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Journal articles on the topic 'Query optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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Safitri, Annisa Heparyanti, Agung Teguh Wibowo Almais, A'la Syauqi, and Roro Inda Melani. "Pengujian Optimization dan Non-Optimization Query Metode Topsis untuk Menentukan Tingkat Kerusakan Sektor Bencana Alam." Jurnal ELTIKOM 6, no. 1 (2022): 89–99. http://dx.doi.org/10.31961/eltikom.v6i1.532.

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Volume data yang sangat besar dari tim surveyor Perencanaan dan Pengendalian Penanganan Bencana(P3B) menciptakan masalah yang luas dan beragam sehingga dapat menghabiskan sumber daya sistem dan waktu pemrosesan yang terbilang lama. Oleh karena itu penelitian ini mengusulkan solusi dengan melakukan Optimasi query pada metode TOPSIS yang diimplementasikan pada sistem pendukung kepeutusan untuk menentukan tingkat kerusakan pasca bencana. Berdasarkan 3 kali uji coba dengan jumlah data yang berbeda-beda yaitu ujicoba ke-1 menggunakan 114 data, ujicoba ke-2 sebanyak 228 data dan ujicoba ke-3 menggun

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Madavi, Chitransh, Chetan Patel, Vedant Jain, and Prof Vandana Kate. "MySQL Select Query Optimization Using Self-Join." International Journal of Research Publication and Reviews 4, no. 4 (2023): 2564–71. http://dx.doi.org/10.55248/gengpi.234.4.36035.

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Sayantan Saha. "Next-generation query optimization: AI-powered query engines." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 472–85. https://doi.org/10.30574/wjaets.2025.15.1.0235.

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AI-powered query optimization represents an emerging paradigm that addresses fundamental limitations in traditional database management systems. By leveraging machine learning techniques, these next-generation query engines can dynamically adapt to evolving data patterns, workload characteristics, and user behaviors. Unlike conventional optimizers that rely on static models and simplified assumptions, AI-driven approaches continuously learn from query execution feedback to improve performance. From workload-aware optimization and adaptive execution to intelligent data management and natural la

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Tkachenko, Olha, and Oleksandr Golubenko. "Optimization of multi-request single-processor COMPUTING." Advanced Information Technology, no. 1 (2) (2023): 32–37. http://dx.doi.org/10.17721/ait.2023.1.05.

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Background. The effectiveness of multi-query execution in single-processor computer system databases is considered. One of the methods of increasing the performance of databases of computer systems is the simultaneous execution of several queries that form a multi-query. Methods. Methods of analysis and criterial optimization are used in the paper. Results. The paper analyzes the processing of a conjunctive multi-query (queries are formed by the conjunction of elementary queries, from which a number of elementary queries are repeatedly included in the queries). With the growing importance of o

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O'Gorman, K., A. El Abbadi, and D. Agrawal. "Multiple query optimization in middleware using query teamwork." Software: Practice and Experience 35, no. 4 (2005): 361–91. http://dx.doi.org/10.1002/spe.640.

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Zhang, Wangda, Junyoung Kim, Kenneth A. Ross, Eric Sedlar, and Lukas Stadler. "Adaptive code generation for data-intensive analytics." Proceedings of the VLDB Endowment 14, no. 6 (2021): 929–42. http://dx.doi.org/10.14778/3447689.3447697.

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Modern database management systems employ sophisticated query optimization techniques that enable the generation of efficient plans for queries over very large data sets. A variety of other applications also process large data sets, but cannot leverage database-style query optimization for their code. We therefore identify an opportunity to enhance an open-source programming language compiler with database-style query optimization. Our system dynamically generates execution plans at query time, and runs those plans on chunks of data at a time. Based on feedback from earlier chunks, alternative

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Munot, Priyanka R., Dipali R. Patil, and Kajal P. Pathak. "SQL Query Optimization Techniques." IJARCCE 8, no. 5 (2019): 87–90. http://dx.doi.org/10.17148/ijarcce.2019.8518.

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Bizarro, P., N. Bruno, and D. J. DeWitt. "Progressive Parametric Query Optimization." IEEE Transactions on Knowledge and Data Engineering 21, no. 4 (2009): 582–94. http://dx.doi.org/10.1109/tkde.2008.160.

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Chaudhuri, Surajit. "Technical perspectiveRelational query optimization." Communications of the ACM 52, no. 10 (2009): 86. http://dx.doi.org/10.1145/1562764.1562786.

