Relevant bibliographies by topics / Query performance

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Query performance'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Query performance"

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He, Ben, and Iadh Ounis. "Query performance prediction." Information Systems 31, no. 7 (November 2006): 585–94. http://dx.doi.org/10.1016/j.is.2005.11.003.

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Shtok, Anna, Oren Kurland, David Carmel, Fiana Raiber, and Gad Markovits. "Predicting Query Performance by Query-Drift Estimation." ACM Transactions on Information Systems 30, no. 2 (May 2012): 1–35. http://dx.doi.org/10.1145/2180868.2180873.

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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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Robb, David A., Paul L. Bowen, A. Faye Borthick, and Fiona H. Rohde. "Improving New Users’ Query Performance." Journal of Data and Information Quality 3, no. 4 (September 2012): 1–22. http://dx.doi.org/10.1145/2348828.2348829.

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Sarnikar, Surendra, Zhu Zhang, and J. Leon Zhao. "Query-performance prediction for effective query routing in domain-specific repositories." Journal of the Association for Information Science and Technology 65, no. 8 (April 11, 2014): 1597–614. http://dx.doi.org/10.1002/asi.23072.

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Xu, Jialu, and Feiyue Ye. "Query Recommendation Using Hybrid Query Relevance." Future Internet 10, no. 11 (November 19, 2018): 112. http://dx.doi.org/10.3390/fi10110112.

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With the explosion of web information, search engines have become main tools in information retrieval. However, most queries submitted in web search are ambiguous and multifaceted. Understanding the queries and mining query intention is critical for search engines. In this paper, we present a novel query recommendation algorithm by combining query information and URL information which can get wide and accurate query relevance. The calculation of query relevance is based on query information by query co-concurrence and query embedding vector. Adding the ranking to query-URL pairs can calculate the strength between query and URL more precisely. Empirical experiments are performed based on AOL log. The results demonstrate the effectiveness of our proposed query recommendation algorithm, which achieves superior performance compared to other algorithms.
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LANG, Hao. "Predicting Query Performance for Text Retrieval." Journal of Software 19, no. 2 (July 10, 2008): 291–300. http://dx.doi.org/10.3724/sp.j.1001.2008.00291.

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Shtok, Anna, Oren Kurland, and David Carmel. "Query Performance Prediction Using Reference Lists." ACM Transactions on Information Systems 34, no. 4 (September 14, 2016): 1–34. http://dx.doi.org/10.1145/2926790.

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O'Neil, Patrick, and Dallan Quass. "Improved query performance with variant indexes." ACM SIGMOD Record 26, no. 2 (June 1997): 38–49. http://dx.doi.org/10.1145/253262.253268.

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Benham, Rodger, Joel Mackenzie, Alistair Moffat, and J. Shane Culpepper. "Boosting Search Performance Using Query Variations." ACM Transactions on Information Systems 37, no. 4 (December 10, 2019): 1–25. http://dx.doi.org/10.1145/3345001.

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Dissertations / Theses on the topic "Query performance"

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Maharajan, Shridevika. "Performance of native SPARQL query processors." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-174542.

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Expressing data in RDF is one approach for making data available as Linked Data on the Web. Searching such data requires an RDF database engine providing some query language. The standard query language for RDF is called SPARQL. An RDF database engine can either be a middleware on top of an existing (relational) database or a native RDF store having its own internal data repository. Organizations often have difficulties to decide which solution they should adopt because there are few comprehensive comparisons of existing native RDF stores with respect to performance and scalability. The Berlin Benchmark provides a framework for comparing the performance different implementations of SPARQL engines in general. We have made a performance comparison between some existing RDF store""solutions based on the Berlin benchmark and summarize their performance outcomes with respect to load and query time. The RDF stores OpenLink Virtuoso, and AllegroGraph are compared. Furthermore  we also evaluate the performance of the general graph database Neo4j with the general SPARQL processor Squirrel on top. As a base-line for middleware solutions we also compare with Jena SDB, running of top of MySQL.
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Robb, David A. "Query error detection : using base rates to improve end user query performance /." [St. Lucia, Qld], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18191.pdf.

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Zeuch, Steffen. "Query Execution on Modern CPUs." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19296.

