Relevant bibliographies by topics / Skyline queries

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Skyline queries'

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Published: 4 June 2021
Last updated: 25 April 2022

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Journal articles on the topic "Skyline queries"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Skyline queries"

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Fu, Gregory Chung Yin. "Skyline queries in database systems /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20FU.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.
Includes bibliographical references (leaves 51-52). Also available in electronic version. Access restricted to campus users.
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Gudala, Satyaveer Goud. "Skyline queries for multi-criteria decision support systems." Kansas State University, 2011. http://hdl.handle.net/2097/13250.

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Master of Science
Department of Computing and Information Sciences
William H. Hsu
In decision-making applications, the Skyline query is used to find a set of non-dominated data points (called Skyline points) in a multi-dimensional dataset. A data point dominates another data point if it is at least as good as the other data point in all dimensions and better in at least one dimension. The skyline consists of data points not dominated by any other data point. Computing the skyline points of a dataset is essential for applications that involve multi-criteria decision making. Skyline queries filter out the interesting tuples from a potentially large dataset. No matter how we weigh our preferences along the attributes, only those tuples which score best under a monotone scoring function are part of the skyline. In other words, the skyline does not contain tuples which are nobody's favorite. With a growing number of real-world applications involving multi-criteria decision making over multiple dimensions, skyline queries can be used to answer those problems accurately and efficiently. This report mainly focuses on various skyline computing algorithms which can be used for online processing efficiently and are suitable to present multi-criteria decision making scenario. I implemented the Branch-and-Bound skyline Algorithm on two different data sets; one is a synthetic dataset and the other is a real dataset. My aim is to explore various subspaces of a given dataset and compute skylines over them, especially those subspace skylines which contain the least number of the skyline points.
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Lampariello, Laura. "Indicatori originali per caratterizzare la rilevanza dei punti dello Skyline." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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La quantità sempre crescente di informazioni disponibili è uno dei maggiori problemi dei sistemi informativi di oggi. Oltre a restituire velocemente i risultati dell’interrogazione, è fondamentale fornire un accesso personalizzato e su misura all’utente finale. Poiché gli attuali sistemi in uso (Query Top-k) presentano delle indubbie limitazioni, cioè non riescono fedelmente a interpretare i desideri dell’utente, si introducono per la prima volta dei nuovi indicatori. Questi nuovi indicatori, applicati ai punti estratti, caratterizzano tutti quei casi di “buon compromesso” che sono difficili da recuperare con interrogazioni Top-k. Essi vengono costruiti sui risultati di un’altra tipologia di interrogazione (Query Skyline) che non viene attualmente usata nei sistemi commerciali. Gli originali indicatori, introdotti qui per la prima volta, sono tre: indice di concavità, robustezza alla griglia e incremento del volume di dominazione. I risultati di questa trattazione potranno essere oggetto di lavori futuri volti all’ideazione di un nuovo e più valido tipo di query che possa sostituire più efficacemente quelle attuali.
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Srivastava, Shweta. "Look Before You Leap: An Adaptive Processing Strategy For Multi-Criteria Decision Support Queries." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/190.

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In recent years, we have witnessed a massive acquisition of data and increasing need to support multi-criteria decision support (MCDS) queries efficiently. Pareto-optimal also known as skyline queries is a popular class of MCDS queries and has received a lot of attention resulting in a flurry of efficient skyline algorithms. The vast majority of such algorithms focus entirely on the input being a single data set. In this work, we provide an adaptive query evaluation technique --- AdaptiveSky that is able to reason at different levels of abstraction thereby effectively minimizing the two primary costs, namely the cost of generating join results and the cost of dominance comparisons to compute the final skyline of the join results. Our approach hinges on two key principles. First, in the input space -- we determine the abstraction levels dynamically at run time instead of assigning a static one at compile time that may or may not work for different data distributions. This is achieved by adaptively partitioning the input data as intermediate results are being generated thereby eliminating the need to access vast majority of the input tuples. Second, we incrementally build the output space, containing the final skyline, without generating a single join result. Our approach is able to reason about the final result space and selectively drill into regions in the output space that show promise in generating result tuples to avoid generation of results that do not contribute to the query result. In this effort, we propose two alternate strategies for reasoning, namely the Euclidean Distance method and the cost-benefit driven Dominance Potential method for reasoning. Our experimental evaluation demonstrates that AdaptiveSky shows superior performance over state-of-the-art techniques over benchmark data sets.
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Raghavan, Venkatesh. "Supporting Multi-Criteria Decision Support Queries over Disparate Data Sources." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-dissertations/120.

