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Journal articles on the topic 'Query processing and optimisation'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

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.

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2

Akili, 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.

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Complex event processing (CEP) evaluates queries over streams of event data to detect situations of interest. If the event data are produced by geographically distributed sources, CEP may exploit in-network processing that distributes the evaluation of a query among the nodes of a network. To this end, a query is modularized and individual query operators are assigned to nodes, especially those that act as data sources. Existing solutions for such operator placement, however, are limited in that they assume all query results to be gathered at one designated node, commonly referred to as a sink. Hence, existing techniques postulate a hierarchical structure of the network that generates and processes the event data. This largely neglects the optimisation potential that stems from truly decentralised query evaluation with potentially many sinks. To address this gap, in this paper, we propose Multi-Sink Evaluation (MuSE) graphs as a formal computational model to evaluate common CEP queries in a decentralised manner. We further prove the completeness of query evaluation under this model. Striving for distributed CEP that can scale to large volumes of high-frequency event streams, we show how to reason on the network costs induced by distributed query evaluation and prune inefficient query execution plans. As such, our work lays the foundation for distributed CEP that is both, sound and efficient.

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3

Kumar 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.

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4

A, 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.

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5

Chun, 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.

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To satisfy a user’s complex requirements, Resource Description Framework (RDF) Stream Processing (RSP) systems envision the fusion of remote RDF data with semantic streams, using common data models to query semantic streams continuously. While streaming data are changing at a high rate and are pushed into RSP systems, the remote RDF data are retrieved from different remote sources. With the growth of SPARQL endpoints that provide access to remote RDF data, RSP systems can easily integrate the remote data with streams. Such integration provides new opportunities for mixing static (or quasi-static) data with streams on a large scale. However, the current RSP systems do not offer any optimisation for the integration. In this article, we present an adaptive plan-based approach to efficiently integrate sematic streams with the static data from a remote source. We create a query execution plan based on temporal constraints among constituent services for the timely acquisition of remote data. To predict the change of remote sources in real time, we propose an adaptive process of detecting a source update, forecasting the update in the future, deciding a new plan to obtain remote data and reacting to a new plan. We extend a SPARQL query with operators for describing the multiple strategies of the proposed adaptive process. Experimental results show that our approach is more efficient than the conventional RSP systems in distributed settings.

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6

Haryanto, 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.

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7

Deshpande, 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.

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8

WEI, 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.

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9

Tang, 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.

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10

Haritsa, 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.

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11

Seshadri, Praveen, Miron Livny, and Raghu Ramakrishnan. "Sequence query processing." ACM SIGMOD Record 23, no. 2 (June 1994): 430–41. http://dx.doi.org/10.1145/191843.191926.

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12

Jiang, Dawei, Qingchao Cai, Gang Chen, H. V. Jagadish, Beng Chin Ooi, Kian-Lee Tan, and Anthony K. H. Tung. "Cohort query processing." Proceedings of the VLDB Endowment 10, no. 1 (September 2016): 1–12. http://dx.doi.org/10.14778/3015270.3015271.

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13

Haas, Peter J., and Joseph M. Hellerstein. "Online query processing." ACM SIGMOD Record 30, no. 2 (June 2001): 623. http://dx.doi.org/10.1145/376284.375800.

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14

Gupta, Siddharth, and Narina Thakur. "Semantic Query Optimisation with Ontology Simulation." International journal of Web & Semantic Technology 1, no. 4 (October 30, 2010): 1–10. http://dx.doi.org/10.5121/ijwest.2010.1401.

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15

Bagui, Sikha, and Keerthi Devulapalli. "Comparison of Hive's query optimisation techniques." International Journal of Big Data Intelligence 5, no. 4 (2018): 243. http://dx.doi.org/10.1504/ijbdi.2018.094993.

