July 9, 2016): 399–420. http://dx.doi.org/10.1007/s10844-016-0419-2.

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Yang, Zhibang, Xu Zhou, Kenli Li, Yunjun Gao, and Keqin Li. "Progressive approaches to flexible group skyline queries." Knowledge and Information Systems 63, no. 6 (April 9, 2021): 1471–96. http://dx.doi.org/10.1007/s10115-021-01562-8.

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