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Relevant bibliographies by topics / Optimization of SQL queries

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Academic literature on the topic 'Optimization of SQL queries'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "Optimization of SQL queries"

1

Ganski, Richard A., and Harry K. T. Wong. "Optimization of nested SQL queries revisited." ACM SIGMOD Record 16, no. 3 (December 1987): 23–33. http://dx.doi.org/10.1145/38714.38723.

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Ordonez, Carlos. "Optimization of Linear Recursive Queries in SQL." IEEE Transactions on Knowledge and Data Engineering 22, no. 2 (February 2010): 264–77. http://dx.doi.org/10.1109/tkde.2009.83.

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Ceri, S., and G. Gottlob. "Translating SQL Into Relational Algebra: Optimization, Semantics, and Equivalence of SQL Queries." IEEE Transactions on Software Engineering SE-11, no. 4 (April 1985): 324–45. http://dx.doi.org/10.1109/tse.1985.232223.

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Ji, Xuechun, Maoxian Zhao, Mingyu Zhai, and Qingxi Wu. "Query Execution Optimization in Spark SQL." Scientific Programming 2020 (February 7, 2020): 1–12. http://dx.doi.org/10.1155/2020/6364752.

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Spark SQL is a big data processing tool for structured data query and analysis. However, due to the execution of Spark SQL, there are multiple times to write intermediate data to the disk, which reduces the execution efficiency of Spark SQL. Targeting on the existing issues, we design and implement an intermediate data cache layer between the underlying file system and the upper Spark core to reduce the cost of random disk I/O. By using the query pre-analysis module, we can dynamically adjust the capacity of cache layer for different queries. And the allocation module can allocate proper memory for each node in cluster. According to the sharing of the intermediate data in the Spark SQL workflow, this paper proposes a cost-based correlation merging algorithm, which can effectively reduce the cost of reading and writing redundant data. This paper develops the SSO (Spark SQL Optimizer) module and integrates it into the original Spark system to achieve the above functions. This paper compares the query performance with the existing Spark SQL by experiment data generated by TPC-H tool. The experimental results show that the SSO module can effectively improve the query efficiency, reduce the disk I/O cost and make full use of the cluster memory resources.

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Seo, Kwangwon, Jinhyun Ahn, and Dong-Hyuk Im. "Optimization of Shortest-Path Search on RDBMS-Based Graphs." ISPRS International Journal of Geo-Information 8, no. 12 (December 1, 2019): 550. http://dx.doi.org/10.3390/ijgi8120550.

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Calculation of the shortest path between two nodes in a graph is a popular operation used in graph queries in applications such as map information systems, social networking services, and biotechnology. Recent shortest-path search techniques based on graphs stored in relational databases are able to calculate the shortest path efficiently, even in large data using frontier-expand-merge operations. However, previous approaches used a sequential bidirectional search method that causes a bottleneck, thus degrading performance. The repeated use of an aggregate SQL function also degrades performance. This paper proposes a parallel bi-directional search method using multithreading. In addition, an efficient optimization method is proposed that uses B-tree indexing instead of an aggregate SQL function. Various experiments using synthetic and real data reveal that the proposed optimization technique performs more efficiently than conventional methods. As the size of data in practical applications continues to grow, these optimizations will enable the shortest path in a graph to be found quickly and accurately.

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Khashan, Eman, Ali Eldesouky, and Sally Elghamrawy. "An adaptive spark-based framework for querying large-scale NoSQL and relational databases." PLOS ONE 16, no. 8 (August 19, 2021): e0255562. http://dx.doi.org/10.1371/journal.pone.0255562.

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The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others’ efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios’ benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.

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K Sirohi, Arjun. "Optimization of Dynamically Generated SQL Queries for Tiny-Huge, Huge-Tiny Problem." International Journal of Database Management Systems 5, no. 1 (February 28, 2013): 53–68. http://dx.doi.org/10.5121/ijdms.2013.5105.

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Zervoudakis, Petros, Haridimos Kondylakis, Nicolas Spyratos, and Dimitris Plexousakis. "Query Rewriting for Incremental Continuous Query Evaluation in HIFUN." Algorithms 14, no. 5 (May 8, 2021): 149. http://dx.doi.org/10.3390/a14050149.

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HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.

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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 (October 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, which provides declarative query interfaces. Here, a multi-objective query optimization approach using an ant-lion optimizer was employed for declarative crowd-sourcing systems. It generates a query plan for developing a better balance between the latency and cost. The experimental outcome of the proposed methodology was validated on UCI automobile and Amazon Mechanical Turk (AMT) datasets. The proposed methodology saves 30%-40% of cost in crowd-sourcing query optimization compared to the existing methods.