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16

Devulapalli, Keerthi, and Sikha Bagui. "Comparison of Hive's query optimisation techniques." International Journal of Big Data Intelligence 5, no. 4 (2018): 243. http://dx.doi.org/10.1504/ijbdi.2018.10009546.

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17

Morvan, Franck, and Abdelkader Hameurlain. "Dynamic query optimisation: towards decentralised methods." International Journal of Intelligent Information and Database Systems 3, no. 4 (2009): 461. http://dx.doi.org/10.1504/ijiids.2009.030440.

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18

EGENHOFER, MAX J. "Query Processing in Spatial-Query-by-Sketch." Journal of Visual Languages & Computing 8, no. 4 (August 1997): 403–24. http://dx.doi.org/10.1006/jvlc.1997.0054.

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19

Kumar, Deepak, Sushil Kumar, and Rohit Bansal. "Multi-objective multi-join query optimisation using modified grey wolf optimisation." International Journal of Advanced Intelligence Paradigms 17, no. 1/2 (2020): 67. http://dx.doi.org/10.1504/ijaip.2020.108760.

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20

Hamdi, Issam, Emna Bouazizi, and Jamel Feki. "Query optimisation in real-time data warehouses." International Journal of Intelligent Information and Database Systems 12, no. 4 (2019): 245. http://dx.doi.org/10.1504/ijiids.2019.10026243.

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21

Hamdi, Issam, Emna Bouazizi, and Jamel Feki. "Query optimisation in real-time data warehouses." International Journal of Intelligent Information and Database Systems 12, no. 4 (2019): 245. http://dx.doi.org/10.1504/ijiids.2019.104534.

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22

Sayli, Ayla, and Armagan Elibol. "Rule Evaluation Algorithm for Semantic Query Optimisation." Applied Mathematics & Information Sciences 7, no. 5 (September 1, 2013): 1773–81. http://dx.doi.org/10.12785/amis/070515.

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23

Poulovassilis, Alexandra, and Carol Small. "Algebraic query optimisation for database programming languages." VLDB Journal The International Journal on Very Large Data Bases 5, no. 2 (April 1, 1996): 119–32. http://dx.doi.org/10.1007/s007780050019.

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24

Shanker Prasad, Bhanu. "CORRELATIONS AND QUERY PROCESSING." International Journal of Advanced Research 8, no. 9 (September 30, 2020): 811–16. http://dx.doi.org/10.21474/ijar01/11726.

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It is known that optimization of join queries based on average selectivities is sub-optimal in highly correlated databases. Relations are naturally divided into partitions , each partition having substantially different statistical characteristics in such databases. It is very compelling to discover such data partitions during query optimization and create multiple plans for a given query , one plan being optimal for a particular combination of data partitions. This scenario calls for the sharing of state among plans, so that common intermediate results are not recomputed. We study this problem in a setting with a routing-based query execution engine based on eddies. Eddies naturally encapsulate horizontal partitioning and maximal state sharing across multiple plan. The purpose of this paper is to present faster execution time over traditional optimization for high correlations, while maintaining the same performance for low correlations.

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25

Leeuwen, Wilco v., Thomas Mulder, Bram van de Wall, George Fletcher, and Nikolay Yakovets. "AvantGraph query processing engine." Proceedings of the VLDB Endowment 15, no. 12 (August 2022): 3698–701. http://dx.doi.org/10.14778/3554821.3554878.

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We demonstrate AvantGraph, a graph query processing engine developed by the Database group at TU Eindhoven. Designed for efficient processing of both subgraph matching and navigational graph queries, AvantGraph encompasses innovation in three key areas: the planner, the cardinality estimator, and the execution engine. We present demonstration scenarios covering a wide range of workloads across diverse domains which (1) provides deep insights into the core challenges of complex graph query processing and (2) showcases corresponding critical optimizations via "under-the-hood" operational insights of AvantGraph's key components.

