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Статті в журналах з теми "Query processing and optimisation"
Diallo, Ousmane, Joel J. P. C. Rodrigues, Mbaye Sene, and Feng Xia. "Real-time query processing optimisation for wireless sensor networks." International Journal of Sensor Networks 18, no. 1/2 (2015): 49. http://dx.doi.org/10.1504/ijsnet.2015.069863.
Повний текст джерелаAkili, Samira, and Matthias Weidlich. "Reasoning on the Efficiency of Distributed Complex Event Processing." Fundamenta Informaticae 179, no. 2 (March 10, 2021): 113–34. http://dx.doi.org/10.3233/fi-2021-2017.
Повний текст джерелаKumar A, Dinesh, and S. Smys. "A clique-based scheduling in real-time query processing optimisation for cloud-based wireless body area networks." International Journal of Biomedical Engineering and Technology 29, no. 4 (2019): 327. http://dx.doi.org/10.1504/ijbet.2019.10022031.
Повний текст джерелаA, Dinesh Kumar, and S. Smys. "A clique-based scheduling in real-time query processing optimisation for cloud-based wireless body area networks." International Journal of Biomedical Engineering and Technology 29, no. 4 (2019): 327. http://dx.doi.org/10.1504/ijbet.2019.100268.
Повний текст джерелаChun, Sejin, Jooik Jung, Seungmin Seo, Wonwoo Ro, and Kyong-Ho Lee. "An adaptive plan-based approach to integrating semantic streams with remote RDF data." Journal of Information Science 43, no. 6 (October 1, 2016): 852–65. http://dx.doi.org/10.1177/0165551516670278.
Повний текст джерелаHaryanto, Anasthasia Agnes, David Taniar, and Kiki Maulana Adhinugraha. "Group Reverse kNN Query optimisation." Journal of Computational Science 11 (November 2015): 205–21. http://dx.doi.org/10.1016/j.jocs.2015.09.006.
Повний текст джерелаDeshpande, Amol, Zachary Ives, and Vijayshankar Raman. "Adaptive Query Processing." Foundations and Trends® in Databases 1, no. 1 (2007): 1–140. http://dx.doi.org/10.1561/1900000001.
Повний текст джерелаWEI, Xiao-Juan. "Skyline Query Processing." Journal of Software 19, no. 6 (October 21, 2008): 1386–400. http://dx.doi.org/10.3724/sp.j.1001.2008.01386.
Повний текст джерелаTang, Dixin, Zechao Shang, Aaron J. Elmore, Sanjay Krishnan, and Michael J. Franklin. "Intermittent query processing." Proceedings of the VLDB Endowment 12, no. 11 (July 2019): 1427–41. http://dx.doi.org/10.14778/3342263.3342278.
Повний текст джерелаHaritsa, Jayant R. "Robust query processing." Proceedings of the VLDB Endowment 13, no. 12 (August 2020): 3425–28. http://dx.doi.org/10.14778/3415478.3415561.
Повний текст джерелаДисертації з теми "Query processing and optimisation"
Manolescu, Ioana. "Efficient XML query processing." Habilitation à diriger des recherches, Université Paris Sud - Paris XI, 2009. http://tel.archives-ouvertes.fr/tel-00542801.
Повний текст джерелаAl-Hoqani, Noura Y. S. "In-network database query processing for wireless sensor networks." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/36226.
Повний текст джерелаBelghoul, Abdeslem. "Optimizing Communication Cost in Distributed Query Processing." Thesis, Université Clermont Auvergne (2017-2020), 2017. http://www.theses.fr/2017CLFAC025/document.