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Kaushik, Raghav, Jeffrey F. Naughton, Raghu Ramakrishnan, and Venkatesan T. Chakravarthy. "Synopses for query optimization." ACM Transactions on Database Systems 30, no. 4 (2005): 1102–27. http://dx.doi.org/10.1145/1114244.1114251.

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Maneth, Sebastian, and Kim Nguyen. "XPath whole query optimization." Proceedings of the VLDB Endowment 3, no. 1-2 (2010): 882–93. http://dx.doi.org/10.14778/1920841.1920954.

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Nuriev, Marat, Rimma Zaripova, Olga Yanova, Irina Koshkina, and Andrey Chupaev. "Enhancing MongoDB query performance through index optimization." E3S Web of Conferences 531 (2024): 03022. http://dx.doi.org/10.1051/e3sconf/202453103022.

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This article delves into the critical aspect of enhancing query performance in MongoDB through meticulous index optimization. It begins with an introduction to MongoDB's unique document-oriented data storage approach and its inherent scalability, which sets the stage for understanding the importance of efficient query processing. The discussion progresses to highlight the pivotal role of indexes in MongoDB, emphasizing their function in expediting data retrieval and the necessity for their optimization to ensure peak database performance. A detailed exploration is provided on the methodologies

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Fent, Philipp, Guido Moerkotte, and Thomas Neumann. "Asymptotically Better Query Optimization Using Indexed Algebra." Proceedings of the VLDB Endowment 16, no. 11 (2023): 3018–30. http://dx.doi.org/10.14778/3611479.3611505.

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Query optimization is essential for the efficient execution of queries. The necessary analysis, if we can and should apply optimizations and transform the query plan, is already challenging. Traditional techniques focus on the availability of columns at individual operators, which does not scale for analysis of data flow through the query. Tracking available columns per operator takes quadratic space, which can result in multi-second optimization time for deep algebra trees. Instead, we need to re-think the naïve algebra representation to efficiently support data flow analysis. In this paper,

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Liu, Fu Min, and Jing Yong Wang. "Research of Query Optimization Based on Improved Quantum Particle Swarm Optimization Algorithm in Distributed Database." Advanced Materials Research 532-533 (June 2012): 1365–69. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1365.

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Database query optimization is a very complicated issue, also is the key influencing factor in database systems performance. Database query operation efficiency is one of the key factors that affect system response time. Therefore, how to improve the efficiency of database query system becomes particularly important. This paper, on the basis of the advantages of Quantum particle swarm optimization algorithm, proposes distributed database query optimization methods based on Quantum particle swarm optimization algorithm, and improves algorithm. Simulation comparison experiments show that Quantum

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Paudel, Nawaraj, and Jagdish Bhatta. "Cost-Based Query Optimization in Centralized Relational Databases." Journal of Institute of Science and Technology 24, no. 1 (2019): 42–46. http://dx.doi.org/10.3126/jist.v24i1.24627.

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Query optimization is the most significant factor for any centralized relational database management system (RDBMS) that reduces the total execution time of a query. Query optimization is the process of executing a SQL (Structured Query Language) query in relational databases to determine the most efficient way to execute a given query by considering the possible query plans. The goal of query optimization is to optimize the given query for the sake of efficiency. Cost-based query optimization compares different strategies based on relative costs (amount of time that the query needs to run) an

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Zhang, Xin, and Ahmed Eldawy. "Spatial Query Optimization With Learning." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4245–48. http://dx.doi.org/10.14778/3685800.3685846.

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Query optimization is a key component in database management systems (DBMS) and distributed data processing platforms. Recent research in the database community incorporated techniques from artificial intelligence to enhance query optimization. Various learning models have been extended and applied to the query optimization tasks, including query execution plan, query rewriting, and cost estimation. The tasks involved in query optimization differ based on the type of data being processed, such as relational data or spatial geometries. This tutorial reviews recent learning-based approaches for

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Li, Feng, and Hai Ying Wang. "Study on Distributed Database Query Optimization." Applied Mechanics and Materials 536-537 (April 2014): 540–44. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.540.

