Relevant bibliographies by topics / Query optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Query optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Query optimization"

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Joshi, Mukul, and Praveen Ranjan Srivastava. "Query Optimization." International Journal of Intelligent Information Technologies 9, no. 1 (2013): 40–55. http://dx.doi.org/10.4018/jiit.2013010103.

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Query optimization is an important aspect in designing database management systems, aimed to find an optimal query execution plan so that overall time of query execution is minimized. Multi join query ordering (MJQO) is an integral part of query optimizer. This paper aims to propose a solution for MJQO problem, which is an NP complete problem. This paper proposes a heuristic based algorithm as a solution of MJQO problem. The proposed algorithm is a combination of two basic search algorithms, cuckoo and tabu search. Simulation shows some exciting results in favour of the proposed algorithm and
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Ioannidis, Yannis E. "Query optimization." ACM Computing Surveys 28, no. 1 (1996): 121–23. http://dx.doi.org/10.1145/234313.234367.

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Wang, Chenxiao, Zach Arani, Le Gruenwald, Laurent d'Orazio, and Eleazar Leal. "Re-optimization for Multi-objective Cloud Database Query Processing using Machine Learning." International Journal of Database Management Systems 13, no. 1 (2021): 21–40. http://dx.doi.org/10.5121/ijdms.2021.13102.

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In cloud environments, hardware configurations, data usage, and workload allocations are continuously changing. These changes make it difficult for the query optimizer of a cloud database management system (DBMS) to select an optimal query execution plan (QEP). In order to optimize a query with a more accurate cost estimation, performing query re-optimizations during the query execution has been proposed in the literature. However, some of there-optimizations may not provide any performance gain in terms of query response time or monetary costs, which are the two optimization objectives for cl
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Нестеров, М. В., Д. Е. Бакитько, and A. O. Михайлова. "Database query optimization." ВІСНИК СХІДНОУКРАЇНСЬКОГО НАЦІОНАЛЬНОГО УНІВЕРСИТЕТУ імені Володимира Даля, no. 5(253) (September 5, 2019): 78–83. http://dx.doi.org/10.33216/1998-7927-2019-253-5-78-83.

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Sellis, Timos K. "Global query optimization." ACM SIGMOD Record 15, no. 2 (1986): 191–205. http://dx.doi.org/10.1145/16856.16874.

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Sellis, Timos K. "Multiple-query optimization." ACM Transactions on Database Systems 13, no. 1 (1988): 23–52. http://dx.doi.org/10.1145/42201.42203.

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Han, Wook-Shin, Wooseong Kwak, Jinsoo Lee, Guy M. Lohman, and Volker Markl. "Parallelizing query optimization." Proceedings of the VLDB Endowment 1, no. 1 (2008): 188–200. http://dx.doi.org/10.14778/1453856.1453882.

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Goel, Piyush, and Bala Iyer. "SQL query optimization." ACM SIGMOD Record 25, no. 2 (1996): 47–56. http://dx.doi.org/10.1145/235968.233318.

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Ioannidis, Yannis E., Raymond T. Ng, Kyuseok Shim, and Timos K. Sellis. "Parametric query optimization." VLDB Journal The International Journal on Very Large Data Bases 6, no. 2 (1997): 132–51. http://dx.doi.org/10.1007/s007780050037.

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Sukheja, Deepak, and Umesh Kumar Singh. "Novel Distributed Query Optimization Model and Hybrid Query Optimization Algorithm." International Journal of Computer Applications 75, no. 17 (2013): 22–32. http://dx.doi.org/10.5120/13203-0461.

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Dissertations / Theses on the topic "Query optimization"

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Yu, Feng. "Constructing Accurate Synopses for Database Query Optimization and Re-optimization." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/dissertations/709.

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Fast and accurate estimations for complex queries are profoundly beneficial for large databases with heavy workloads. The most widely adopted query optimizers use synopses to tune up the databases in manners of optimization and re-optimization. From Chapter 1 to Chapter 3, we focus on the synopses for query optimization. We propose a statistical summary for a database, called CS2 (Correlated Sample Synopsis), to provide rapid and accurate result size estimations for all queries with joins and arbitrary selections. Unlike the state-of-the-art techniques, CS2 does not completely rely on simple
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Brasetvik, Alex, and Hans Olav Norheim. "iAD: Query Optimization in MARS." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9099.

