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Journal articles on the topic 'Query Executor'

Author: Grafiati

Published: 6 September 2023

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1

Huang, Silu, Erkang Zhu, Surajit Chaudhuri, and Leonhard Spiegelberg. "T-Rex: Optimizing Pattern Search on Time Series." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–26. http://dx.doi.org/10.1145/3589275.

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Pattern search is an important class of queries for time series data. Time series patterns often match variable-length segments with a large search space, thereby posing a significant performance challenge. The existing pattern search systems, for example, SQL query engines supporting MATCH_RECOGNIZE, are ineffective in pruning the large search space of variable-length segments. In many cases, the issue is due to the use of a restrictive query language modeled on time series points and a computational model that limits search space pruning. We built T-ReX to address this problem using two main

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Yogatama, Bobbi W., Weiwei Gong, and Xiangyao Yu. "Orchestrating data placement and query execution in heterogeneous CPU-GPU DBMS." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2491–503. http://dx.doi.org/10.14778/3551793.3551809.

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There has been a growing interest in using GPU to accelerate data analytics due to its massive parallelism and high memory bandwidth. The main constraint of using GPU for data analytics is the limited capacity of GPU memory. Heterogeneous CPU-GPU query execution is a compelling approach to mitigate the limited GPU memory capacity and PCIe bandwidth. However, the design space of heterogeneous CPU-GPU query execution has not been fully explored. We aim to improve state-of-the-art CPU-GPU data analytics engine by optimizing data placement and heterogeneous query execution. First, we introduce a s

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Barish, G., and C. A. Knoblock. "An Expressive Language and Efficient Execution System for Software Agents." Journal of Artificial Intelligence Research 23 (June 1, 2005): 625–66. http://dx.doi.org/10.1613/jair.1548.

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Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution

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Yang, Yifei, Matt Youill, Matthew Woicik, et al. "FlexPushdownDB." Proceedings of the VLDB Endowment 14, no. 11 (2021): 2101–13. http://dx.doi.org/10.14778/3476249.3476265.

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Modern cloud databases adopt a storage-disaggregation architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. Two solutions have been explored to mitigate the bottleneck: caching and computation pushdown. While both techniques can significantly reduce network traffic, existing DBMSs consider them as orthogonal techniques and support only one or the other, leaving potential performance benefits unexploited. In this paper we present FlexPushdownDB (FPDB) , an OLAP cloud DBMS pr

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DAS, ARIYAM, and CARLO ZANIOLO. "A Case for Stale Synchronous Distributed Model for Declarative Recursive Computation." Theory and Practice of Logic Programming 19, no. 5-6 (2019): 1056–72. http://dx.doi.org/10.1017/s1471068419000358.

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AbstractA large class of traditional graph and data mining algorithms can be concisely expressed in Datalog, and other Logic-based languages, once aggregates are allowed in recursion. In fact, for most BigData algorithms, the difficult semantic issues raised by the use of non-monotonic aggregates in recursion are solved byPre-Mappability(${\cal P}$reM), a property that assures that for a program with aggregates in recursion there is an equivalent aggregate-stratified program. In this paper we show that, by bringing together the formal abstract semantics of stratified programs with the efficien

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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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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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Sen, Rathijit, Abhishek Roy, Alekh Jindal, et al. "AutoExecutor." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2855–58. http://dx.doi.org/10.14778/3476311.3476362.

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Right-sizing resources for query execution is important for cost-efficient performance, but estimating how performance is affected by resource allocations, upfront, before query execution is difficult. We demonstrate AutoExecutor , a predictive system that uses machine learning models to predict query run times as a function of the number of allocated executors, that limits the maximum allowed parallelism, for Spark SQL queries running on Azure Synapse.

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Beedkar, Kaustubh, David Brekardin, Jorge-Anulfo Quiané-Ruiz, and Volker Markl. "Compliant geo-distributed data processing in action." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2843–46. http://dx.doi.org/10.14778/3476311.3476359.