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Kotenko, Igor, Andrey Krasov, Igor Ushakov, and Konstantin Izrailov. "An Approach for Stego-Insider Detection Based on a Hybrid NoSQL Database." Journal of Sensor and Actuator Networks 10, no. 2 (March 30, 2021): 25. http://dx.doi.org/10.3390/jsan10020025.

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One of the reasons for the implementation of information security threats in organizations is the insider activity of its employees. There is a big challenge to detect stego-insiders-employees who create stego-channels to secretly receive malicious information and transfer confidential information across the organization’s perimeter. Especially presently, with great popularity of wireless sensor networks (WSNs) and Internet of Things (IoT) devices, there is a big variety of information that could be gathered and processed by stego-insiders. Consequently, the problem arises of identifying such intruders and their transmission channels. The paper proposes an approach to solving this problem. The paper provides a review of the related works in terms of insider models and methods of their identification, including techniques for handling insider attacks in WSN, as well methods of embedding and detection of stego-embeddings. This allows singling out the basic features of stego-insiders, which could be determined by their behavior in the network. In the interests of storing these attributes of user behavior, as well as storing such attributes from large-scale WSN, a hybrid NoSQL database is created based on graph and document-oriented approaches. The algorithms for determining each of the features using the NoSQL database are specified. The general scheme of stego-insider detection is also provided. To confirm the efficiency of the approach, an experiment was carried out on a real network. During the experiment, a database of user behavior was collected. Then, user behavior features were retrieved from the database using special SQL queries. The analysis of the results of SQL queries is carried out, and their applicability for determining the attribute is justified. Weak points of the approach and ways to improve them are indicated.

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Dissertations / Theses on the topic "Optimization of SQL queries"

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Hasan, Waqar. "Optimization of SQL queries for parallel machines /." Berlin [u.a.] : Springer, 1996. http://www.loc.gov/catdir/enhancements/fy0815/96039704-d.html.

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Muller, Leslie. "'n Ondersoek na en bydraes tot navraaghantering en -optimering deur databasisbestuurstelsels / L. Muller." Thesis, North-West University, 2006. http://hdl.handle.net/10394/1181.

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The problems associated with the effective design and uses of databases are increasing. The information contained in a database is becoming more complex and the size of the data is causing space problems. Technology must continually develop to accommodate this growing need. An inquiry was conducted in order to find effective guidelines that could support queries in general in terms of performance and productivity. Two database management systems were researched to compare die theoretical aspects with the techniques implemented in practice. Microsoft SQL Sewer and MySQL were chosen as the candidates and both were put under close scrutiny. The systems were researched to uncover the methods employed by each to manage queries. The query optimizer forms the basis for each of these systems and manages the parsing and execution of any query. The methods employed by each system for storing data were researched. The way that each system manages table joins, uses indices and chooses optimal execution plans were researched. Adjusted algorithms were introduced for various index processes like B+ trees and hash indexes. Guidelines were compiled that are independent of the database management systems and help to optimize relational databases. Practical implementations of queries were used to acquire and analyse the execution plan for both MySQL and SQL Sewer. This plan along with a few other variables such as execution time is discussed for each system. A model is used for both database management systems in this experiment.
Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2007.

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Janeček, Jiří. "Optimalizace strukturovaných dotazů nad rozsáhlými databázemi." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-412868.

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This master's thesis deals with optimization of structured queries on large databases. Principles of these optimizations are used during creation of application, which allows finding over one specific large database. At the same time this thesis compares efficiency between the new designed SQL constructions and the not optimized SQL constructions.

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Ferreira, Mônica Ribeiro Porto. "Suporte a consultas por similaridade unárias em SQL." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-01042008-101843/.