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<p>This document is the report for the authors' joint effort in researching and designing a query optimizer for Fast's next-generation search platform, known as MARS. The work was done during our master's thesis at the Department of Computer and Information Science at the Norwegian University of Science and Technology, spring 2009. MARS does not currently employ any form of query optimizer, but does have a parser and a runtime system. The report therefore focuses on the core query optimizing aspects, like plan generation and optimizer design. First, we give an introduction to query optimizers
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Genet, Bryan Howard. "Is Semantic Query Optimization Worthwhile?" The University of Waikato, 2007. http://hdl.handle.net/10289/2531.

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The term quote semantic query optimization quote (SQO) denotes a methodology whereby queries against databases are optimized using semantic information about the database objects being queried. The result of semantically optimizing a query is another query which is syntactically different to the original, but semantically equivalent and which may be answered more efficiently than the original. SQO is distinctly different from the work performed by the conventional SQL optimizer. The SQL optimizer generates a set of logically equivalent alternative execution paths based ultimately on the rules
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Waas, Florian Michael. "Principles of probabilistic query optimization." [S.l. : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2000. http://dare.uva.nl/document/83735.

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Waite, Edwin Richard. "Web Based Query Optimization Simulator." CSUSB ScholarWorks, 2004. https://scholarworks.lib.csusb.edu/etd-project/2519.

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The Web Based Query Optimization Simulator (WBQOS) is a software tool designed to enhance understanding of query optimization with a Relational Database Management System (RDBMS). WBQOS allows the user to visualize and participate in query optimization, which enhances the learning process.
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Shastri, Avani. "MTopS: Multi-Query Optimization for Continuous Top-K Query Workloads." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/759.

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A continuous top-k query retrieves the k most preferred objects from a data stream according to a given preference function. These queries are important for a broad spectrum of applications from web-based advertising, network traffic monitoring, to financial analysis. Given the nature of such applications, a data stream may be subjected at any given time to multiple top-k queries with varying parameter settings requested simultaneously by different users. This workload of simultaneous top-k queries must be executed efficiently to assure real time responsiveness. However, existing methods in t
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Elsidani, Elariss Haifa. "A new visual query language and query optimization for mobile GPS." Thesis, Kingston University, 2008. http://eprints.kingston.ac.uk/20306/.

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In recent years computer applications have been deployed to manage spatial data with Geographic Information Systems (GIS) to store and analyze data related to domains such as transportation and tourism. Recent developments have shown that there is an urgent need to develop systmes for mobile devices and particularly for Location Based Services (LBS) such as proximity analysis that helps in finding the nearest neighbors, for example. restaurant, and the facilities that are located within a circle area around the user's location, known as a buffer area, for example, all restaurants within 100 me
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Wang, Di. "Query optimization for database federation systems." Worcester, Mass. : Worcester Polytechnic Institute, 2009. http://www.wpi.edu/Pubs/ETD/Available/etd-050409-131957/.

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Blunschi, Lukas. "Cost-based query optimization in iMeMex." Zurich : ETH, Swiss Federal Institute of Technology, Department of Computer Science, Databases and Information Systems Group, 2007. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=425.

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Eom, Boyun. "Query optimization using frequent itemset mining." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0010844.

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Books on the topic "Query optimization"

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Dombrovskaya, Henrietta, Boris Novikov, and Anna Bailliekova. PostgreSQL Query Optimization. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6885-8.

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Dombrovskaya, Henrietta, Boris Novikov, and Anna Bailliekova. PostgreSQL Query Optimization. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0069-6.

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Gao, Yunjun, and Qing Liu. Preference Query Analysis and Optimization. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6635-1.

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Kuck, Sharon McCure. Network query optimization made easy. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1985.

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Agbenorhevi, William. Relational queries and query optimization. University ofEast London, 1997.

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Pollack, Edward. Introduction to SQL Server Query Optimization. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5144-7.

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Illarramendi, A. Query optimization for KBMSs: Temporal syntactic and semantic transformations (revised). Computer Science Department, University of Toronto, 1990.

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Aberer, Karl. Semantic query optimization for methods in object-oriented database systems. Gesellschaft für Mathematik und Datenverarbeitung, 1994.

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Jurišica, Igor. Query optimization for knowledge base management systems: A machine learning approach. National Library of Canada = Bibliothèque nationale du Canada, 1993.

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Hendrikus Johannes Antonius van Kuijk. Semantic query optimization in distributed database systems: A knowledge-based approach. [s.n.], 1990.

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Book chapters on the topic "Query optimization"

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Bell, Charles. "Query Optimization." In Expert MySQL. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4660-2_13.

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Pitoura, Evaggelia. "Query Optimization." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_861-2.

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Pitoura, Evaggelia. "Query Optimization." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_861.