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26

Ilarri, Sergio, Eduardo Mena, and Arantza Illarramendi. "Location-dependent query processing." ACM Computing Surveys 42, no. 3 (March 2010): 1–73. http://dx.doi.org/10.1145/1670679.1670682.

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27

Mamoulis, Nikos, Dimitris Papadias, and Dinos Arkoumanis. "Complex Spatial Query Processing." GeoInformatica 8, no. 4 (December 2004): 311–46. http://dx.doi.org/10.1023/b:gein.0000040830.73424.f0.

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28

Chen, Lisi, Gao Cong, Christian S. Jensen, and Dingming Wu. "Spatial keyword query processing." Proceedings of the VLDB Endowment 6, no. 3 (January 2013): 217–28. http://dx.doi.org/10.14778/2535569.2448955.

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29

Bača, Radim, Michal Krátký, Irena Holubová, Martin Nečaský, Tomáš Skopal, Martin Svoboda, and Sherif Sakr. "Structural XML Query Processing." ACM Computing Surveys 50, no. 5 (November 13, 2017): 1–41. http://dx.doi.org/10.1145/3095798.

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30

Li, Kaiyu, Yong Zhang, Guoliang Li, Wenbo Tao, and Ying Yan. "Bounded Approximate Query Processing." IEEE Transactions on Knowledge and Data Engineering 31, no. 12 (December 1, 2019): 2262–76. http://dx.doi.org/10.1109/tkde.2018.2877362.

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31

Qi, Jianzhong, Rui Zhang, Christian S. Jensen, Kotagiri Ramamohanarao, and Jiayuan HE. "Continuous Spatial Query Processing." ACM Computing Surveys 51, no. 3 (July 16, 2018): 1–39. http://dx.doi.org/10.1145/3193835.

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32

Keefe, T. F., M. B. Thuraisingham, and W. T. Tsai. "Secure query-processing strategies." Computer 22, no. 3 (March 1989): 63–70. http://dx.doi.org/10.1109/2.16229.

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33

Imielinski, Tomasz. "Abstraction in query processing." Journal of the ACM 38, no. 3 (July 1991): 533–57. http://dx.doi.org/10.1145/116825.116832.

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34

Kolcun, Roman, David E. Boyle, and Julie A. McCann. "Efficient Distributed Query Processing." IEEE Transactions on Automation Science and Engineering 13, no. 3 (July 2016): 1230–46. http://dx.doi.org/10.1109/tase.2016.2530941.

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35

LEE, Ki-Hoon. "How XPath Query Minimization Impacts Query Processing Performance." IEICE Transactions on Information and Systems E95.D, no. 9 (2012): 2258–64. http://dx.doi.org/10.1587/transinf.e95.d.2258.

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36

Liu, Bao Long, Hua Chen, and Yi Guo. "Encrypted XML Data Query Processing with Range Query." Applied Mechanics and Materials 65 (June 2011): 241–45. http://dx.doi.org/10.4028/www.scientific.net/amm.65.241.

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Without decrypting the encrypted blocks, it is difficult to query encrypted XML data. Inspired by Query-Aware decryption scheme, the paper extends the scheme to support range query with value index. Value index is based on order preserving encryption. The testing results indicate that the proposed scheme can complete a correct query processing, and support range query.

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37

Park, Dong-Joo, Sangwon Park, Tae-Sun Chung, and Sang-Won Lee. "Distance Browsing Query Processing using Query Result Set." KIPS Transactions:PartD 12D, no. 5 (October 1, 2005): 673–82. http://dx.doi.org/10.3745/kipstd.2005.12d.5.673.

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38

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 (December 2004): 325–48. http://dx.doi.org/10.1016/j.datak.2004.05.002.

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39

Chen, F. C. F., and M. H. Dunham. "Common subexpression processing in multiple-query processing." IEEE Transactions on Knowledge and Data Engineering 10, no. 3 (1998): 493–99. http://dx.doi.org/10.1109/69.687980.