Повний текст джерелаIn this thesis, we take a complementary look to the problem of optimizing the time for communicating query results in distributed query processing, by investigating the relationship between the communication time and the middleware configuration. Indeed, the middleware determines, among others, how data is divided into batches and messages before being communicated over the network. Concretely, we focus on the research question: given a query Q and a network environment, what is the best middleware configuration that minimizes the time for transferring the query result over the network? To the best of our knowledge, the database research community does not have well-established strategies for middleware tuning. We present first an intensive experimental study that emphasizes the crucial impact of middleware configuration on the time for communicating query results. We focus on two middleware parameters that we empirically identified as having an important influence on the communication time: (i) the fetch size F (i.e., the number of tuples in a batch that is communicated at once to an application consuming the data) and (ii) the message size M (i.e., the size in bytes of the middleware buffer, which corresponds to the amount of data that can be communicated at once from the middleware to the network layer; a batch of F tuples can be communicated via one or several messages of M bytes). Then, we describe a cost model for estimating the communication time, which is based on how data is communicated between computation nodes. Precisely, our cost model is based on two crucial observations: (i) batches and messages are communicated differently over the network: batches are communicated synchronously, whereas messages in a batch are communicated in pipeline (asynchronously), and (ii) due to network latency, it is more expensive to communicate the first message in a batch compared to any other message that is not the first in its batch. We propose an effective strategy for calibrating the network-dependent parameters of the communication time estimation function i.e, the costs of first message and non first message in their batch. Finally, we develop an optimization algorithm to effectively compute the values of the middleware parameters F and M that minimize the communication time. The proposed algorithm allows to quickly find (in small fraction of a second) the values of the middleware parameters F and M that translate a good trade-off between low resource consumption and low communication time. The proposed approach has been evaluated using a dataset issued from application in Astronomy
Mesmoudi, Amin. "Declarative parallel query processing on large scale astronomical databases." Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10326.
Повний текст джерелаThis work is carried out in framework of the PetaSky project. The objective of this project is to provide a set of tools allowing to manage Peta-bytes of data from astronomical observations. Our work is concerned with the design of a scalable approach. We first started by analyzing the ability of MapReduce based systems and supporting SQL to manage the LSST data and ensure optimization capabilities for certain types of queries. We analyzed the impact of data partitioning, indexing and compression on query performance. From our experiments, it follows that there is no “magic” technique to partition, store and index data but the efficiency of dedicated techniques depends mainly on the type of queries and the typology of data that are considered. Based on our work on benchmarking, we identified some techniques to be integrated to large-scale data management systems. We designed a new system allowing to support multiple partitioning mechanisms and several evaluation operators. We used the BSP (Bulk Synchronous Parallel) model as a parallel computation paradigm. Unlike MapeReduce model, we send intermediate results to workers that can continue their processing. Data is logically represented as a graph. The evaluation of queries is performed by exploring the data graph using forward and backward edges. We also offer a semi-automatic partitioning approach, i.e., we provide the system administrator with a set of tools allowing her/him to choose the manner of partitioning data using the schema of the database and domain knowledge. The first experiments show that our approach provides a significant performance improvement with respect to Map/Reduce systems
Oğuz, Damla. "Méthodes d'optimisation pour le traitement de requêtes réparties à grande échelle sur des données liées." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30067/document.
Повний текст джерелаLinked Data is a term to define a set of best practices for publishing and interlinking structured data on the Web. As the number of data providers of Linked Data increases, the Web becomes a huge global data space. Query federation is one of the approaches for efficiently querying this distributed data space. It is employed via a federated query engine which aims to minimize the response time and the completion time. Response time is the time to generate the first result tuple, whereas completion time refers to the time to provide all result tuples. There are three basic steps in a federated query engine which are data source selection, query optimization, and query execution. This thesis contributes to the subject of query optimization for query federation. Most of the studies focus on static query optimization which generates the query plans before the execution and needs statistics. However, the environment of Linked Data has several difficulties such as unpredictable data arrival rates and unreliable statistics. As a consequence, static query optimization can cause inefficient execution plans. These constraints show that adaptive query optimization should be used for federated query processing on Linked Data. In this thesis, we first propose an adaptive join operator which aims to minimize the response time and the completion time for federated queries over SPARQL endpoints. Second, we extend the first proposal to further reduce the completion time. Both proposals can change the join method and the join order during the execution by using adaptive query optimization. The proposed operators can handle different data arrival rates of relations and the lack of statistics about them. The performance evaluation of this thesis shows the efficiency of the proposed adaptive operators. They provide faster completion times and almost the same response times, compared to symmetric hash join. Compared to bind join, the proposed operators perform substantially better with respect to the response time and can also provide faster completion times. In addition, the second proposed operator provides considerably faster response time than bind-bloom join and can improve the completion time as well. The second proposal also provides faster completion times than the first proposal in all conditions. In conclusion, the proposed adaptive join operators provide the best trade-off between the response time and the completion time. Even though our main objective is to manage different data arrival rates of relations, the performance evaluation reveals that they are successful in both fixed and different data arrival rates
Gillani, Syed. "Semantically-enabled stream processing and complex event processing over RDF graph streams." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES055/document.