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In this paper we present our work on compliant geo-distributed data processing. Our work focuses on the new dimension of dataflow constraints that regulate the movement of data across geographical or institutional borders. For example, European directives may regulate transferring only certain information fields (such as non personal information) or aggregated data. Thus, it is crucial for distributed data processing frameworks to consider compliance with respect to dataflow constraints derived from these regulations. We have developed a compliance-based data processing framework, which (i) al

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Azhir, Elham, Mehdi Hosseinzadeh, Faheem Khan, and Amir Mosavi. "Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark." Mathematics 10, no. 19 (2022): 3517. http://dx.doi.org/10.3390/math10193517.

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Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. However, traditional clustering algorithms take a significant amount of execution time for clustering such large datasets. The MapReduce distributed computing model provides efficient solutions for storing and processing vast quantities of data. Apache Spark and Apache Hadoop frameworks are used in the present investigation to cluster different sizes of query datasets in the Map

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Bagui, Sikha, and Evorell Fridge. "Estimating Query Timings in Elasticsearch." Transactions on Networks and Communications 9, no. 2 (2021): 15–36. http://dx.doi.org/10.14738/tnc.92.9887.

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In a shared Elasticsearch environment it can be useful to know how long a particular query will take to execute. This information can be used to enforce rate limiting or distribute requests equitably among multiple clusters. Elasticsearch uses multiple Lucene instances on multiple hosts as an underlying search engine implementation, but this abstraction makes it difficult to predict execution with previously known predictors such as the number of postings. This research investigates the ability of different pre-retrieval statistics, available through Elasticsearch, to accurately predict the ex

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Azhir, Elham, Nima Jafari Navimipour, Mehdi Hosseinzadeh, Arash Sharifi, and Aso Darwesh. "A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method." PeerJ Computer Science 7 (June 1, 2021): e580. http://dx.doi.org/10.7717/peerj-cs.580.

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Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this techni

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Jain, Ms Nisha, and Dr Preeti Tiwari. "MULTI-JOIN-ORDERING QUERY OPTIMIZATION ALGORITHM FOR HIVE WAREHOUSE WITH MAPREDUCE." oorja 19, no. 1 (2021): 94–102. http://dx.doi.org/10.55399/hssg6334.

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According to the Digital Report of July, 2021, Billions of users around the world uses Mobile Phones, Internet, social media every second. This huge range of heterogeneous digital data is called Big Data, and is measured in terms of terabytes or petabytes. It is difficult to the conventional relational databases to handle these heterogeneous data for data analytics, but is still in use significantly in the growth of Big Data. To handle SQL-based structured queries, Hadoop is one of the prominent and well-suited solution that allows Big Data to be stored and processed. Hive support SQL queries

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Trummer, Immanuel, Junxiong Wang, Ziyun Wei, et al. "SkinnerDB: Regret-bounded Query Evaluation via Reinforcement Learning." ACM Transactions on Database Systems 46, no. 3 (2021): 1–45. http://dx.doi.org/10.1145/3464389.

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SkinnerDB uses reinforcement learning for reliable join ordering, exploiting an adaptive processing engine with specialized join algorithms and data structures. It maintains no data statistics and uses no cost or cardinality models. Also, it uses no training workloads nor does it try to link the current query to seemingly similar queries in the past. Instead, it uses reinforcement learning to learn optimal join orders from scratch during the execution of the current query. To that purpose, it divides the execution of a query into many small time slices. Different join orders are tried in diffe

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Vander Sande, Miel, Ruben Verborgh, Anastasia Dimou, Pieter Colpaert, and Erik Mannens. "Hypermedia-Based Discovery for Source Selection Using Low-Cost Linked Data Interfaces." International Journal on Semantic Web and Information Systems 12, no. 3 (2016): 79–110. http://dx.doi.org/10.4018/ijswis.2016070103.

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Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed—even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern

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Gounaris, Anastasios, Norman W. Paton, Alvaro A. A. Fernandes, and Rizos Sakellariou. "Self-monitoring query execution for adaptive query processing." Data & Knowledge Engineering 51, no. 3 (2004): 325–48. http://dx.doi.org/10.1016/j.datak.2004.05.002.