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Os operadores convencionais para comparação de dados por igualdade e por relação de ordem total não são adequados para o gerenciamento de dados complexos como, por exemplo, os dados multimí?dia (imagens, áudio, textos longos), séries temporais e seqüências genéticas. Para comparar dados desses tipos, o grau de similaridade entre suas instâncias é, em geral, o fator mais importante sendo, portanto, indicado que as operações de consulta sejam realizadas utilizando os chamados operadores por similaridade. Existem operadores de busca por similaridade tanto unários quanto binários. Os operadores unários são utilizados para implementar operações de seleção, enquanto os operadores binários destinam-se a operações de junção. A álgebra relacional, usada nos Sistemas de Gerenciamento de Bases de Dados Relacionais, não provê suporte para expressar critérios de busca por similaridade. Para suprir esse suporte, está em desenvolvimento no Grupo de Bases de Dados e Imagens (GBdI-ICMC-USP) uma extensão à álgebra relacional que permite representar as consultas por similaridade em expressões algébricas. Esta dissertação incorpora-se nesse empreendimento, abordando o tratamento aos operadores unários por similaridade na álgebra, bem como a implementação do otimizador de consultas por similaridade no SIREN (Similarity Retrieval Engine) para que as consultas por similaridade possam ser respondidas pelos Sistemas de Gerenciamento de Bases de Dados relacionais
Conventional operators for data comparison based on exact matching and total order relations are not appropriate to manage complex data, such as multimedia data (e.g. images, audio and large texts), time series and genetic sequences. In fact, the most important aspect to compare complex data is usually the similarity degree between instances, leading to the use of similarity operators to perform search and retrieval operations. Similarity operators can be classified as unary or as binary, respectively used to implement selection operations and joins. However, the Relation Algebra, employed in Relational Database Management Systems (DBMS), does not provide resources to express similarity search criteria. In order to fulfill this lack of support, an extension to the Relational Algebra is under development at GBdI-ICMC-USP (Grupo de Bases de Dados e Imagens), aiming to represent similarity queries in algebraic expressions. This work contributes to such an effort by dealing with unary similarity operators in Relational Algebra and by developing a similarity query optimizer for SIREN (Similarity Retrieval Engine), therefore allowing similarity queries to be answered by Relational DBMS

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Mounagurusamy, Purani. "Parsing AQL Queries into SQL Queries using ANTLR." Thesis, Linköpings universitet, Databas och informationsteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-124151.

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An Electronic Health Record is a collection of each patient’s health information which is stored electronically or in digital format. openEHR is an open standard specification for electronic health record data. openEHR has a method for querying a set of clinical data using the Archetype Query Language (AQL). The EHR data is in XML format and this format is a tree like structure. Since XML databases were considerably slower, AQL needs to be translated to another query language. Researchers have already investigated translating AQL to XQuery and tested the performance. Since the performance was not satisfactory, we now investigate translating AQL to SQL. AQL queries are translated to SQL queries using the ANTLR tool. The translation is implemented in Java language. The AQL queries which are translated into SQL queries are also tested in this thesis work. Finally, the result is to get the corresponding SQL query for any given AQL query.

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Venkatamuniyappa, Vijay Kumar. "Towards automatic grading of SQL queries." Kansas State University, 2018. http://hdl.handle.net/2097/38819.

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Master of Science
Department of Computer Science
Doina Caragea
An Introduction to Databases course involves learning the concepts of data storage, manipulation, and retrieval. Relational databases provide an ideal learning path for understanding database concepts. The Structured Query Language (SQL) is a standard language for interacting with relational database. Each database vendor implements a variation of the SQL standard. Furthermore, a particular question that asks for some data can be written in many ways, using somewhat similar or structurally different SQL queries. Evaluation of SQL queries for correctness involves the verification of the SQL syntax and semantics, as well as verification of the output of queries and the usage of correct clauses. An evaluation tool should be independent of the specific database queried, and of the nature of the queries, and should allow multiple ways of providing input and retrieving the output. In this report, we have developed an evaluation tool for SQL queries, which checks for correctness of MySQL and PostgreSQL queries with the help of a parser that can identify SQL clauses. The tool developed will act as a portal for students to test and improve their queries, and finally to submit the queries for grading. The tool minimizes the manual effort required while grading, by taking advantage of the SQL parser to check queries for correctness, provide feedback, and allow submission.

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Jain, Ritika. "Validation of SQL queries over streaming warehouses." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62867.

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There is often a need to recover the “missing” query that produced a particular output from a data stream. As an example, since a data stream is constantly evolving, the analyst may be curious about using the query from the past to evaluate it on the current state of the data stream, for further analysis. Previous research has studied the problem of reverse engineering a query that would produce a given result at a particular database state. We study the following problem. Given a streaming database D=, a result Rout , and a set of candidate queries Q, efficiently find all queries Qi ϵ Q such that for some state Dji of the stream, Qi(Dji) = Rout , and report the pair (Q,witQ) where witQ is the witness of (in)validity. A witness for a valid query Qval is a state Di s.t. Qval(Di) = Rout. For an invalid query Qinval , a witness is a pair of consecutive states (Di, Di+1) such that Rout \ Qinval (Di) ≠ Ø ≠ Qinval (Di+1) \ Rout. We allow any PTIME computable monotone query to be included in Q. While techniques developed in previous research can be used to generate the candidate query set Q, we focus on developing a scalable strategy for quickly determining the witness. We establish theoretical worst-case performance guarantees for our proposed approach and show that it is no more than a factor of O(log |DRDS|) of the optimal “Lucky guess” strategy, where Q(DRDS) = Rout. We empirically evaluate our technique and compare with natural baselines inspired from previous research. We show that the baselines either fail to scale or incur an inordinate amount of overhead by failing to take advantage of natural properties of a data stream. By contrast, our strategy scales effortlessly for very large data streams. Moreover, it never performs more than a small constant times the optimal amount of work, regardless of the state of the data stream that may have led to Rout.
Science, Faculty of
Computer Science, Department of
Graduate