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Über die letzten Jahrzehnte haben sich Datenbanken von festplatten-basierten zu hauptspeicher-basierten Datenbanksystemen entwickelt. Um diese Herausforderungen anzugehen und das volle Potenzial moderner Prozessoren zu erschließen, stellt diese Dissertation vier Ansätze vor um den Einfluss der „Memory Wall“ zu reduzieren. Der erste Ansatz zeigt auf, wie spezielle Prozessorinstruktionen (sogenannte SIMD Instruktionen) die Ausnutzung von Caches erhöhen und gleichzeitig die Anzahl der Instruktionen verringern. In dieser Arbeit werden dazu vorhandene Baumstrukturen so angepasst, dass diese SIMD Instruktionen verwendet werden können und somit die benötigte Hauptspeicherbandbreite verringert wird. Der zweite Ansatz dieser Arbeit führt ein Model ein, welches es ermöglicht die Anfrageausführung in verschiedenen Datenbanksystemen zu vereinheitlichen und dadurch vergleichbar zu machen. Durch diese Vereinheitlichung wird es möglich, die Hardwareausnutzung durch Hinzunahme von Wissen über die auszuführende Hardware zu optimieren. Der dritte Ansatz analysiert verschiedene Datenbankoperatoren bezüglich ihres Verhaltens auf verschiedenen Hardwareumgebungen. Diese Analyse ermöglicht es, Datenbankoperatoren besser zu verstehen und Kostenmodelle für ihr Verhalten zu entwickeln. Der vierte Ansatz dieser Arbeit baut auf der Analyse der Operatoren auf und führt einen progressiven Optimierungsalgorithmus ein, der die Ausführung von Anfragen zur Laufzeit auf die jeweiligen Bedingungen wie z.B. Daten- oder Hardwareeigenschaften anpasst. Dazu werden zur Laufzeit prozessorinterne Zähler verwendet, die das Verhalten des Operators auf der jeweiligen Hardware widerspiegeln.
Over the last decades, database systems have been migrated from disk to memory architectures such as RAM, Flash, or NVRAM. Research has shown that this migration fundamentally shifts the performance bottleneck upwards in the memory hierarchy. Whereas disk-based database systems were largely dominated by disk bandwidth and latency, in-memory database systems mainly depend on the efficiency of faster memory components, e.g., RAM, caches, and registers. To encounter these challenges and enable the full potential of the available processing power of modern CPUs for database systems, this thesis proposes four approaches to reduce the impact of the Memory Wall. First, SIMD instructions increase the cache line utilization and decrease the number of executed instructions if they operate on an appropriate data layout. Thus, we adapt tree structures for processing with SIMD instructions to reduce demands on the memory bus and processing units are decreased. Second, by modeling and executing queries following a unified model, we are able to achieve high resource utilization. Therefore, we propose a unified model that enables us to utilize knowledge about the query plan and the underlying hardware to optimize query execution. Third, we need a fundamental knowledge about the individual database operators and their behavior and requirements to optimally distribute the resources among available computing units. We conduct an in-depth analysis of different workloads using performance counters create these insights. Fourth, we propose a non-invasive progressive optimization approach based on in-depth knowledge of individual operators that is able to optimize query execution during run-time. In sum, using additional run-time statistics gathered by performance counters, a unified model, and SIMD instructions, this thesis improves query execution on modern CPUs.
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Suto, Tamas. "Performance Trees : A Query Specification Formalism For Quantitative Performance Analysis." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501211.

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Wien, Sigurd. "Efficient Top-K Fuzzy Interactive Query Expansion While Formulating a Query : From a Performance Perspective." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-23010.

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Interactive query expansion and fuzzy search are two efficient techniques for assisting a user in an information retrieval process. Interactive query expansion helps the user refine a query by giving suggestions on how a query might be extended to further specify the actual information need of the user. Fuzzy search, on the other hand, supports the user by including results for terms that approximately equals the query string. This avoids reformulating queries with slight misspellings and will retrieve results for indexed terms not spelled as expected. This study will look at the performance aspects of combining these concepts to give the user real time suggestions on how to complete query as the query is formulated letter by letter. These suggestions will be a set of terms from the index that are fuzzy matches of the query string terms, and are chosen based on the individual rank of the term, the semantic correlation between the individual term and the edit distance between the query and the suggestion.The combination of these techniques is challenging from a performance aspect because each of them requires a lot of computation, and their relationship is such that these computations will be multiplicative when combined. Giving suggestions letter by letter as the user types requires a lookup for each letter and fuzzy search will expand each of these lookups with the fuzzy matches of the prefix to match against the index. For each of these different completions of the fuzzy matched prefixes, we will need to calculate the semantic correlation it has to the previous matched terms.This study will present three algorithms to give top-k suggestions for the single term case and then extend these in three ways to handle multi term queries. These algorithms will use a trie based term index with some extensions to enable fast lookup of top-k terms that match a given prefix and to assess the semantic correlation between the terms in the suggestion. The performance review will demonstrate that our approach will be viable to use for presenting the user with suggestions in real time even with a fairly large number of terms.
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Wang, Jing. "Optimizing graph query performance by indexing and caching." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8272/.