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He, Zhenzhen, Jiong Yu, and Binglei Guo. "Execution Time Prediction for Cypher Queries in the Neo4j Database Using a Learning Approach." Symmetry 14, no. 1 (2022): 55. http://dx.doi.org/10.3390/sym14010055.

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With database management systems becoming complex, predicting the execution time of graph queries before they are executed is one of the challenges for query scheduling, workload management, resource allocation, and progress monitoring. Through the comparison of query performance prediction methods, existing research works have solved such problems in traditional SQL queries, but they cannot be directly applied in Cypher queries on the Neo4j database. Additionally, most query performance prediction methods focus on measuring the relationship between correlation coefficients and retrieval perfo

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Vengadeswaran, S., and S. R. Balasundaram. "An Optimal Data Placement Strategy for Improving System Performance of Massive Data Applications Using Graph Clustering." International Journal of Ambient Computing and Intelligence 9, no. 3 (2018): 15–30. http://dx.doi.org/10.4018/ijaci.2018070102.

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This article describes how the time taken to execute a query and return the results, increase exponentially as the data size increases, leading to more waiting times of the user. Hadoop with its distributed processing capability is considered as an efficient solution for processing such large data. Hadoop's Default Data Placement Strategy (HDDPS) allocates the data blocks randomly across the cluster of nodes without considering any of the execution parameters. This result in non-availability of the blocks required for execution in local machine so that the data has to be transferred across the

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Allenstein, Brett, Andrew Yost, Paul Wagner, and Joline Morrison. "A query simulation system to illustrate database query execution." ACM SIGCSE Bulletin 40, no. 1 (2008): 493–97. http://dx.doi.org/10.1145/1352322.1352301.

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Payton, Jamie, Christine Julien, Vasanth Rajamani, and Gruia-Catalin Roman. "Using snapshot query fidelity to adapt continuous query execution." Pervasive and Mobile Computing 8, no. 3 (2012): 317–30. http://dx.doi.org/10.1016/j.pmcj.2012.02.005.

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Ganguly, Sumit, Waqar Hasan, and Ravi Krishnamurthy. "Query optimization for parallel execution." ACM SIGMOD Record 21, no. 2 (1992): 9–18. http://dx.doi.org/10.1145/141484.130291.

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tyagi, Geetanjali. "Ontology Based Fuzzy Query Execution." American Journal of Networks and Communications 4, no. 3 (2015): 16. http://dx.doi.org/10.11648/j.ajnc.s.2015040301.14.

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Barkhordari, Mohammadhossein, and Mahdi Niamanesh. "Aras." International Journal of Distributed Systems and Technologies 8, no. 2 (2017): 47–60. http://dx.doi.org/10.4018/ijdst.2017040104.

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Because of to the high rate of data growth and the need for data analysis, data warehouse management for big data is an important issue. Single node solutions cannot manage the large amount of information. Information must be distributed over multiple hardware nodes. Nevertheless, data distribution over nodes causes each node to need data from other nodes to execute a query. Data exchange among nodes creates problems, such as the joins between data segments that exist on different nodes, network congestion, and hardware node wait for data reception. In this paper, the Aras method is proposed.

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Zhai, Hong Yu, Li Li, and Hong Hua Xu. "The Design of Query Processing in Data Stream Management System." Advanced Materials Research 952 (May 2014): 351–54. http://dx.doi.org/10.4028/www.scientific.net/amr.952.351.

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data stream management system is used to manage and query coming large, continuous, fast and flexible data stream. The system is based on the flow of data extraction, transformation, combination, which is the main content and task query execution. This paper mainly discusses the design and implementation of query execution module and query execution is composed of two parts which include query operations, query execution and scheduling.

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Fent, Philipp, and Thomas Neumann. "A practical approach to groupjoin and nested aggregates." Proceedings of the VLDB Endowment 14, no. 11 (2021): 2383–96. http://dx.doi.org/10.14778/3476249.3476288.