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Manzi, Eric R. "SQL-ACT : content-based and history-aware input prediction for non-trivial SQL queries." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/119534.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 79-81).
This thesis presents SqlAct, a SQL auto-completion system that uses content-based and history-aware input prediction to assist in the process of composing non-trivial queries. By offering the most relevant suggestions to complete the partially typed query first at the word-level and then at the statement-level, SqlAct hopes to help both novice and expert SQL developers to increase their productivity. Two approaches are explored: word-level suggestions are optimized based on the database's schema and content statistics, and statement-level suggestions that rely on Long Short-term Memory (LSTM) Recurrent Neural Networks language models trained on historical queries. The word-level model is integrated in a responsive command-line interface database client which is evaluated quantitatively and qualitatively. Results shows SqlAct provides a highly-responsive interface that makes high quality suggestions to complete the currently typed query. Possible directions for integration with the word-level model in the command-line tool are explored as well as the planned evaluation techniques.
by Eric R. Manzi.
M. Eng.

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Escalante, Osuna Carlos. "Estimating the cost of GraphLog queries." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0002/NQ32743.pdf.

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Trigoni, Agathoniki. "Semantic optimization of OQL queries." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620163.

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Books on the topic "Optimization of SQL queries"

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Hasan, Waqar, ed. Optimization of SQL Queries for Parallel Machines. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0020710.

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Optimization of SQL queries for parallel machines. Berlin: Springer, 1996.

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Churcher, Clare. Beginning SQL Queries. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1955-3.

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Tennick, Art. Practical SQL queries for Microsoft SQL server 2008 R2. New York: McGraw-Hill, 2011.

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Lewis, Gary M. ORACLE reporting: Queries with SQL objects. Cambridge, MA: Komenda Pub. Co., 1995.

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

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Microsoft SQL Server 2000 optimization guide. Upper Saddle River, N.J: Prentice Hall PTR, 2001.

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

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Tennick, Art. Practical DMX queries for SQL server analysis services. New York: McGraw-Hill, 2010.

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1955-, Hernandez Michael J., and Hernandez Michael J. 1955-, eds. SQL queries for mere mortals: A hands-on guide to data manipulation in SQL. 2nd ed. Upper Saddle River, NJ: Addison-Wesley, 2008.

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Book chapters on the topic "Optimization of SQL queries"

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Aggelis, Vasilis, and Stavros Cosmadakis. "Optimization of Nested SQL Queries by Tableau Equivalence." In Research Issues in Structured and Semistructured Database Programming, 31–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44543-9_3.

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Hameurlain, A., and F. Morvan. "An optimization method of data communication and control for parallel execution of SQL queries." In Lecture Notes in Computer Science, 301–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57234-1_27.

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Cybula, Piotr, and Kazimierz Subieta. "Query Optimization through Cached Queries for Object-Oriented Query Language SBQL." In SOFSEM 2010: Theory and Practice of Computer Science, 308–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11266-9_26.

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Pazos R., Rodolfo A., Andres A. Verastegui, José A. Martínez F., Martin Carpio, and Juana Gaspar H. "Translation of Natural Language Queries to SQL that Involve Aggregate Functions, Grouping and Subqueries for a Natural Language Interface to Databases." In Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, 431–48. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71008-2_31.

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Harkins, Susan Sales, and Martin W. P. Reid. "Queries: The Sequel." In SQL: Access to SQL Server, 253–78. Berkeley, CA: Apress, 2002. http://dx.doi.org/10.1007/978-1-4302-1573-8_11.

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Batra, Rahul. "Writing Basic Queries." In SQL Primer, 47–56. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3576-8_5.

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Darie, Cristian, Karli Watson, Chris Hart, Kevin Hoffman, and Julian Skinner. "Combining SQL Queries." In The Programmer’s Guide to SQL, 147–66. Berkeley, CA: Apress, 2003. http://dx.doi.org/10.1007/978-1-4302-0800-6_6.