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In the era of "big data revolution," marked by an exponential growth of information, extracting value from data enables analysts and businesses to address challenging problems such as drug discovery, fraud detection, and earthquake predictions. Multi-Criteria Decision Support (MCDS) queries are at the core of big-data analytics resulting in several classes of MCDS queries such as OLAP, Top-K, Pareto-optimal, and nearest neighbor queries. The intuitive nature of specifying multi-dimensional preferences has made Pareto-optimal queries, also known as skyline queries, popular. Existing skyline algorithms however do not address several crucial issues such as performing skyline evaluation over disparate sources, progressively generating skyline results, or robustly handling workload with multiple skyline over join queries. In this dissertation we thoroughly investigate topics in the area of skyline-aware query evaluation. In this dissertation, we first propose a novel execution framework called SKIN that treats skyline over joins as first class citizens during query processing. This is in contrast to existing techniques that treat skylines as an "add-on," loosely integrated with query processing by being placed on top of the query plan. SKIN is effective in exploiting the skyline characteristics of the tuples within individual data sources as well as across disparate sources. This enables SKIN to significantly reduce two primary costs, namely the cost of generating the join results and the cost of skyline comparisons to compute the final results. Second, we address the crucial business need to report results early; as soon as they are being generated so that users can formulate competitive decisions in near real-time. On top of SKIN, we built a progressive query evaluation framework ProgXe to transform the execution of queries involving skyline over joins to become non-blocking, i.e., to be progressively generating results early and often. By exploiting SKIN's principle of processing query at multiple levels of abstraction, ProgXe is able to: (1) extract the output dependencies in the output spaces by analyzing both the input and output space, and (2) exploit this knowledge of abstract-level relationships to guarantee correctness of early output. Third, real-world applications handle query workloads with diverse Quality of Service (QoS) requirements also referred to as contracts. Time sensitive queries, such as fraud detection, require results to progressively output with minimal delay, while ad-hoc and reporting queries can tolerate delay. In this dissertation, by building on the principles of ProgXe we propose the Contract-Aware Query Execution (CAQE) framework to support the open problem of contract driven multi-query processing. CAQE employs an adaptive execution strategy to continuously monitor the run-time satisfaction of queries and aggressively take corrective steps whenever the contracts are not being met. Lastly, to elucidate the portability of the core principle of this dissertation, the reasoning and query processing at different levels of data abstraction, we apply them to solve an orthogonal research question to auto-generate recommendation queries that facilitate users in exploring a complex database system. User queries are often too strict or too broad requiring a frustrating trial-and-error refinement process to meet the desired result cardinality while preserving original query semantics. Based on the principles of SKIN, we propose CAPRI to automatically generate refined queries that: (1) attain the desired cardinality and (2) minimize changes to the original query intentions. In our comprehensive experimental study of each part of this dissertation, we demonstrate the superiority of the proposed strategies over state-of-the-art techniques in both efficiency, as well as resource consumption.
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Alami, Karim. "Optimisation des requêtes de préférence skyline dans des contextes dynamiques." Thesis, Bordeaux, 2020. http://www.theses.fr/2020BORD0135.