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This paper presents a heterogeneous sensor networks to improve query processing mechanism. Analysis of the advantages and disadvantages of centralized query processing algorithm is proposed based on the spatial distance distributed query processing algorithm based on semantic similarity and distributed query processing algorithms for query execution processes described. Use simulation to choose better coverage and semantic similarity by half, and centralized query processing algorithm, based on the spatial distance distributed algorithms, distributed algorithms and performance-based semantic d

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Abdullah, Fatima, Limei Peng, and Byungchul Tak. "A Survey of IoT Stream Query Execution Latency Optimization within Edge and Cloud." Wireless Communications and Mobile Computing 2021 (November 16, 2021): 1–16. http://dx.doi.org/10.1155/2021/4811018.

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IoT (Internet of Things) streaming data has increased dramatically over the recent years and continues to grow rapidly due to the exponential growth of connected IoT devices. For many IoT applications, fast stream query processing is crucial for correct operations. To achieve better query performance and quality, researchers and practitioners have developed various types of query execution models—purely cloud-based, geo-distributed, edge-based, and edge-cloud-based models. Each execution model presents unique challenges and limitations of query processing optimizations. In this work, we provid

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Tao, Jeffrey, Natalie Maus, Haydn Jones, Yimeng Zeng, Jacob R. Gardner, and Ryan Marcus. "Learned Offline Query Planning via Bayesian Optimization." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–29. https://doi.org/10.1145/3725316.

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Analytics database workloads often contain queries that are executed repeatedly. Existing optimization techniques generally prioritize keeping optimization cost low, normally well below the time it takes to execute a single instance of a query. If a given query is going to be executed thousands of times, could it be worth investing significantly more optimization time? In contrast to traditional online query optimizers, we propose an offline query optimizer that searches a wide variety of plans and incorporates query execution as a primitive. Our offline query optimizer combines variational au

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Chen, Rongxin, Zongyue Wang, and Yuling Hong. "Pipelined XPath Query Based on Cost Optimization." Scientific Programming 2021 (May 27, 2021): 1–16. http://dx.doi.org/10.1155/2021/5559941.

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XPath query is the key part of XML data processing, and its performance is usually critical for XML applications. In the process of XPath query, there is inherent seriality between query steps, which makes it difficult to parallelize the query effectively as a whole. On the other hand, although XPath query has the characteristics of data stream processing and is suitable for pipeline processing, the data flow of each query step usually varies a lot, which results in limited performance under multithreading conditions. In this paper, we propose a pipelined XPath query method (PXQ) based on cost

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S. Dhande, Sheetal, and Bamnote G.R. "Query Optimization in OODBMS: Identifying Subquery for Query Management." International Journal of Database Management Systems 6, no. 2 (2014): 49–66. http://dx.doi.org/10.5121/ijdms.2014.6204.

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Ye, Chen, Haoyang Duan, Hua Zhang, Yifan Wu, and Guojun Dai. "Learned Query Optimization by Constraint-Based Query Plan Augmentation." Mathematics 12, no. 19 (2024): 3102. http://dx.doi.org/10.3390/math12193102.

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Over the last decades, various cost-based optimizers have been proposed to generate optimal plans for SQL queries. These optimizers are key to achieving good performance in database systems and can speed up query execution. Still, they may need enormous expert efforts and perform poorly on complicated queries. Learning-based optimizers have been shown to achieve high-quality plans by learning from past experiences. However, these solutions treat each query separately and neglect the semantic equivalence among different queries. Intuitively, a high-quality plan may be obtained for a complicated

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Mella, Eduardo, M. Andrea Rodríguez, Loreto Bravo, and Diego Gatica. "Query rewriting for semantic query optimization in spatial databases." GeoInformatica 23, no. 1 (2019): 79–104. http://dx.doi.org/10.1007/s10707-018-00335-w.

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Kumar, T. V. Vijay, Rahul Singh, and Amit Kumar. "Distributed Query Plan Generation using Ant Colony Optimization." International Journal of Applied Metaheuristic Computing 6, no. 1 (2015): 1–22. http://dx.doi.org/10.4018/ijamc.2015010101.

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Query processing is a critical performance evaluation parameter and has received a considerable amount of attention especially in the context of distributed database systems. The aim of distributed query processing is to effectively and efficiently process the query. This entails laying down an optimal distributed query processing strategy that generates efficient query plans Since in distributed database systems, the data is distributed and replicated at multiple sites, the number of query plans increases exponentially with increase in the number of relations accessed by the query along with

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Wu, Bo Zhu. "Research on Query Optimization Technology in Distributed Database." Advanced Materials Research 433-440 (January 2012): 3335–39. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3335.

