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Relevant bibliographies by topics / Performance of KG query processing

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Academic literature on the topic 'Performance of KG query processing'

Author: Grafiati

Published: 20 February 2023

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Journal articles on the topic "Performance of KG query processing"

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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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Liu, C., and C. Yu. "Performance issues in distributed query processing." IEEE Transactions on Parallel and Distributed Systems 4, no. 8 (1993): 889–905. http://dx.doi.org/10.1109/71.238624.

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Franklin, Michael J., Björn Thór Jónsson, and Donald Kossmann. "Performance tradeoffs for client-server query processing." ACM SIGMOD Record 25, no. 2 (1996): 149–60. http://dx.doi.org/10.1145/235968.233328.

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Shanker Prasad, Bhanu. "CORRELATIONS AND QUERY PROCESSING." International Journal of Advanced Research 8, no. 9 (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 proble

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Bok, Park, Lim, and Yoo. "Mobile P2P-Based Skyline Query Processing over Delay-Tolerant Networks." Electronics 8, no. 11 (2019): 1276. http://dx.doi.org/10.3390/electronics8111276.

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Skyline query-processing techniques considering various properties in peer to peer (P2P)-based services have become a recent topic of research. In this paper, we propose a new skyline query-processing scheme to improve the query-processing performance and accuracy in a mobile P2P service over delay-tolerant networks. The proposed scheme collects data on the query object from neighboring nodes and establishes a local skyline through static properties to reduce query-processing costs. To improve the query accuracy in a non-uniform distribution environment, the query-dissemination range is expand

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Novikov, Boris, Alice Pigul, and Anna Yarygina. "A Performance Analysis of Semantic Caching for XML Query Processing." International Journal of Knowledge-Based Organizations 3, no. 4 (2013): 40–60. http://dx.doi.org/10.4018/ijkbo.2013100103.

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Caching is important for any system attempting to achieve high performance. The semantic caching is an approach trying to benefit from the certain knowledge of data semantics. The authors expect that this information might enable reuse of semantically close data rather than exactly equal to cached data in the traditional system. However, the major obstacle for extensive application of semantic caching for any data model or query language is the computational complexity of the query containment problem, which is, in general, undecidable. In this article the authors introduce and compare three a

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Lakshmi, C., and K. Usha Rani. "Improving the performance of query processing using proposed resilient distributed processing technique." International Journal of Intelligent Computing and Cybernetics 14, no. 2 (2021): 158–69. http://dx.doi.org/10.1108/ijicc-10-2020-0157.

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PurposeResilient distributed processing technique (RDPT), in which mapper and reducer are simplified with the Spark contexts and support distributed parallel query processing.Design/methodology/approachThe proposed work is implemented with Pig Latin with Spark contexts to develop query processing in a distributed environment.FindingsQuery processing in Hadoop influences the distributed processing with the MapReduce model. MapReduce caters to the works on different nodes with the implementation of complex mappers and reducers. Its results are valid for some extent size of the data.Originality/v

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Allam, Tahani M. "Estimate the Performance of Cloudera Decision Support Queries." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 01 (2022): 127–38. http://dx.doi.org/10.3991/ijoe.v18i01.27877.

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Hive and Impala queries are used to process a big amount of data. The overwriting amount of information requires an efficient data processing system. When we deal with a long-term batch query and analysis Hive will be more suitable for this query. Impala is the most powerful system suitable for real-time interactive Structured Query Language (SQL) query which are added a massive parallel processing to Hadoop distributed cluster. The data growth makes a problem with SQL Cluster because the execution processing time is increased. In this paper, a comparison is demonstrated between the performanc

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

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

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H, Rajendra. "Query Processing on Encrypted Database A Performance Evaluation." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 4 (2015): 2383–88. http://dx.doi.org/10.17762/ijritcc2321-8169.1504131.

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Dissertations / Theses on the topic "Performance of KG query processing"

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Salehpour, Masoud. "High-performance Query Processing over Knowledge Graphs." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28569.