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Subgraph/supergraph queries, though central to graph analytics, are costly as they entail the NP-Complete problem of subgraph isomorphism. To expedite graph query processing, the community has contributed a wealth of approaches that gradually form two categories, i.e., heuristic subgraph isomorphism (SI) methods and algorithms following “filter-then-verify” paradigm (FTV). However, they both bear performance limitations. And a significant drawback of current studies lies in that they throw away the results obtained when executing previous graph queries. To this end, the current work shall present a fresh solution named iGQ, principle of which is to acquire and utilize knowledge from the results of previously executed queries. iGQ encompasses two component subindexes to identify if a new query is a subgraph or supergraph of previously executed queries, such that the stored knowledge will be turned on to accelerate the execution of the new query graph through reducing the subgraph isomorphism tests to be performed. The correctness of iGQ is assured by formal proof. Moreover, iGQ affords the elegance of double use for subgraph and supergraph query processing, bridging the two separate research threads in the community. On the other hand, using cache to accelerate query processing has been prevalent in data management systems. In the realm of graph structured queries, however, little work has been done. Meanwhile, modern big data applications are emerging and demanding the high performance of graph query processing. Therefore, this thesis shall put forth a full-fledged graph caching system coined GraphCache for graph queries. From the ground up, GraphCache is designed as a semantic graph cache that could harness both subgraph and supergraph cache hits, expanding the traditional hits confined by exact match. GraphCache is featured by well-defined subsystems and interfaces, allowing for the flexibility of plugging in any general subgraph/supergraph query solution, be it an FTV algorithm or SI method. Furthermore, GraphCache incorporates the iGQ as the engine of query processing, where previously issued queries are leveraged to expedite graph query processing. With the continuous arrival of queries and the finite memory space, GraphCache requires mechanisms to effectively manage the space, which in turn emerges the problem of cache replacement. But none of the existing replacement policies are developed specifically for graph cache. This work hence proposes a number of graph query aware strategies with different trade-offs and emphasizes a novel hybrid replacement policy with competitive performance. Following the established research in literature, GraphCache handles graph queries against a static dataset, i.e., all graphs in the underlying dataset keep untouched during the continual arrival and execution of queries. However, in real-world applications, the graph dataset naturally evolves/changes over time. This poses a significant challenge for the current graph caching technique and hence gives rise to the requirement of advanced systems that are capable of accelerating subgraph/supergraph queries against dynamic datasets. To address the problem, this work shall contribute an upgraded graph caching system, namely GraphCache+, stressing the newly plugged in subsystems and components of dealing with the consistency of graph cache. GraphCache+ is characterized by its two cache models that represent different designs of ensuring graph cache consistency, as well as the novel logics of alleviating subgraph and supergraph query processing with formal proof of correctness. Additionally, this work is bundled with comprehensive performance evaluations of GraphCache/GraphCache+ with over 6 million queries against both real-world and synthetic datasets with different characteristics, revealing a number of non-trivial lessons. In overall, this work contributes to the community from three perspectives: it provides a fresh idea to expedite graph query processing, applicable for both SI methods and FTV algorithms; it presents GraphCache, to the best of our knowledge the first full-fledged graph caching system for general subgraph/supergraph queries; it explores the topic of graph cache consistency, putting forth a systematic solution GraphCache+.
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Richardson, Bartley D. "A Performance Study of XML Query Optimization Techniques." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1258475256.

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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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Wang, Lian, and 王漣. "Mining information from XML documents for query performance enhancement." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30497486.

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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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Books on the topic "Query performance"

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Fritchey, Grant. SQL Server Query Performance Tuning. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3.

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Krogh, Jesper Wisborg. MySQL 8 Query Performance Tuning. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5584-1.

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Fritchey, Grant. SQL Server 2012 Query Performance Tuning. Berkeley, CA: Apress, 2012.

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Fritchey, Grant. SQL Server 2017 Query Performance Tuning. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3888-2.

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Fritchey, Grant. SQL Server 2012 Query Performance Tuning. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4204-8.

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Sajal, Dam, ed. SQL Server 2008 query performance tuning distilled. Berkeley, CA: Apress, 2009.

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Fritchey, Grant. SQL Server 2008 query performance tuning distilled. Berkeley, CA: Apress, 2009.

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Fritchey, Grant, and Sajal Dam. SQL Server 2008 Query Performance Tuning Distilled. Berkeley, CA: Apress, 2009. http://dx.doi.org/10.1007/978-1-4302-1903-3.

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Kekäläinen, Jaana. The effects of query complexity, expansion and structure on retrieval performance in probabilistic text retrieval. Tampere, Finland: University of Tampere, 1999.