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Batra, Rahul. "Doing More with Queries." In SQL Primer, 75–85. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3576-8_8.

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Hasler, Tony. "Rewriting Queries." In Exper t Oracle SQL, 425–44. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-5978-7_16.

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Kellenberger, Kathi, and Lee Everest. "Writing Advanced Queries." In Beginning T-SQL, 611–58. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6606-9_16.

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Conference papers on the topic "Optimization of SQL queries"

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Ganski, Richard A., and Harry K. T. Wong. "Optimization of nested SQL queries revisited." In the 1987 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1987. http://dx.doi.org/10.1145/38713.38723.

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Myalapalli, Vamsi Krishna, and A. S. N. Chakravarthy. "Revamping SQL queries for cost based optimization." In 2016 International Conference on Circuits, Controls, Communications and Computing (I4C). IEEE, 2016. http://dx.doi.org/10.1109/cimca.2016.8053263.

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Ordonez, Carlos, Javier García-García, and Zhibo Chen. "Dynamic optimization of generalized SQL queries with horizontal aggregations." In the 2012 international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2213836.2213919.

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Das, Gopal Chandra, and Jayant R. Haritsa. "Robust Heuristics for Scalable Optimization of Complex SQL Queries." In 2007 IEEE 23rd International Conference on Data Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icde.2007.368993.

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Lee, Kyeong-Min, InA Kim, and Kyu-Chul Lee. "DQN-based Join Order Optimization by Learning Experiences of Running Queries on Spark SQL." In 2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 2020. http://dx.doi.org/10.1109/icdmw51313.2020.00107.

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K. Sirohi, Arjun, and Vidushi Sharma. "Context-sensitive Indexes in RDBMS for Performance Optimization of SQL Queries in Multi-tenant/Multi-application Environments." In 17th International Conference on Enterprise Information Systems. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005350802590270.

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Motwani, Rajeev, Shubha U. Nabar, and Dilys Thomas. "Auditing SQL Queries." In 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497437.

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Zhong, Zhikai. "Plunger Style Optimization by Machine Learning." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/208633-stu.

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Abstract:

Abstract Oil and gas wells often need artificial lift technologies to help extract reservoir fluids as the wells age and the reservoir pressure decreases–among those technologies is plunger lift. Plungers are metal cylinders that fit snug inside the tubing in which they are still able to slide freely. Plungers are often used with gas lift: they get pushed up and down the tubing like a piston to unload all the fluids by periodically shut in and open up the well. Many plungers contain a one-way valve that enables them to fall through flow easily and rise to the surface with a seal to prevent fluid slippage. There are many styles of plungers based on their weight, fall speed, and embedded one-way valves’ mechanism, and they should be used in different kinds of wells. Plungers can be grouped into five major different categories. This project utilizes machine learning and data analytics to predict what the most optimal type of plungers is for a given well in order to reduce gas injection and maximize liquid production. To accomplish this project, more than two million rows of raw plunger lift production data were queried using SQL, a database query language, then pivoted, cleaned, and turned into a data table with approximately two hundred twenty thousand rows. The data came from about 900 wells with a time span of more than 400 days of production. Utilizing the python package Scikit Learn's random forest regressor, five separate machine learning models were trained, tuned, and cross-validated to predict the daily revenue and/or efficiency of a well if it were to run with the corresponding style of the plunger. The models were able to achieve about .85-.90 accuracy scores. On the other hand, a data visualization guide was built to visualize all the past plunger operation history to analyze the efficiency of a type of plunger; it also acts as an educational tool for operators to study the behaviors of different plungers in various wells. The results of this project were achieved by field testing. Given that there was a time constraint, approximately ten wells were tested with eight of them showing revenue and/or efficiency improvements of 5-10%. More testing, tuning, and data gathering need to be done in the future to improve the project to apply at a larger scale.

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Park, Hyun-A., Justin Zhan, and Dong Hoon Lee. "Privacy Preserving SQL Queries." In 2008 International Conference on Information Security and Assurance ISA. IEEE, 2008. http://dx.doi.org/10.1109/isa.2008.40.

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Kaoshik, Ronak, Rohit Patil, Prakash R, Shaurya Agarawal, Naman Jain, and Mayank Singh. "ACL-SQL: Generating SQL Queries from Natural Language." In CODS COMAD 2021: 8th ACM IKDD CODS and 26th COMAD. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3430984.3431046.

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Reports on the topic "Optimization of SQL queries"

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

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