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The label “Knowledge Graph” (KG) has been used in the literature for over four decades, typically to refer to a collection of information about real-world entities and their inter-relationships. The proliferation of KGs in recent times opens up exciting opportunities for a broad range of semantic applications such as recommendations. However, unlocking the full potential of KGs in response to the growing deployment requires data platforms to efficiently store and process the content to support various applications. What began with extensions of relational database systems to store the conte

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Qian, Shi-Min. "Performance evaluation of query processing on network of workstations." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0015/MQ48440.pdf.

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Gibas, Michael A. "Improving Query Performance through Application-Driven Processing and Retrieval." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218470693.

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Qian, Shi-Min Carleton University Dissertation Computer Science. "Performance evaluation of query processing on network of workstations." Ottawa, 1999.

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Ryeng, Norvald. "Improving Query Processing Performance in Large Distributed Database Management Systems." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14695.

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The dream of computing power as readily available as the electricity in a wall socket is coming closer to reality with the arrival of grid and cloud computing. At the sametime, databases grow to sizes beyond what can be efficiently managed by single server systems. There is a need for efficient distributed database management systems (DBMSs). Current distributed DBMSs are not built to scale to more than tensor hundreds of sites (i.e., nodes or computers). Users of grid and cloud computingexpect not only almost innite scalability, i.e., at least to thousands of sites, but alsothat the scale is

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Ji, Yuanzhen. "Handling Tradeoffs between Performance and Query-Result Quality in Data Stream Processing." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-233997.

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Data streams in the form of potentially unbounded sequences of tuples arise naturally in a large variety of domains including finance markets, sensor networks, social media, and network traffic management. The increasing number of applications that require processing data streams with high throughput and low latency have promoted the development of data stream processing systems (DSPS). A DSPS processes data streams with continuous queries, which are issued once and return query results to users continuously as new tuples arrive. For stream-based applications, both the query-execution perform

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Saeed, Ifrah. "A portable relational algebra library for high performance data-intensive query processing." Thesis, Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51967.

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A growing number of industries are turning to data warehousing applications such as forecasting and risk assessment to process large volumes of data. These data warehousing applications, which utilize queries comprised of a mix of arithmetic and relational algebra (RA) operators, currently run on systems that utilize commodity multi-core CPUs. If we acknowledge the data-intensive nature of these applications, general purpose graphics processing units (GPUs) with high throughput and memory bandwidth seem to be natural candidates to host these applications. However, since such relational queries

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Raghavan, Venkatesh. "VAMANA -- A high performance, scalable and cost driven XPath engine." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0505104-185545/.

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Kalakuntla, Preetham. "Performance Analysis of kNN Query Processing on large datasets using CUDA & Pthreads : comparing between CPU & GPU." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14758.

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Telecom companies do a lot of analytics to provide consumers a better service and to stay in competition. These companies accumulate special big data that has potential to provide inputs for business. Query processing is one of the major tool to fire analytics at their data. Traditional query processing techniques which follow in-memory algorithm cannot cope up with the large amount of data of telecom operators. The k nearest neighbour technique(kNN) is best suitable method for classification and regression of large datasets. Our research is focussed on implementation of kNN as query processin

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Ji, Yuanzhen [Verfasser], Christof [Akademischer Betreuer] Fetzer, and Pascal [Gutachter] Falber. "Handling Tradeoffs between Performance and Query-Result Quality in Data Stream Processing / Yuanzhen Ji ; Gutachter: Pascal Falber ; Betreuer: Christof Fetzer." Dresden : Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://d-nb.info/115616947X/34.

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Book chapters on the topic "Performance of KG query processing"

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Nevarez, Benjamin. "Intelligent Query Processing." In High Performance SQL Server. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6491-1_10.

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Fritchey, Grant. "Intelligent Query Processing." In SQL Server 2022 Query Performance Tuning. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8891-7_21.

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Fritchey, Grant. "Row-by-Row Processing." In SQL Server Query Performance Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_22.

