Relevant bibliographies by topics / Cardinality Estimation

Academic literature on the topic 'Cardinality Estimation'

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Published: 6 September 2023

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Journal articles on the topic "Cardinality Estimation"

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Harmouch, Hazar, and Felix Naumann. "Cardinality estimation." Proceedings of the VLDB Endowment 11, no. 4 (December 2017): 499–512. http://dx.doi.org/10.1145/3186728.3164145.

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Kwon, Suyong, Woohwan Jung, and Kyuseok Shim. "Cardinality estimation of approximate substring queries using deep learning." Proceedings of the VLDB Endowment 15, no. 11 (July 2022): 3145–57. http://dx.doi.org/10.14778/3551793.3551859.

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Cardinality estimation of an approximate substring query is an important problem in database systems. Traditional approaches build a summary from the text data and estimate the cardinality using the summary with some statistical assumptions. Since deep learning models can learn underlying complex data patterns effectively, they have been successfully applied and shown to outperform traditional methods for cardinality estimations of queries in database systems. However, since they are not yet applied to approximate substring queries, we investigate a deep learning approach for cardinality estimation of such queries. Although the accuracy of deep learning models tends to improve as the train data size increases, producing a large train data is computationally expensive for cardinality estimation of approximate substring queries. Thus, we develop efficient train data generation algorithms by avoiding unnecessary computations and sharing common computations. We also propose a deep learning model as well as a novel learning method to quickly obtain an accurate deep learning-based estimator. Extensive experiments confirm the superiority of our data generation algorithms and deep learning model with the novel learning method.
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Liu, Jie, Wenqian Dong, Qingqing Zhou, and Dong Li. "Fauce." Proceedings of the VLDB Endowment 14, no. 11 (July 2021): 1950–63. http://dx.doi.org/10.14778/3476249.3476254.

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Cardinality estimation is a fundamental and critical problem in databases. Recently, many estimators based on deep learning have been proposed to solve this problem and they have achieved promising results. However, these estimators struggle to provide accurate results for complex queries, due to not capturing real inter-column and inter-table correlations. Furthermore, none of these estimators contain the uncertainty information about their estimations. In this paper, we present a join cardinality estimator called Fauce. Fauce learns the correlations across all columns and all tables in the database. It also contains the uncertainty information of each estimation. Among all studied learned estimators, our results are promising: (1) Fauce is a light-weight estimator, it has 10× faster inference speed than the state of the art estimator; (2) Fauce is robust to the complex queries, it provides 1.3×--6.7× smaller estimation errors for complex queries compared with the state of the art estimator; (3) To the best of our knowledge, Fauce is the first estimator that incorporates uncertainty information for cardinality estimation into a deep learning model.
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Sun, Ji, Jintao Zhang, Zhaoyan Sun, Guoliang Li, and Nan Tang. "Learned cardinality estimation." Proceedings of the VLDB Endowment 15, no. 1 (September 2021): 85–97. http://dx.doi.org/10.14778/3485450.3485459.

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Cardinality estimation is core to the query optimizers of DBMSs. Non-learned methods, especially based on histograms and samplings, have been widely used in commercial and open-source DBMSs. Nevertheless, histograms and samplings can only be used to summarize one or few columns, which fall short of capturing the joint data distribution over an arbitrary combination of columns, because of the oversimplification of histograms and samplings over the original relational table(s). Consequently, these traditional methods typically make bad predictions for hard cases such as queries over multiple columns, with multiple predicates, and joins between multiple tables. Recently, learned cardinality estimators have been widely studied. Because these learned estimators can better capture the data distribution and query characteristics, empowered by the recent advance of (deep learning) models, they outperform non-learned methods on many cases. The goals of this paper are to provide a design space exploration of learned cardinality estimators and to have a comprehensive comparison of the SOTA learned approaches so as to provide a guidance for practitioners to decide what method to use under various practical scenarios.
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Han, Yuxing, Ziniu Wu, Peizhi Wu, Rong Zhu, Jingyi Yang, Liang Wei Tan, Kai Zeng, et al. "Cardinality estimation in DBMS." Proceedings of the VLDB Endowment 15, no. 4 (December 2021): 752–65. http://dx.doi.org/10.14778/3503585.3503586.