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Groupjoins, the combined execution of a join and a subsequent group by, are common in analytical queries, and occur in about 1/8 of the queries in TPC-H and TPC-DS. While they were originally invented to improve performance, efficient parallel execution of groupjoins can be limited by contention, which limits their usefulness in a many-core system. Having an efficient implementation of groupjoins is highly desirable, as groupjoins are not only used to fuse group by and join but are also introduced by the unnesting component of the query optimizer to avoid nested-loops evaluation of aggregates.

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Arasu, Arvind, Shivnath Babu, and Jennifer Widom. "The CQL continuous query language: semantic foundations and query execution." VLDB Journal 15, no. 2 (2005): 121–42. http://dx.doi.org/10.1007/s00778-004-0147-z.

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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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Schiavio, Filippo, Daniele Bonetta, and Walter Binder. "Language-agnostic integrated queries in a managed polyglot runtime." Proceedings of the VLDB Endowment 14, no. 8 (2021): 1414–26. http://dx.doi.org/10.14778/3457390.3457405.

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Language-integrated query (LINQ) frameworks offer a convenient programming abstraction for processing in-memory collections of data, allowing developers to concisely express declarative queries using general-purpose programming languages. Existing LINQ frameworks rely on the well-defined type system of statically-typed languages such as C # or Java to perform query compilation and execution. As a consequence of this design, they do not support dynamic languages such as Python, R, or JavaScript. Such languages are however very popular among data scientists, who would certainly benefit from LINQ

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

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Query optimization is an important aspect in designing database management systems, aimed to find an optimal query execution plan so that overall time of query execution is minimized. Multi join query ordering (MJQO) is an integral part of query optimizer. This paper aims to propose a solution for MJQO problem, which is an NP complete problem. This paper proposes a heuristic based algorithm as a solution of MJQO problem. The proposed algorithm is a combination of two basic search algorithms, cuckoo and tabu search. Simulation shows some exciting results in favour of the proposed algorithm and

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Nasibullin, Arsen. "Fault Tolerant Hash Join for Distributed Systems." Computer tools in education, no. 4 (December 28, 2022): 68–82. http://dx.doi.org/10.32603/2071-2340-2022-4-68-82.

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Nowadays, enterprises are inclined to deploy data processing and analytical applications from well-equipped mainframes with highly available hardware components to commodity computers. Commodity machines are less reliable than expensive mainframes. Applications deployed on commodity clusters have to deal with failures that occur frequently. Mostly, these applications perform complex client queries with aggregation and join operations. The longer a query executes, the more it suffers from failures. It causes the entire work has to be re-executed. This paper presents a fault tolerant hash join (

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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 memor

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Wu, Wentao, Xi Wu, Hakan Hacigümüş, and Jeffrey F. Naughton. "Uncertainty aware query execution time prediction." Proceedings of the VLDB Endowment 7, no. 14 (2014): 1857–68. http://dx.doi.org/10.14778/2733085.2733092.

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Paton, Norman W., Marcelo A. T. de Aragão, and Alvaro A. A. A. Fernandes. "Utility-driven adaptive query workload execution." Future Generation Computer Systems 28, no. 7 (2012): 1070–79. http://dx.doi.org/10.1016/j.future.2011.08.014.

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Baby, Tinu, and Aswani Kumar Cherukuri. "On query execution over encrypted data." Security and Communication Networks 8, no. 2 (2014): 321–31. http://dx.doi.org/10.1002/sec.982.

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Sarika Prakash, Kale, and P. M. Joe Prathap. "Evaluating Aggregate Functions of Iceberg Query Using Priority Based Bitmap Indexing Strategy." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3745. http://dx.doi.org/10.11591/ijece.v7i6.pp3745-3752.

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Aggregate function and iceberg queries are important and common in many applications of data warehouse because users are generally interested in looking for variance or unusual patterns. Normally, the nature of the queries to be executed on data warehouse are the queries with aggregate function followed by having clause, these type of queries are known as iceberg query. Especially to have efficient techniques for processing aggregate function of iceberg query is very important because their processing cost is much higher than that of the other basic relational operations such as SELECT and PRO

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Kabra, Navin, and David J. DeWitt. "Efficient mid-query re-optimization of sub-optimal query execution plans." ACM SIGMOD Record 27, no. 2 (1998): 106–17. http://dx.doi.org/10.1145/276305.276315.