Повний текст джерелаThere is a paradigm shift in the nature and processing means of today’s data: data are used to being mostly static and stored in large databases to be queried. Today, with the advent of new applications and means of collecting data, most applications on the Web and in enterprises produce data in a continuous manner under the form of streams. Thus, the users of these applications expect to process a large volume of data with fresh low latency results. This has resulted in the introduction of Data Stream Processing Systems (DSMSs) and a Complex Event Processing (CEP) paradigm – both with distinctive aims: DSMSs are mostly employed to process traditional query operators (mostly stateless), while CEP systems focus on temporal pattern matching (stateful operators) to detect changes in the data that can be thought of as events. In the past decade or so, a number of scalable and performance intensive DSMSs and CEP systems have been proposed. Most of them, however, are based on the relational data models – which begs the question for the support of heterogeneous data sources, i.e., variety of the data. Work in RDF stream processing (RSP) systems partly addresses the challenge of variety by promoting the RDF data model. Nonetheless, challenges like volume and velocity are overlooked by existing approaches. These challenges require customised optimisations which consider RDF as a first class citizen and scale the processof continuous graph pattern matching. To gain insights into these problems, this thesis focuses on developing scalable RDF graph stream processing, and semantically-enabled CEP systems (i.e., Semantic Complex Event Processing, SCEP). In addition to our optimised algorithmic and data structure methodologies, we also contribute to the design of a new query language for SCEP. Our contributions in these two fields are as follows: • RDF Graph Stream Processing. We first propose an RDF graph stream model, where each data item/event within streams is comprised of an RDF graph (a set of RDF triples). Second, we implement customised indexing techniques and data structures to continuously process RDF graph streams in an incremental manner. • Semantic Complex Event Processing. We extend the idea of RDF graph stream processing to enable SCEP over such RDF graph streams, i.e., temporalpattern matching. Our first contribution in this context is to provide a new querylanguage that encompasses the RDF graph stream model and employs a set of expressive temporal operators such as sequencing, kleene-+, negation, optional,conjunction, disjunction and event selection strategies. Based on this, we implement a scalable system that employs a non-deterministic finite automata model to evaluate these operators in an optimised manner. We leverage techniques from diverse fields, such as relational query optimisations, incremental query processing, sensor and social networks in order to solve real-world problems. We have applied our proposed techniques to a wide range of real-world and synthetic datasets to extract the knowledge from RDF structured data in motion. Our experimental evaluations confirm our theoretical insights, and demonstrate the viability of our proposed methods
Alrammal, Muath. "Algorithms for XML stream processing : massive data, external memory and scalable performance." Phd thesis, Université Paris-Est, 2011. http://tel.archives-ouvertes.fr/tel-00779309.
Повний текст джерелаPhan, Duy-Hung. "Algorithmes d'aggrégation pour applications Big Data." Electronic Thesis or Diss., Paris, ENST, 2016. http://www.theses.fr/2016ENST0043.
Повний текст джерелаTraditional databases are facing problems of scalability and efficiency dealing with a vast amount of big-data. Thus, modern data management systems that scale to thousands of nodes, like Apache Hadoop and Spark, have emerged and become the de-facto platforms to process data at massive scales. In such systems, many data processing optimizations that were well studied in the database domain have now become futile because of the novel architectures and programming models. In this context, this dissertation pledged to optimize one of the most predominant operations in data processing: data aggregation for such systems.Our main contributions were the logical and physical optimizations for large-scale data aggregation, including several algorithms and techniques. These optimizations are so intimately related that without one or the other, the data aggregation optimization problem would not be solved entirely. Moreover, we integrated these optimizations in our multi-query optimization engine, which is totally transparent to users. The engine, the logical and physical optimizations proposed in this dissertation formed a complete package that is runnable and ready to answer data aggregation queries at massive scales. We evaluated our optimizations both theoretically and experimentally. The theoretical analyses showed that our algorithms and techniques are much more scalable and efficient than prior works. The experimental results using a real cluster with synthetic and real datasets confirmed our analyses, showed a significant performance boost and revealed various angles about our works. Last but not least, our works are published as open sources for public usages and studies
Camacho, Rodriguez Jesus. "Efficient techniques for large-scale Web data management." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112229/document.