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40

Subramaniam, Samini, Su-Cheng Haw, and Lay-Ki Soon. "Improved Centralized XML Query Processing Using Distributed Query Workload." IEEE Access 9 (2021): 29127–42. http://dx.doi.org/10.1109/access.2021.3058383.

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41

Balkir, N. H., G. Ozsoyoglu, and Z. M. Ozsoyoglu. "A graphical query language: VISUAL and its query processing." IEEE Transactions on Knowledge and Data Engineering 14, no. 5 (September 2002): 955–78. http://dx.doi.org/10.1109/tkde.2002.1033767.

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42

Liu, Shuo, and Hassan A. Karimi. "Grid query optimizer to improve query processing in grids." Future Generation Computer Systems 24, no. 5 (May 2008): 342–53. http://dx.doi.org/10.1016/j.future.2007.06.003.

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43

Faiz, Sami, Emna Bouazizi, and Sana Hamdi. "Query optimisation in real-time spatial big data." International Journal of Information and Decision Sciences 12, no. 4 (2020): 348. http://dx.doi.org/10.1504/ijids.2020.10029337.

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44

Hamdi, Sana, Emna Bouazizi, and Sami Faiz. "Query optimisation in real-time spatial big data." International Journal of Information and Decision Sciences 12, no. 4 (2020): 348. http://dx.doi.org/10.1504/ijids.2020.110450.

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45

Sun, W., W. Meng, and C. Yu. "Query Optimisation in Distributed Object-Oriented Database Systems." Computer Journal 35, no. 2 (April 1, 1992): 98–107. http://dx.doi.org/10.1093/comjnl/35.2.98.

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46

Sahal, Radhya, Mohamed H. Khafagy, and Fatma A. Omara. "Big data multi-query optimisation with Apache Flink." International Journal of Web Engineering and Technology 13, no. 1 (2018): 78. http://dx.doi.org/10.1504/ijwet.2018.092401.

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47

Kowalski, Tomasz Marek, and Radosław Adamus. "Optimisation of language-integrated queries by query unnesting." Computer Languages, Systems & Structures 47 (January 2017): 131–50. http://dx.doi.org/10.1016/j.cl.2016.09.002.

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48

Yadav, Monika, and T. V. Vijay Kumar. "Distributed Query Plan Generation using Cuckoo Search Algorithm." International Journal of Energy Optimization and Engineering 6, no. 1 (January 2017): 86–100. http://dx.doi.org/10.4018/ijeoe.2017010105.

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Query processing in distributed databases involves data transmission amongst sites capable of providing answers to a distributed query. For this, a distributed query processing strategy, which generates efficient query processing plans for a given distributed query, needs to be devised. Since in distributed databases, the data is fragmented and replicated at multiple sites, the number of query plans increases exponentially with increase in the number of sites capable of providing answers to a distributed query. As a result, generating efficient query processing plans, from amongst all possible query plans, becomes a complex problem. This distributed query plan generation (DQPG) problem has been addressed using the Cuckoo Search Algorithm (CSA) in this paper. Accordingly, a CSA based DQPG algorithm (DQPGCSA) that aims to generate Top-K query plans having minimum cost of processing a distributed query has been proposed. Experimental based comparison of DQPGCSA with the existing GA based DQPG algorithm shows that the former is able to generate Top-K query plans that have a comparatively lower query processing cost. This, in turn, reduces the query response time resulting in efficient decision making.

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49

BhanuPrakash, N., and E. Kesavulu Reddy. "Query Processing In Text Mining." International Journal of Computer Sciences and Engineering 9, no. 10 (October 31, 2021): 19–23. http://dx.doi.org/10.26438/ijcse/v9i10.1923.

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50

Green, Todd J. "Datalog and Recursive Query Processing." Foundations and Trends® in Databases 5, no. 2 (2012): 105–95. http://dx.doi.org/10.1561/1900000017.

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