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Bai, Samita, and Shakeel A. Khoja. "Hybrid Query Execution on Linked Data With Complete Results." International Journal on Semantic Web and Information Systems 17, no. 1 (2021): 25–49. http://dx.doi.org/10.4018/ijswis.2021010102.

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The link traversal strategies to query Linked Data over WWW can retrieve up-to-date results using a recursive URI lookup process in real-time. The downside of this approach comes with the query patterns having subject unbound (i.e. ?S rdf:type:Class). Such queries fail to start up the traversal process as the RDF pages are subject-centric in nature. Thus, zero-knowledge link traversal leads to the empty query results for these queries. In this paper, the authors analyze a large corpus of real-world SPARQL query logs and identify the Most Frequent Predicates (MFPs) occurring in these queries. T

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He, Dong, Supun C. Nakandala, Dalitso Banda, et al. "Query processing on tensor computation runtimes." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2811–25. http://dx.doi.org/10.14778/3551793.3551833.

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The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the number of specialized hardware devices, which are now offered by major cloud vendors. By hiding the low-level complexity through a tensor-based interface, tensor computation runtimes (TCRs) such as PyTorch allow data scientists to efficiently exploit the exciting capabilities offered by the new hardware. In this paper, we explore how database management systems can ride the wave of innovation happening in the AI space. We

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Asswad, Mohammad Mourhaf AL, Sergio de Cesare, and Mark Lycett. "A Query-based Approach for Semi-Automatic Annotation of Web Services." International Journal of Information Systems and Social Change 2, no. 2 (2011): 37–54. http://dx.doi.org/10.4018/jissc.2011040103.

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Semantic Web services (SWS) have attracted increasing attention due to their potential to automate discovery and composition of current syntactic Web services. An issue that prevents a wider adoption of SWS relates to the manual nature of the semantic annotation task. Manual annotation is a difficult, error-prone, and time-consuming process and automating the process is highly desirable. Though some approaches have been proposed to semi-automate the annotation task, they are difficult to use and cannot perform accurate annotation for the following reasons: (1) They require building application

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LIU, LING, CALTON PU, and KIRILL RICHINE. "DISTRIBUTED QUERY SCHEDULING SERVICE: AN ARCHITECTURE AND ITS IMPLEMENTATION." International Journal of Cooperative Information Systems 07, no. 02n03 (1998): 123–66. http://dx.doi.org/10.1142/s0218843098000088.

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We present the systematic design and development of a distributed query scheduling service (DQS) in the context of DIOM, a distributed and interoperable query mediation system.26 DQS consists of an extensible architecture for distributed query processing, a three-phase optimization algorithm for generating efficient query execution schedules, and a prototype implementation. Functionally, two important execution models of distributed queries, namely moving query to data or moving data to query, are supported and combined into a unified framework, allowing the data sources with limited search an

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Ghosh, Dhrubajyoti, Peeyush Gupta, Sharad Mehrotra, Roberto Yus, and Yasser Altowim. "JENNER." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2666–78. http://dx.doi.org/10.14778/3551793.3551822.

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Emerging domains, such as sensor-driven smart spaces and social media analytics, require incoming data to be enriched prior to its use. Enrichment often consists of machine learning (ML) functions that are too expensive/infeasible to execute at ingestion. We develop a strategy entitled Just-in-time ENrichmeNt in quERy Processing (JENNER) to support interactive analytics over data as soon as it arrives for such application context. JENNER exploits the inherent tradeoffs of cost and quality often displayed by the ML functions to progressively improve query answers during query execution. We desc

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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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Lim, Jongtae, Byounghoon Kim, Hyeonbyeong Lee, Dojin Choi, Kyoungsoo Bok, and Jaesoo Yoo. "An Efficient Distributed SPARQL Query Processing Scheme Considering Communication Costs in Spark Environments." Applied Sciences 12, no. 1 (2021): 122. http://dx.doi.org/10.3390/app12010122.