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Fritchey, Grant. "Row-by-Row Processing." In SQL Server 2017 Query Performance Tuning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3888-2_23.

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He, Ben, and Iadh Ounis. "Inferring Query Performance Using Pre-retrieval Predictors." In String Processing and Information Retrieval. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30213-1_5.

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Soleimany, Cyrus, and Sivarama P. Dandamudi. "Distributed Parallel Query Processing on Networks of Workstations." In High Performance Computing and Networking. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45492-6_42.

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Pérez-Iglesias, Joaquín, and Lourdes Araujo. "Evaluation of Query Performance Prediction Methods by Range." In String Processing and Information Retrieval. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16321-0_23.

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Fritchey, Grant. "Row-by-Row Processing from Cursors and Other Causes." In SQL Server 2022 Query Performance Tuning. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8891-7_18.

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Kersten, M. L., S. Shair-Ali, and C. A. Berg. "Performance analysis of a dynamic query processing scheme." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-54132-2_61.

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Zeng, Sijun, and Sivarama P. Dandamudi. "Centralized architecture for parallel query processing on networks of workstations." In High-Performance Computing and Networking. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0100629.

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Conference papers on the topic "Performance of KG query processing"

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Hains, Gaetan, and Muath Alrammal. "Performance and Scalability of XML Query Processing." In 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS). IEEE, 2012. http://dx.doi.org/10.1109/cisis.2012.18.

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Wlodarczyk, Tomasz Wiktor, Yi Han, and Chunming Rong. "Performance Analysis of Hadoop for Query Processing." In 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications (WAINA). IEEE, 2011. http://dx.doi.org/10.1109/waina.2011.130.

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Franklin, Michael J., Björn Thór Jónsson, and Donald Kossmann. "Performance tradeoffs for client-server query processing." In the 1996 ACM SIGMOD international conference. ACM Press, 1996. http://dx.doi.org/10.1145/233269.233328.

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Farakani, Vahid, Mohammad Harizi, and Mehdi Harizi. "Effective Guidance-Based XML Query Processing." In 2008 10th IEEE International Conference on High Performance Computing and Communications (HPCC). IEEE, 2008. http://dx.doi.org/10.1109/hpcc.2008.46.

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Wu, Hao, and Hui Fang. "Analytical Performance Modeling for Top-K Query Processing." In CIKM '14: 2014 ACM Conference on Information and Knowledge Management. ACM, 2014. http://dx.doi.org/10.1145/2661829.2661931.

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Chandra, Surendar, and William Acosta. "Using query transformation to improve Gnutella search performance." In Distributed Processing, Workshops and Phd Forum (IPDPSW). IEEE, 2010. http://dx.doi.org/10.1109/ipdpsw.2010.5470933.

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Kim, Hyeongsik, Abhisha Bhattacharyya, and Kemafor Anyanwu. "Semantic query transformations for increased parallelization in distributed knowledge graph query processing." In SC '19: The International Conference for High Performance Computing, Networking, Storage, and Analysis. ACM, 2019. http://dx.doi.org/10.1145/3295500.3356212.

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Ding, Shuai, Jinru He, Hao Yan, and Torsten Suel. "Using graphics processors for high performance IR query processing." In the 18th international conference. ACM Press, 2009. http://dx.doi.org/10.1145/1526709.1526766.

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Liu, Mo, Elke Rundensteiner, Dan Dougherty, et al. "High-performance nested CEP query processing over event streams." In 2011 IEEE International Conference on Data Engineering (ICDE 2011). IEEE, 2011. http://dx.doi.org/10.1109/icde.2011.5767839.

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Ding, Shuai, Jinru He, Hao Yan, and Torsten Suel. "Using graphics processors for high-performance IR query processing." In Proceeding of the 17th international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1367497.1367732.

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Reports on the topic "Performance of KG query processing"

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Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41960.

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The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in nonquantitative text descriptions. We present initial text mining analyses of parsed text i

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