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Through the in-depth study of the existing distributed database query processing technology, this paper proposes a distributed database query processing program. This program optimizes the existing query processing, stores the commonly used query results according to the query frequency, to be directly used by the subsequent queries or used as intermediate query results, thus avoiding possible transmission of a large number of data, thereby reducing the query time and improving query efficiency.

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АльМусави, О. А. Р., and О. Я. Кравец. "Algorithmization of repeated query optimization in cloud databases with the aid of computer training." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 10, no. 1(36) (2022): 20–21. http://dx.doi.org/10.26102/2310-6018/2022.36.1.020.

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В облачных средах конфигурация оборудования, использование данных, и распределение рабочей нагрузки постоянно меняются. Эти изменения затрудняют оптимизатору запросов системы управления облачными базами данных подобрать оптимальный план выполнения запроса (QEP). Чтобы оптимизировать запрос с более точной оценкой затрат, в литературе было предложено во время выполнения запроса осуществлять повторную оптимизацию запроса. Тем не менее, некоторые из этих оптимизаций не могут обеспечить прирост производительности с точки зрения времени ответа на запрос или денежных затрат, которые являются двумя це

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Deb, Mrinal. "AI-Driven Adaptive Indexing and Query Optimization in Graph Databases." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03746.

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Abstract: Graph databases have emerged as a pivotal solution for managing intercon- nected data, providing a more intuitive way to model relationships compared to traditional relational databases. As the complexity and scale of the graph data increase, the need for efficient indexing and intelligent query optimization becomes paramount. This paper presents an AI-driven approach to adaptive indexing and query optimization in Neo4j, leveraging a movie dataset. By inte- grating Python-based preprocessing and fine-tuning an OpenAI language model on a custom schema, we demonstrate how natural langu

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Koch, Christoph, and Peter Lindner. "Query Optimization by Quantifier Elimination." Proceedings of the ACM on Management of Data 2, no. 2 (2024): 1–25. http://dx.doi.org/10.1145/3651607.

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Query optimizers have a limited arsenal of techniques for optimizing nested queries. In this paper, we develop a new approach for query optimization based on quantifier elimination. Quantifier elimination is a well-established tool for proving the decidability of logical theories. Here, however, we show that it can be turned into an effective query optimization technique that may yield asymptotic improvements in query processing efficiency. In addition, the technique establishes a foundation for certain well-known but previously little-understood aggregation based techniques for optimizing nes

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Dong, Rui, Jie Liu, Yuxuan Zhu, Cong Yan, Barzan Mozafari, and Xinyu Wang. "SlabCity: Whole-Query Optimization Using Program Synthesis." Proceedings of the VLDB Endowment 16, no. 11 (2023): 3151–64. http://dx.doi.org/10.14778/3611479.3611515.

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Query rewriting is often a prerequisite for effective query optimization, particularly for poorly-written queries. Prior work on query rewriting has relied on a set of "rules" based on syntactic pattern-matching. Whether relying on manual rules or auto-generated ones, rule-based query rewriters are inherently limited in their ability to handle new query patterns. Their success is limited by the quality and quantity of the rules provided to them. To our knowledge, we present the first synthesis-based query rewriting technique, SlabCity, capable of whole-query optimization without relying on any

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Kumar, T. V. Vijay, Amit Kumar, and Rahul Singh. "Distributed Query Plan Generation using Particle Swarm Optimization." International Journal of Swarm Intelligence Research 4, no. 3 (2013): 58–82. http://dx.doi.org/10.4018/ijsir.2013070104.

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A large number of queries are posed on databases spread across the globe. In order to process these queries efficiently, optimal query processing strategies that generate efficient query processing plans are being devised. In distributed relational database systems, due to replication of relations at multiple sites, the relations required to answer a query may necessitate accessing of data from multiple sites. This leads to an exponential increase in the number of possible alternative query plans for processing a query. Though it is not computationally feasible to explore all possible query pl

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Huang, Li, and Hong Bing Cheng. "Query Optimization Based on Data Provenance." Advanced Materials Research 186 (January 2011): 586–90. http://dx.doi.org/10.4028/www.scientific.net/amr.186.586.