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Various distributed processing schemes were studied to efficiently utilize a large scale of RDF graph in semantic web services. This paper proposes a new distributed SPARQL query processing scheme considering communication costs in Spark environments to reduce I/O costs during SPARQL query processing. We divide a SPARQL query into several subqueries using a WHERE clause to process a query of an RDF graph stored in a distributed environment. The proposed scheme reduces data communication costs by grouping the divided subqueries in related nodes through the index and processing them, and the gro

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Wei, Ziyun, and Immanuel Trummer. "SkinnerMT." Proceedings of the VLDB Endowment 16, no. 4 (2022): 905–17. http://dx.doi.org/10.14778/3574245.3574272.

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SkinnerMT is an adaptive query processing engine, specialized for multi-core platforms. SkinnerMT features different strategies for parallel processing that allow users to trade between average run time and performance robustness. First, SkinnerMT supports execution strategies that execute multiple query plans in parallel, thereby reducing the risk to find near-optimal plans late and improving robustness. Second, SkinnerMT supports data-parallel processing strategies. Its parallel multi-way join algorithm is sensitive to the assignment from tuples to threads. Here, SkinnerMT uses a cost-based

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Deepa, S. "A Query Optimization Framework for Fuzzy Relational Databases." Asian Journal of Engineering and Applied Technology 1, no. 1 (2012): 43–46. http://dx.doi.org/10.51983/ajeat-2012.1.1.2502.

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Ever since the development of relational model, relational database systems have been extensively studied and several commercial relational database systems are currently available. Relational model usually take care of only well defined data. In order to capture more meaning to the data an extension of the classical relational model called fuzzy relational model was proposed. The key reasons for the success of relational database lies in the power of declarative languages and execution strategies used in query optimization. Estimating the cost of fuzzy query based on system catalog introduces

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Аль Мусави, О. А. Р., and Е. Е. Красновский. "EXPERIMENTAL STUDY OF THE EFFECTIVENESS OF ALGORITHMS FOR REPEATED QUERY OPTIMIZATION IN CLOUD DATABASES BASED ON COMPUTER TRAINING." СИСТЕМЫ УПРАВЛЕНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ, no. 3(89) (September 30, 2022): 29–34. http://dx.doi.org/10.36622/vstu.2022.89.3.007.

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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. 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 целями о

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Hidayat, Kukuh Triyuliarno, Riza Arifudin, and Alamsyah Alamsyah. "Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time." Scientific Journal of Informatics 5, no. 1 (2018): 27. http://dx.doi.org/10.15294/sji.v5i1.12720.

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The relational database is defined as the database by connecting between tables. Each table has a collection of information. The information is processed in the database by using queries, such as data retrieval, data storage, and data conversion. If the information in the table or data has a large size, then the query process to process the database becomes slow. In this paper, Genetic Algorithm is used to process queries in order to optimize and reduce query execution time. The results obtained are query execution with genetic algorithm optimization to show the best execution time. The geneti

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Yong Zhang, a., Cunhua Li, et al. "Optimized query execution in E-commerce sites." International Journal of Digital Content Technology and its Applications 6, no. 20 (2012): 286–95. http://dx.doi.org/10.4156/jdcta.vol6.issue20.31.

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Singh, Rashmi, Somesh Sharma, Sourabh Singh, and Bhawna Singh. "Reducing Run-time Execution in Query Optimization." International Journal of Computer Applications 96, no. 6 (2014): 1–6. http://dx.doi.org/10.5120/16795-6505.

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Wolf, Florian, Michael Brendle, Norman May, Paul R. Willems, Kai-Uwe Sattler, and Michael Grossniklaus. "Robustness metrics for relational query execution plans." Proceedings of the VLDB Endowment 11, no. 11 (2018): 1360–72. http://dx.doi.org/10.14778/3236187.3236191.

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