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Les requêtes de préférence sont des outils intéressants pour calculer des sous-ensembles représentatifs d'ensembles de données en entrée ou pour classer les tuples en fonction des préférences des utilisateurs. Dans cette thèse, nous abordons principalement l'optimisation des requêtes Skyline, une classe spéciale des requêtes de préférence, dans des contextes dynamiques. Dans une première partie, nous abordons la maintenance incrémentale de la structure d'indexation multidimensionnelle NSC qui a été démontrée efficace pour répondre aux requêtes skyline dans un contexte statique. Plus précisément, nous abordons (i) le cas des données dynamiques, c'est-à-dire que des tuples sont insérés ou supprimés à tout moment, et (ii) le cas des données en streaming, c'est-à-dire que les tuples sont insérés et écartés après un intervalle de temps spécifique. En cas de données dynamiques, nous repensons la structure et proposons des procédures pour gérer efficacement les insertions et les suppressions. En cas de données en streaming, nous proposons MSSD un pipeline de données qui gère les données par lot et maintient NSCt une variation de NSC. Dans une deuxième partie, nous abordons le cas des ordres dynamiques, c'est-à-dire que certains ou tous les attributs de l'ensemble de données sont catégoriques et chaque utilisateur exprime son propre ordre partiel sur le domaine de ces attributs. Nous proposons des algorithmes parallèles évolutifs qui décomposent une requête en un ensemble de sous-requêtes et traitent chaque sous-requête indépendamment. Pour optimiser davantage, nous proposons la matérialisation partielle des sous-requêtes et introduisons le problème de la sélection de sous-requêtes en fonction d'un coût
Preference queries are interesting tools to compute small representatives of datasets or to rank tuples based on the users’ preferences. In this thesis, we mainly focus on the optimization of Skyline queries, a special class of preference queries, in dynamic contexts. In a first part, we address the incremental maintenance of the multidimensional indexing structure NSC which has been shown efficient for answering skyline queries in a static context. More precisely, we address (i) the case of dynamic data, i.e. tuples are inserted or deleted at any time, and (ii) the case of streaming data, i.e. tuples are appended only, and discarded after a specific interval of time. In case of dynamic data, we redesign the structure and propose procedures to handle efficiently both insertions and deletions. In case of streaming data, we propose MSSD a data pipeline which operates in batch mode, and maintains NSCt a variation of NSC. In a second part, we address the case of dynamic orders, i.e, some or all attributes of the dataset are nominal and each user expresses his/her own partial order on these attributes’ domain. We propose highly scalable parallel algorithms that decompose an issued query into a set of sub-queries and process each sub-query independently. In a further step for optimization, we propose the partial materialization of sub-queries and introduce the problem of cost-driven sub-queries selection
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Elmi, Saïda. "An Advanced Skyline Approach for Imperfect Data Exploitation and Analysis." Thesis, Chasseneuil-du-Poitou, Ecole nationale supérieure de mécanique et d'aérotechnique, 2017. http://www.theses.fr/2017ESMA0011/document.

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Ce travail de thèse porte sur un modèle de requête de préférence, appelée l'opérateur Skyline, pour l'exploitation de données imparfaites. L'imperfection de données peut être modélisée au moyen de la théorie de l'évidence. Ce type de données peut être géré dans des bases de données imparfaites appelées bases de données évidentielles. D'autre part, l'opérateur skyline est un outil puissant pour extraire les objets les plus intéressants dans une base de données.Dans le cadre de cette thèse, nous définissons une nouvelle sémantique de l'opérateur Skyline appropriée aux données imparfaites modélisées par la théorie de l'évidence. Nous introduisons par la suite la notion de points marginaux pour optimiser le calcul distribué du Skyline ainsi que la maintenance des objets Skyline en cas d'insertion ou de suppression d'objets dans la base de données.Nous modélisons aussi une fonction de score pour mesurer le degré de dominance de chaque objet skyline et définir le top-k Skyline. Une dernière contribution porte sur le raffinement de la requête Skyline pour obtenir les meilleurs objets skyline appelés objets Etoile ou Skyline stars
The main purpose of this thesis is to study an advanced database tool named the skyline operator in the context of imperfect data modeled by the evidence theory. In this thesis, we first address, on the one hand, the fundamental question of how to extend the dominance relationship to evidential data, and on the other hand, it provides some optimization techniques for improving the efficiency of the evidential skyline. We then introduce efficient approach for querying and processing the evidential skyline over multiple and distributed servers. ln addition, we propose efficient methods to maintain the skyline results in the evidential database context wben a set of objects is inserted or deleted. The idea is to incrementally compute the new skyline, without reconducting an initial operation from the scratch. In the second step, we introduce the top-k skyline query over imperfect data and we develop efficient algorithms its computation. Further more, since the evidential skyline size is often too large to be analyzed, we define the set SKY² to refine the evidential skyline and retrieve the best evidential skyline objects (or the stars). In addition, we develop suitable algorithms based on scalable techniques to efficiently compute the evidential SKY². Extensive experiments were conducted to show the efficiency and the effectiveness of our approaches
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Abidi, Amna. "Imperfect RDF Databases : From Modelling to Querying." Thesis, Chasseneuil-du-Poitou, Ecole nationale supérieure de mécanique et d'aérotechnique, 2019. http://www.theses.fr/2019ESMA0008/document.