Повний текст джерелаThe recent development of commercial cloud computing environments has strongly impacted research and development in distributed software platforms. Cloud providers offer a distributed, shared-nothing infrastructure, that may be used for data storage and processing.In parallel with the development of cloud platforms, programming models that seamlessly parallelize the execution of data-intensive tasks over large clusters of commodity machines have received significant attention, starting with the MapReduce model very well known by now, and continuing through other novel and more expressive frameworks. As these models are increasingly used to express analytical-style data processing tasks, the need for higher-level languages that ease the burden of writing complex queries for these systems arises.This thesis investigates the efficient management of Web data on large-scale infrastructures. In particular, we study the performance and cost of exploiting cloud services to build Web data warehouses, and the parallelization and optimization of query languages that are tailored towards querying Web data declaratively.First, we present AMADA, an architecture for warehousing large-scale Web data in commercial cloud platforms. AMADA operates in a Software as a Service (SaaS) approach, allowing users to upload, store, and query large volumes of Web data. Since cloud users support monetary costs directly connected to their consumption of resources, our focus is not only on query performance from an execution time perspective, but also on the monetary costs associated to this processing. In particular, we study the applicability of several content indexing strategies, and show that they lead not only to reducing query evaluation time, but also, importantly, to reducing the monetary costs associated with the exploitation of the cloud-based warehouse.Second, we consider the efficient parallelization of the execution of complex queries over XML documents, implemented within our system PAXQuery. We provide novel algorithms showing how to translate such queries into plans expressed in the PArallelization ConTracts (PACT) programming model. These plans are then optimized and executed in parallel by the Stratosphere system. We demonstrate the efficiency and scalability of our approach through experiments on hundreds of GB of XML data.Finally, we present a novel approach for identifying and reusing common subexpressions occurring in Pig Latin scripts. In particular, we lay the foundation of our reuse-based algorithms by formalizing the semantics of the Pig Latin query language with extended nested relational algebra for bags. Our algorithm, named PigReuse, operates on the algebraic representations of Pig Latin scripts, identifies subexpression merging opportunities, selects the best ones to execute based on a cost function, and merges other equivalent expressions to share its result. We bring several extensions to the algorithm to improve its performance. Our experiment results demonstrate the efficiency and effectiveness of our reuse-based algorithms and optimization strategies
Geng, Ke. "XML semantic query optimisation." Thesis, University of Auckland, 2011. http://hdl.handle.net/2292/6815.
Повний текст джерелаКниги з теми "Query processing and optimisation"
Photothongtham, Sant. Query processing and optimisation on ERT-SQL. Manchester: UMIST, 1997.
Знайти повний текст джерелаA, Al-Ghabra M. Investigation of Databases Query Optimisation. London: University ofEast London, 1995.
Знайти повний текст джерелаDeshpande, Amol. Adaptive query processing. Boston: Now, 2007.
Знайти повний текст джерелаCatania, Barbara, and Lakhmi C. Jain, eds. Advanced Query Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28323-9.
Повний текст джерелаGao, Yunjun, and Xiaoye Miao. Query Processing over Incomplete Databases. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01863-3.
Повний текст джерелаKim, Won, David S. Reiner, and Don S. Batory, eds. Query Processing in Database Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82375-6.
Повний текст джерелаKim, Won. Query Processing in Database Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985.
Знайти повний текст джерелаCardiff, J. The design of an efficient and extensiblesystem for performing semantic query optimisation. Dublin: Trinity College, Department of Computer Science, 1991.
Знайти повний текст джерела1954-, Freytag Johann Christoph, Maier David 1953-, and Vossen Gottfried, eds. Query processing for advanced database systems. San Mateo, Calif: Morgan Kaufmann Publishers, 1994.
Знайти повний текст джерелаSignorile, Danielle Larocca. SAP Query Reporting. Upper Saddle River: Sams Publishing, 2007.
Знайти повний текст джерелаЧастини книг з теми "Query processing and optimisation"
Noon, Nan N., and Janusz R. Getta. "Optimisation of Query Processing with Multilevel Storage." In Intelligent Information and Database Systems, 691–700. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49390-8_67.
Повний текст джерелаBertino, Elisa, Barbara Catania, and Elena Ferrari. "Query Processing." In Multimedia Databases in Perspective, 181–217. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0957-0_9.
Повний текст джерелаPitoura, Evaggelia. "Query Processing." In Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_860-2.
Повний текст джерелаPitoura, Evaggelia. "Query Processing." In Encyclopedia of Database Systems, 2288. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_860.
Повний текст джерелаSciore, Edward. "Query Processing." In Database Design and Implementation, 213–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33836-7_8.