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Sciore, Edward. "Query Optimization." In Database Design and Implementation. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33836-7_15.

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Greco, Sergio, and Cristian Molinaro. "Query Optimization." In Datalog and Logic Databases. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-01854-1_7.

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Pitoura, Evaggelia. "Query Optimization." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_861.

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Dombrovskaya, Henrietta, Boris Novikov, and Anna Bailliekova. "Ultimate Optimization Algorithm." In PostgreSQL Query Optimization. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0069-6_16.

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Dombrovskaya, Henrietta, Boris Novikov, and Anna Bailliekova. "Ultimate Optimization Algorithm." In PostgreSQL Query Optimization. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6885-8_15.

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Dombrovskaya, Henrietta, Boris Novikov, and Anna Bailliekova. "Avoiding the Pitfalls of Object-Relational Mapping." In PostgreSQL Query Optimization. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0069-6_14.

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Dombrovskaya, Henrietta, Boris Novikov, and Anna Bailliekova. "More Complex Filtering and Search." In PostgreSQL Query Optimization. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0069-6_15.

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Conference papers on the topic "Query optimization"

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Wang, Zhen, Jiqing Han, Liwen Zhang, and Youcheng Zhang. "InfoMin-based Query Embedding Optimization For Query-based Universal Sound Separation." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887870.

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Dimri, Namya, Arunesh Srivastava, Malepati Ananya, Sinchana Math, K. Venugopal, and S. N. Kavitha. "Parameterized Complexity for Query Subset Optimization." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10816997.

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Sellis, Timos K. "Global query optimization." In the 1986 ACM SIGMOD international conference. ACM Press, 1986. http://dx.doi.org/10.1145/16894.16874.

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Vu, Tin. "Deep Query Optimization." In SIGMOD/PODS '19: International Conference on Management of Data. ACM, 2019. http://dx.doi.org/10.1145/3299869.3300104.

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Sen, Rajkumar, Jack Chen, and Nika Jimsheleishvilli. "Query Optimization Time." In the 3rd VLDB Workshop. ACM Press, 2015. http://dx.doi.org/10.1145/2803140.2803148.

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Goel, Piyush, and Bala Iyer. "SQL query optimization." In the 1996 ACM SIGMOD international conference. ACM Press, 1996. http://dx.doi.org/10.1145/233269.233318.

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Papadimitriou, Christos H., and Mihalis Yannakakis. "Multiobjective query optimization." In the twentieth ACM SIGMOD-SIGACT-SIGART symposium. ACM Press, 2001. http://dx.doi.org/10.1145/375551.375560.

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El Husseini, Wafaa, Cheikh Brahim El Vaigh, François Goasdoué, and Hélène Jaudoin. "Query Optimization for Ontology-Mediated Query Answering." In WWW '24: The ACM Web Conference 2024. ACM, 2024. http://dx.doi.org/10.1145/3589334.3645567.

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Assefa, Andinet, and Fekade Getahun. "Multi-query optimization for semantic news feed query." In the International Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2457276.2457303.

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Zhuang, Yi, Qing Li, and Lei Chen. "Multi-query Optimization for Distributed Similarity Query Processing." In 2008 28th IEEE International Conference on Distributed Computing Systems (ICDCS). IEEE, 2008. http://dx.doi.org/10.1109/icdcs.2008.58.

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Reports on the topic "Query optimization"

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Chen, J. S. J. Distributed-query optimization in fragmented data-base systems. Office of Scientific and Technical Information (OSTI), 1987. http://dx.doi.org/10.2172/7183881.

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Sheu, Phillip. Query Optimization and Planning in Object-Oriented Knowledge Bases. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada256006.

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Borgwardt, Stefan, and Veronika Thost. Temporal Query Answering in EL. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.214.

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Context-aware systems use data about their environment for adaptation at runtime, e.g., for optimization of power consumption or user experience. Ontology-based data access (OBDA) can be used to support the interpretation of the usually large amounts of data. OBDA augments query answering in databases by dropping the closed-world assumption (i.e., the data is not assumed to be complete any more) and by including domain knowledge provided by an ontology. We focus on a recently proposed temporalized query language that allows to combine conjunctive queries with the operators of the well-known pr
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Hong, Seokyong, Sangkeun Lee, Seung Hwan Lim, Sreenivas R. Sukumar, and Ranga Raju Vatsavai. Query optimization for graph analytics on linked data using SPARQL. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1215587.

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Lupher, Antonio. Shark: SQL and Analytics with Cost-Based Query Optimization on Coarse-Grained Distributed Memory. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada603561.

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