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Presberg, David. Porting the distributed array query and visualization tool for high performance Fortran to the SP2. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1996.

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Book chapters on the topic "Query performance"

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Harrison, Guy, and Michael Harrison. "Query Tuning." In MongoDB Performance Tuning, 123–53. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6879-7_6.

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Dombrovskaya, Henrietta, Boris Novikov, and Anna Bailliekova. "Application Development and Performance." In PostgreSQL Query Optimization, 197–210. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6885-8_10.

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Brimhall, Jason, David Dye, Jonathan Gennick, Andy Roberts, and Wayne Sheffield. "Query Performance Tuning." In SQL Server 2012 T-SQL Recipes, 465–506. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4201-7_21.

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Simmons, Ken, and Sylvester Carstarphen. "Managing Query Performance." In Pro SQL Server 2012 Administration, 421–49. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-3916-1_16.

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Fritchey, Grant. "Query Performance Metrics." In SQL Server Query Performance Tuning, 69–84. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_6.

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Fritchey, Grant. "Analyzing Query Performance." In SQL Server Query Performance Tuning, 85–109. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_7.

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Fritchey, Grant. "Query Performance Metrics." In SQL Server 2017 Query Performance Tuning, 103–30. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3888-2_6.

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Fritchey, Grant. "Analyzing Query Performance." In SQL Server 2017 Query Performance Tuning, 131–83. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3888-2_7.

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Fritchey, Grant. "Query Recompilation." In SQL Server Query Performance Tuning, 321–54. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_17.

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Fritchey, Grant. "Query Recompilation." In SQL Server 2012 Query Performance Tuning, 281–311. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4204-8_10.

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Conference papers on the topic "Query performance"

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Cronen-Townsend, Steve, Yun Zhou, and W. Bruce Croft. "Predicting query performance." In the 25th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/564376.564429.

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Sormunen, Eero, Sakari Hokkanen, Petteri Kangaslampi, Petri Pyy, and Bemmu Sepponen. "Query performance analyser -." In the 25th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/564376.564491.

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Raiber, Fiana, and Oren Kurland. "Query-performance prediction." In SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2600428.2609581.

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Sondak, Mor, Anna Shtok, and Oren Kurland. "Estimating query representativeness for query-performance prediction." In SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2484028.2484107.

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Sawyer, Scott M., B. David O'Gwynn, An Tran, and Tamara Yu. "Understanding query performance in Accumulo." In 2013 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2013. http://dx.doi.org/10.1109/hpec.2013.6670330.

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Pal, Dipasree, Mandar Mitra, and Samar Bhattacharya. "Using Multiple Query Expansion Algorithms to Predict Query Performance." In 2014 Fourth International Conference of Emerging Applications of Information Technology (EAIT). IEEE, 2014. http://dx.doi.org/10.1109/eait.2014.67.

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Zendel, Oleg, J. Shane Culpepper, and Falk Scholer. "Is Query Performance Prediction With Multiple Query Variations Harder Than Topic Performance Prediction?" In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3404835.3463039.

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Kanhabua, Nattiya, and Kjetil Nørvåg. "Time-based query performance predictors." In the 34th international ACM SIGIR conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2009916.2010109.

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Kustarev, Andrey, Yury Ustinovskiy, Anna Mazur, and Pavel Serdyukov. "Session-based query performance prediction." In the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398692.

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Holzschuher, Florian, and René Peinl. "Performance of graph query languages." In the Joint EDBT/ICDT 2013 Workshops. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2457317.2457351.

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Reports on the topic "Query performance"

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Bethel, E. Wes, Scott Campbell, Eli Dart, John Shalf, Kurt Stockinger, and Kesheng Wu. High Performance Visualization using Query-Driven Visualizationand Analytics. Office of Scientific and Technical Information (OSTI), June 2006. http://dx.doi.org/10.2172/888965.

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Gao, Xiaoming, and Judy Qiu. Comparing IndexedHBase and Riak for Serving Truthy: Performance of Data Loading and Query Evaluation. Fort Belvoir, VA: Defense Technical Information Center, August 2013. http://dx.doi.org/10.21236/ada603198.

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Furey, 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.

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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 in the digitally available USDA soil taxonomy documentation and the Soil Survey Geographic database. Previous research has shown that latent information structure can be extracted from scientific literature using Natural Language Processing techniques, and we show that this latent information can be used to expedite query performance by using syntactic elements and part-of-speech tags as indices. Technical vocabulary often poses a text mining challenge due to the rarity of its diction in the broader context. We introduce an extension to the common English vocabulary that allows for nearly-complete indexing of USDA Soil Series Descriptions.
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