Повний текст джерелаPitoura, Evaggelia. "Query Processing." In Encyclopedia of Database Systems, 3026–27. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_860.
Повний текст джерелаLiu, Qianhong, and Peter A. Ng. "Query Transformation." In Document Processing and Retrieval, 201–18. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1295-6_7.
Повний текст джерелаBöhlen, Michael H. "Temporal Query Processing." In Encyclopedia of Database Systems, 1–4. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_408-2.
Повний текст джерелаLiu, Qing. "Approximate Query Processing." In Encyclopedia of Database Systems, 1–7. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_534-2.
Повний текст джерелаSattler, Kai-Uwe. "Distributed Query Processing." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_704-2.
Повний текст джерелаТези доповідей конференцій з теми "Query processing and optimisation"
Ul Ain Ali, Qurat. "Heterogeneous Model Query Optimisation." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00104.
Повний текст джерелаHarangsri, Banchong, John Shepherd, and Anne Ngu. "Query optimisation in multidatabase systems using query classification." In the 1996 ACM symposium. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/331119.331170.
Повний текст джерелаHartmann, Sven, and Sebastian Link. "XML Query Optimisation: Specify your Selectivity." In 18th International Conference on Database and Expert Systems Applications (DEXA 2007). IEEE, 2007. http://dx.doi.org/10.1109/dexa.2007.19.
Повний текст джерелаHartmann, Sven, and Sebastian Link. "XML Query Optimisation: Specify your Selectivity." In 18th International Conference on Database and Expert Systems Applications (DEXA 2007). IEEE, 2007. http://dx.doi.org/10.1109/dexa.2007.4312851.
Повний текст джерелаTsialiamanis, Petros, Lefteris Sidirourgos, Irini Fundulaki, Vassilis Christophides, and Peter Boncz. "Heuristics-based query optimisation for SPARQL." In the 15th International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2247596.2247635.
Повний текст джерелаTamine, L., and M. Boughanem. "Query optimisation using an improved genetic algorithm." In the ninth international conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/354756.354842.
Повний текст джерелаCornacchia, Roberto, Alex van Ballegooij, and Arjen P. de Vries. "A case study on array query optimisation." In the 1st international workshop. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1039470.1039476.
Повний текст джерелаChawla, Tanvi, Girdhari Singh, and Emmanuel S. Pilli. "A shortest path approach to SPARQL chain query optimisation." In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2017. http://dx.doi.org/10.1109/icacci.2017.8126102.
Повний текст джерелаJindal, Vandana, and Anil Kumar Verma. "Query Processing." In International Conference on Computer Applications — Database Systems. Singapore: Research Publishing Services, 2010. http://dx.doi.org/10.3850/978-981-08-7300-4_1662.
Повний текст джерелаde Moor, Oege, Damien Sereni, Pavel Avgustinov, and Mathieu Verbaere. "Type inference for datalog and its application to query optimisation." In the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1376916.1376957.
Повний текст джерелаЗвіти організацій з теми "Query processing and optimisation"
Liu, Jane. Monotone Approximate Query Processing. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada267153.
Повний текст джерелаArmbrust, Michael P. Scale-Independent Relational Query Processing. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada597352.
Повний текст джерелаWu, Kesheng, Ekow Otoo, and Arie Shoshani. Compressed bitmap indices for efficient query processing. Office of Scientific and Technical Information (OSTI), September 2001. http://dx.doi.org/10.2172/808915.
Повний текст джерелаKavraki, Lydia, Jean-Claude Latombe, Rajeew Motwani, and P. Raghavan. Randomized Query Processing in Robot Motion Planning. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada326821.
Повний текст джерелаRotem, Doron, Kurt Stockinger, and Kesheng Wu. Towards Optimal Multi-Dimensional Query Processing with BitmapIndices. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/881846.
Повний текст джерелаSAMATOVA, Nagiza Faridovna. In Situ Indexing and Query Processing of AMR Data. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1502394.
Повний текст джерелаPerich, Filip, Jeffrey Undercoffer, Lalana Kagal, Anupam Joshi, Timothy Finin, and Yelena Yesha. In Reputation We Believe: Query Processing in Mobile Ad-Hoc Networks. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439635.
Повний текст джерелаChen, Yanpei, Sara Alspaugh, and Randy H. Katz. Interactive Query Processing in Big Data Systems: A Cross Industry Study of MapReduce Workloads. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada561769.
Повний текст джерелаFurey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41960.
Повний текст джерела