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L’intérêt sans cesse croissant des données RDF disponibles sur le Web a conduit à l’émergence de multiple et importants efforts de recherche pour enrichir le formalisme traditionnel des données RDF à des fins d’exploitation et d’analyse. Le travail de cette thèse s’inscrit dans la continuation de ces efforts en abordant la problématique de la gestion des données RDF en présence d’imperfections (manque de confiance/validité, incertitude, etc.). Les contributions de la thèse sont comme suit: (1) Nous avons proposé d’appliquer l’opérateur skyline sur les données RDF pondérées par des mesures de confiance (Trust-RDF) dans le but d’extraire les ressources les plus confiantes selon des critères définis par l’utilisateur. (2) Nous avons discuté via des méthodes statistiques l’impact des mesures de confiance sur le Trust-skyline.(3) Nous avons intégré à la structure des données RDF un quatrième élément, exprimant une mesure de possibilité. Pour gérer cette mesure de possibilité, un cadre langagier appropriée est étudié, à savoir Pi-SPARQL, qui étend le langage SPARQL aux requêtes permettant de traiter des distributions de possibilités. (4) Nous avons étudié une variante d’opérateur skyline pour extraire les ressources RDF possibilistes qui ne sont éventuellement dominées par aucune autre ressource dans le sens de l’optimalité de Pareto
The ever-increasing interest of RDF data on the Web has led to several and important research efforts to enrich traditional RDF data formalism for the exploitation and analysis purpose. The work of this thesis is a part of the continuation of those efforts by addressing the issue of RDF data management in presence of imperfection (untruthfulness, uncertainty, etc.). The main contributions of this dissertation are as follows. (1) We tackled the trusted RDF data model. Hence, we proposed to extend the skyline queries over trust RDF data, which consists in extracting the most interesting trusted resources according to user-defined criteria. (2) We studied via statistical methods the impact of the trust measure on the Trust-skyline set.(3) We integrated in the structure of RDF data (i.e., subject-property-object triple) a fourth element expressing a possibility measure to reflect the user opinion about the truth of a statement.To deal with possibility requirements, appropriate framework related to language is introduced, namely Pi-SPARQL, that extends SPARQL to be possibility-aware query language.Finally, we studied a new skyline operator variant to extract possibilistic RDF resources that are possibly dominated by no other resources in the sense of Pareto optimality
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Yuan, Yidong Computer Science &amp Engineering Faculty of Engineering UNSW. "Efficient computation of advanced skyline queries." 2007. http://handle.unsw.edu.au/1959.4/40511.

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Skyline has been proposed as an important operator for many applications, such as multi-criteria decision making, data mining and visualization, and user-preference queries. Due to its importance, skyline and its computation have received considerable attention from database research community recently. All the existing techniques, however, focus on the conventional databases. They are not applicable to online computation environment, such as data stream. In addition, the existing studies consider efficiency of skyline computation only, while the fundamental problem on the semantics of skylines still remains open. In this thesis, we study three problems of skyline computation: (1) online computing skyline over data stream; (2) skyline cube computation and its analysis; and (3) top-k most representative skyline. To tackle the problem of online skyline computation, we develop a novel framework which converts more expensive multiple dimensional skyline computation to stabbing queries in 1-dimensional space. Based on this framework, a rigorous theoretical analysis of the time complexity of online skyline computation is provided. Then, efficient algorithms are proposed to support ad hoc and continuous skyline queries over data stream. Inspired by the idea of data cube, we propose a novel concept of skyline cube which consists of skylines of all possible non-empty subsets of a given full space. We identify the unique sharing strategies for skyline cube computation and develop two efficient algorithms which compute skyline cube in a bottom-up and top-down manner, respectively. Finally, a theoretical framework to answer the question about semantics of skyline and analysis of multidimensional subspace skyline are presented. Motived by the fact that the full skyline may be less informative because it generally consists of a large number of skyline points, we proposed a novel skyline operator -- top-k most representative skyline. The top-k most representative skyline operator selects the k skyline points so that the number of data points, which are dominated by at least one of these k skyline points, is maximized. To compute top-k most representative skyline, two efficient algorithms and their theoretical analysis are presented.
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Chia-HengChang and 張嘉恒. "Continuous Skyline Queries in Road Networks." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/43915730531332275302.

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碩士
國立成功大學
資訊工程學系碩博士班
98
The skyline query is an efficient tool for preference-based data analysis and attracts more attention than ever in the database community. Given a set of d-dimensional objects D, a skyline query retrieves all objects from D, which cannot be dominated by any others in D. In this paper, we investigate how to process the skyline query in road network, where the road distance between objects needs to be considered in query processing. Different from the previous related works, our work focuses on processing the continuous distance-based skyline query. We present two novel and important query types, named the Continuous d"-Skyline Query (Cd"-SQ for short) and the Continuous k nearest neighbor-Skyline Query (Cknn-SQ for short). To efficiently process the Cd"-SQ and Cknn-SQ in road network, we first design a grid index to manage the information of road network and objects, and then develop several algorithms combined with the grid index to determine the query result. Finally, we conduct a comprehensive set of experiments to demonstrate the effectiveness and the effciency of the proposed approaches.
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Book chapters on the topic "Skyline queries"

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Thakur, Nilu. "Skyline Queries." In Encyclopedia of GIS, 1056–62. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1221.

