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Articoli di riviste sul tema "Skyline queries"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

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

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2

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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Abstract (sommario):

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 continuo

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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

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

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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 e

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6

Liu, Jinfei, Juncheng Yang, Li Xiong, et al. "Skyline Diagram: Efficient Space Partitioning for Skyline Queries." IEEE Transactions on Knowledge and Data Engineering 33, no. 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 (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

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8

Lougmiri, Zekri. "A New Progressive Method for Computing Skyline Queries." Journal of Information Technology Research 10, no. 3 (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 bas

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9

De Lorenzis, Emilio, and Davide Martinenghi. "Partitioning Strategies for Parallel Computation of Flexible Skylines." Algorithms 18, no. 3 (2025): 141. https://doi.org/10.3390/a18030141.

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Abstract (sommario):

While classical skyline queries identify interesting data within large datasets, flexible skylines introduce preferences through constraints on attribute weights, and further reduce the data returned. However, computing these queries can be time-consuming for large datasets. We propose and implement a parallel computation scheme consisting of a parallel phase followed by a sequential phase, and apply it to flexible skylines. We assess the additional effect of an initial filtering phase to reduce dataset size before parallel processing, and the elimination of the sequential part (the most time-

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10

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 (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 necessit

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11

Ke, Senfu, Xiaodong Fu, and Jie Li. "Efficient skyline query processing with user-specified conditional preference." PeerJ Computer Science 11 (January 29, 2025): e2659. https://doi.org/10.7717/peerj-cs.2659.

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In the realm of multi-attribute decision-making, the utilization of skyline queries has gained increasing popularity for assisting users in identifying objects with optimal attribute combinations. With the growing demand for personalization, integrating user’s preferences into skyline queries has emerged as an intriguing and promising research direction. However, the diverse expressions of preferences pose challenges to existing personalized skyline queries. Current methods assume that user preferences are too simplistic and do not represent the interdependencies between attributes. This poses

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12

Choi, Jong-Hyeok, Fei Hao, and Aziz Nasridinov. "HI-Sky: Hash Index-Based Skyline Query Processing." Applied Sciences 10, no. 5 (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 comparis

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13

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

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14

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

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15

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

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16

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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17

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

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18

Ciaccia, Paolo, and Davide Martinenghi. "Directional Queries: Making Top-k Queries More Effective in Discovering Relevant Results." Proceedings of the ACM on Management of Data 2, no. 6 (2024): 1–26. https://doi.org/10.1145/3698807.

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Abstract (sommario):

Top- k queries, in particular those based on a linear scoring function, are a common way to extract relevant results from large datasets. Their major advantage over alternative approaches, such as skyline queries (which return all the undominated objects in a dataset), is that the cardinality of the output can be easily controlled through the k parameter and user preferences can be accommodated by appropriately weighing the involved attributes. In this paper we concentrate on two so-far neglected aspects of top- k queries: first, their general ability to return all the potentially interesting

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19

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 (2013): 2002–13. http://dx.doi.org/10.14778/2556549.2556580.

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20

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

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21

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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Abstract (sommario):

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

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22

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 (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-represent

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23

Haddache, Mohamed, Allel Hadjali, and Hamid Azzoune. "Skyline refinement exploiting fuzzy formal concept analysis." International Journal of Intelligent Computing and Cybernetics 14, no. 3 (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

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24

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 (2018): 798. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp798-806.

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Abstract (sommario):

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 prov

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25

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

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Abstract (sommario):

Nowadays, in most of the modern database applications, lots of critical queries and tasks cannot be completely addressed by machine. Crowdsourcing 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 provi

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26

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

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27

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

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28

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 (2006): 1645–58. http://dx.doi.org/10.1109/tkde.2006.185.

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29

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

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30

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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31

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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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

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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34

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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35

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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36

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

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37

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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38

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

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39

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

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40

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

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41

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

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42

Kang, Hyunchul. "In-Network Processing of Skyline Join Queries in Wireless Sensor Networks Using Synopses of Skyline Attribute Value Ranges." Sensors 23, no. 6 (2023): 3022. http://dx.doi.org/10.3390/s23063022.

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Abstract (sommario):

We investigate the in-network processing of a skyline join query in wireless sensor networks (WSNs). While much research was conducted on processing skyline queries in WSNs, skyline join queries were dealt with only in traditional centralized or distributed database environments. However, such techniques cannot be applied to WSNs. Carrying out join filtering, as well as skyline filtering using them in WSNs, is infeasible due to limited memory in senor nodes and to excessive energy consumption in wireless communications. In this paper, we propose a protocol to process a skyline join query in WS

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43

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 i

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44

Martinenghi, Davide. "Computing Non-Dominated Flexible Skylines in Vertically Distributed Datasets with No Random Access." Data 10, no. 5 (2025): 76. https://doi.org/10.3390/data10050076.

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In today’s data-driven world, algorithms operating with vertically distributed datasets are crucial due to the increasing prevalence of large-scale, decentralized data storage. These algorithms process data locally, thereby reducing data transfer and exposure to breaches, while at the same time improving scalability thanks to data distribution across multiple sources. Top-k queries are a key tool in vertically distributed scenarios and are widely applied in critical applications involving sensitive data. Classical top-k algorithms typically resort to sorted access to sequentially scan the data

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45

S.Saravanapriya, 1. Dr.V.Thiagarasu 2. "A SURVEY ON SKYLINE COMPUTING FOR UNCERTAIN DATABASE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 9 (2017): 35–39. https://doi.org/10.5281/zenodo.884146.

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Skyline filters out hard and fast of thrilling points from a probably large set of statistics points. A point is thrilling if it is not dominated by using explicit. The skyline queries are vital on the way to assist users to deal with the large amount of available records through figuring out a fixed of interesting records items. Skyline computation is extensively used in multi-standards decision making. This paper conducts a survey on research issues on computing skyline for uncertain databases, with the view of providing interested researchers with an overview of the most recent research dir

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Xiao, Yingyuan, Xu Jiao, Hongya Wang, Ching-Hsien Hsu, Li Liu, and Wenguang Zheng. "Efficient Continuous Skyline Query Processing in Wireless Sensor Networks." Sensors 19, no. 13 (2019): 2902. http://dx.doi.org/10.3390/s19132902.

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Owing to the rapid advent of wireless technology and proliferation of smart sensors, wireless sensor networks (WSNs) have been widely used to monitor and query the physical world in many applications based on the Internet of Things (IoT), such as environmental monitoring and event surveillance. A WSN can be treated as a distributed database to respond to user queries. Skyline query, as one of the popular queries for multi-criteria decision making, has received considerable attention due to its numerous applications. In this paper, we study how to process a continuous skyline query over a senso

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47

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 (2021): 1471–96. http://dx.doi.org/10.1007/s10115-021-01562-8.

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

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49

Lin, Xin, Jianliang Xu, and Haibo Hu. "Range-Based Skyline Queries in Mobile Environments." IEEE Transactions on Knowledge and Data Engineering 25, no. 4 (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 (2017): 1031–44. http://dx.doi.org/10.1109/tkde.2017.2654459.

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