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Data Provenance is a key of evaluating authority and uncertainty in data query. Query process technology based on data provenance overcomes the shortcomings of traditional data integration on query quality and efficiency. This paper constructs a data model of heterogeneous data sources provenance, i.e. Semiring Provenance, based on tracing provenance of data origination and evolution. It’s proved to be effective in creating mapping between heterogeneous schemas and optimizing query quality and authority evaluation. Experiments using real data set show that our approach provides an effective an

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Meng, Yao. "Study on Query Optimization of Distributed Database." Applied Mechanics and Materials 533 (February 2014): 448–51. http://dx.doi.org/10.4028/www.scientific.net/amm.533.448.

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Distributed query optimization in contemporary distributed database system increasingly important role, excellent query optimizer algorithm can effectively improve the query performance of the system. This article will be distributed query optimization hybrid algorithm is applied to improve the performance of distributed query optimization algorithms. Through simulation experiments and comparing two improved algorithms and the merits of the original algorithm, results were analyzed and show that the improved algorithm is better than the original algorithm has been improved to some extent in ac

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Kumar, Deepak, Deepti Mehrotra, and Rohit Bansal. "Query Optimization in Crowd-Sourcing Using Multi-Objective Ant Lion Optimizer." International Journal of Information Technology and Web Engineering 14, no. 4 (2019): 50–63. http://dx.doi.org/10.4018/ijitwe.2019100103.

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Nowadays, query optimization is a biggest concern for crowd-sourcing systems, which are developed for relieving the user burden of dealing with the crowd. Initially, a user needs to submit a structured query language (SQL) based query and the system takes the responsibility of query compiling, generating an execution plan, and evaluating the crowd-sourcing market place. The input queries have several alternative execution plans and the difference in crowd-sourcing cost between the worst and best plans. In relational database systems, query optimization is essential for crowd-sourcing systems,

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Song, Haoze, Wenchao Zhou, Feifei Li, Xiang Peng, and Heming Cui. "Rethink Query Optimization in HTAP Databases." Proceedings of the ACM on Management of Data 1, no. 4 (2023): 1–27. http://dx.doi.org/10.1145/3626750.

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The advent of data-intensive applications has fueled the evolution of hybrid transactional and analytical processing (HTAP). To support mixed workloads, distributed HTAP databases typically maintain two data copies that are specially tailored for data freshness and performance isolation. In particular, a copy in a row-oriented format is well-suited for OLTP workloads, and a second copy in a column-oriented format is optimized for OLAP workloads. Such a hybrid design opens up a new design space for query optimization: plans can be optimized over different data formats and can be executed over i

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Song, Ren Jie, and Yan Wang. "Query Optimization Based on the Simulated Annealing and Particle Swarm Optimization." Applied Mechanics and Materials 229-231 (November 2012): 1870–73. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1870.

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In order to allow the user to quickly and accurately search the required information, a query optimization method based on a simulated annealing and particle swarm hybrid algorithm is proposed. The basic idea is: the query population into two flat sub populations, a sub population by using simulated annealing algorithm optimization, another sub populations by using particle swarm algorithm optimization, comparison of two adaptive values, to find the global optimal value. The experimental results show that the mixed algorithm, can further improve the precision and recall of query optimization.

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Wagh, Ajay, and Varsha Nemade. "Query Optimization using Multiple Techniques." International Journal of Computer Applications 163, no. 3 (2017): 30–32. http://dx.doi.org/10.5120/ijca2017913490.

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Markl, Volker. "Making Learned Query Optimization Practical." ACM SIGMOD Record 51, no. 1 (2022): 5. http://dx.doi.org/10.1145/3542700.3542702.

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Query optimization has been a challenging problem ever since the relational data model had been proposed. The role of the query optimizer in a database system is to compute an execution plan for a (relational) query expression comprised of physical operators whose implementations correspond to the operations of the (relational) algebra. There are many degrees of freedom for selecting a physical plan, in particular due to the laws of associativity, commutativity, and distributivity among the operators in the (relational) algebra, which necessitates our taking the order of operations into consid

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MounirHassan, Mohamed, and Ahmed Mohammed Sultan. "SQOPI: Semantic Query Optimization Framework." International Journal of Computer Applications 96, no. 6 (2014): 27–32. http://dx.doi.org/10.5120/16800-6516.

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MENG, Xiao-Feng. "Research on XML Query Optimization." Journal of Software 17, no. 10 (2006): 2069. http://dx.doi.org/10.1360/jos172069.

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Sinha, M., and S. V. Chande. "Query Optimization Using Genetic Algorithms." Research Journal of Information Technology 2, no. 3 (2010): 139–44. http://dx.doi.org/10.3923/rjit.2010.139.144.

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