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Thakur, Nilu. "Skyline Queries." In Encyclopedia of GIS, 1–9. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_1221-2.

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Thakur, Nilu. "Skyline Queries." In Encyclopedia of GIS, 1897–905. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_1221.

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Vlachou, Akrivi, Christos Doulkeridis, Kjetil Nørvåg, and Yannis Kotidis. "Subspace Skyline Queries." In Peer-to-Peer Query Processing over Multidimensional Data, 43–61. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2110-8_6.

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Guo, Xi, Chuan Xiao, and Yoshiharu Ishikawa. "Combination Skyline Queries." In Transactions on Large-Scale Data- and Knowledge-Centered Systems VI, 1–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34179-3_1.

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El-Dawy, Eman, Hoda M. O. Mokhtar, and Ali El-Bastawissy. "Directional Skyline Queries." In Data and Knowledge Engineering, 15–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34679-8_2.

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Bosc, Patrick, Allel Hadjali, and Olivier Pivert. "On Possibilistic Skyline Queries." In Flexible Query Answering Systems, 412–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24764-4_36.

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Jaudoin, Hélène, Olivier Pivert, and Daniel Rocacher. "Exception-Tolerant Skyline Queries." In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 120–29. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08852-5_13.

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Emrich, Tobias, Maximilian Franzke, Nikos Mamoulis, Matthias Renz, and Andreas Züfle. "Geo-Social Skyline Queries." In Database Systems for Advanced Applications, 77–91. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05813-9_6.

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Su, I.-Fang, Yu-Chi Chung, and Chiang Lee. "Top-k Combinatorial Skyline Queries." In Database Systems for Advanced Applications, 79–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12098-5_6.

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Conference papers on the topic "Skyline queries"

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Afrati, Foto N., Paraschos Koutris, Dan Suciu, and Jeffrey D. Ullman. "Parallel skyline queries." In the 15th International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2274576.2274605.

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Böhm, Christian, Frank Fiedler, Annahita Oswald, Claudia Plant, and Bianca Wackersreuther. "Probabilistic skyline queries." In Proceeding of the 18th ACM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1645953.1646037.

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Tiakas, Eleftherios, Apostolos N. Papadopoulos, and Yannis Manolopoulos. "Skyline queries: An introduction." In 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2015. http://dx.doi.org/10.1109/iisa.2015.7388053.

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"Materializing Distributed Skyline Queries." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004794203720379.

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Dzolkhifli, Zarina, Hamidah Ibrahim, Fatimah Sidi, Lilly Suriani Affendey, Siti Nurulain Mohd Rum, and Ali A. Alwan. "Efficient Skyline Computation of Multiple Range Skyline Queries." In iiWAS2021: The 23rd International Conference on Information Integration and Web Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3487664.3487718.

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Liu, Jinfei, Juncheng Yang, Li Xiong, Jian Pei, and Jun Luo. "Skyline Diagram: Finding the Voronoi Counterpart for Skyline Queries." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00065.

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Cosgaya-Lozano, Adan, Andrew Rau-Chaplin, and Norbert Zeh. "Parallel Computation of Skyline Queries." In 21st International Symposium on High Performance Computing Systems and Applications (HPCS'07). IEEE, 2007. http://dx.doi.org/10.1109/hpcs.2007.25.

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Woods, Louis, Gustavo Alonso, and Jens Teubner. "Parallel Computation of Skyline Queries." In 2013 IEEE 21st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 2013. http://dx.doi.org/10.1109/fccm.2013.18.

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Zaman, Asif, Md Mahbubul Islam, Md Anisuzzaman Siddique, and Yasuhiko Morimoto. "Distributed k-dominant skyline queries." In 2012 15th International Conference on Computer and Information Technology (ICCIT). IEEE, 2012. http://dx.doi.org/10.1109/iccitechn.2012.6509757.

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Rahul, Saladi, and Ravi Janardan. "Algorithms for range-skyline queries." In the 20th International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2424321.2424406.

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