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Cardinality estimation (CardEst) plays a significant role in generating high-quality query plans for a query optimizer in DBMS. In the last decade, an increasing number of advanced CardEst methods (especially ML-based) have been proposed with outstanding estimation accuracy and inference latency. However, there exists no study that systematically evaluates the quality of these methods and answer the fundamental problem: to what extent can these methods improve the performance of query optimizer in real-world settings, which is the ultimate goal of a CardEst method. In this paper, we comprehensively and systematically compare the effectiveness of CardEst methods in a real DBMS. We establish a new benchmark for CardEst, which contains a new complex real-world dataset STATS and a diverse query workload STATS-CEB. We integrate multiple most representative CardEst methods into an open-source DBMS PostgreSQL, and comprehensively evaluate their true effectiveness in improving query plan quality, and other important aspects affecting their applicability. We obtain a number of key findings under different data and query settings. Furthermore, we find that the widely used estimation accuracy metric (Q-Error) cannot distinguish the importance of different sub-plan queries during query optimization and thus cannot truly reflect the generated query plan quality. Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods. It could serve as a better optimization objective for future CardEst methods.
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Yang, Zongheng, Eric Liang, Amog Kamsetty, Chenggang Wu, Yan Duan, Xi Chen, Pieter Abbeel, Joseph M. Hellerstein, Sanjay Krishnan, and Ion Stoica. "Deep unsupervised cardinality estimation." Proceedings of the VLDB Endowment 13, no. 3 (November 2019): 279–92. http://dx.doi.org/10.14778/3368289.3368294.

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Chen, Jeremy, Yuqing Huang, Mushi Wang, Semih Salihoglu, and Ken Salem. "Accurate summary-based cardinality estimation through the lens of cardinality estimation graphs." Proceedings of the VLDB Endowment 15, no. 8 (April 2022): 1533–45. http://dx.doi.org/10.14778/3529337.3529339.

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This paper is an experimental and analytical study of two classes of summary-based cardinality estimators that use statistics about input relations and small-size joins in the context of graph database management systems: (i) optimistic estimators that make uniformity and conditional independence assumptions; and (ii) the recent pessimistic estimators that use information theoretic linear programs (LPs). We begin by analyzing how optimistic estimators use pre-computed statistics to generate cardinality estimates. We show these estimators can be modeled as picking bottom-to-top paths in a cardinality estimation graph (CEG), which contains sub-queries as nodes and edges whose weights are average degree statistics. We show that existing optimistic estimators have either undefined or fixed choices for picking CEG paths as their estimates and ignore alternative choices. Instead, we outline a space of optimistic estimators to make an estimate on CEGs, which subsumes existing estimators. We show, using an extensive empirical analysis, that effective paths depend on the structure of the queries. While on acyclic queries and queries with small-size cycles, using the maximum-weight path is effective to address the well known underestimation problem, on queries with larger cycles these estimates tend to overestimate, which can be addressed by using minimum weight paths. We next show that optimistic estimators and seemingly disparate LP-based pessimistic estimators are in fact connected. Specifically, we show that CEGs can also model some recent pessimistic estimators. This connection allows us to adopt an optimization from pessimistic estimators to optimistic ones, and provide insights into the pessimistic estimators, such as showing that they have combinatorial solutions.
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Chen, Jeremy, Yuqing Huang, Mushi Wang, Semih Salihoglu, and Kenneth Salem. "Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs." ACM SIGMOD Record 52, no. 1 (June 7, 2023): 94–102. http://dx.doi.org/10.1145/3604437.3604458.

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We study two classes of summary-based cardinality estimators that use statistics about input relations and small-size joins: (i) optimistic estimators, which were defined in the context of graph database management systems, that make uniformity and conditional independence assumptions; and (ii) the recent pessimistic estimators that use information theoretic linear programs (LPs). We show that optimistic estimators can be modeled as picking bottom-to-top paths in a cardinality estimation graph (CEG), which contains subqueries as nodes and edges whose weights are average degree statistics. We show that existing optimistic estimators have either undefined or fixed choices for picking CEG paths as their estimates and ignore alternative choices. Instead, we outline a space of optimistic estimators to make an estimate on CEGs, which subsumes existing estimators. We show, using an extensive empirical analysis, that effective paths depend on the structure of the queries. We next show that optimistic estimators and seemingly disparate LP-based pessimistic estimators are in fact connected. Specifically, we show that CEGs can also model some recent pessimistic estimators. This connection allows us to provide insights into the pessimistic estimators, such as showing that they have combinatorial solutions.
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Jie, Xu, Lan Haoliang, Ding Wei, and Ju Ao. "Network Host Cardinality Estimation Based on Artificial Neural Network." Security and Communication Networks 2022 (March 24, 2022): 1–14. http://dx.doi.org/10.1155/2022/1258482.

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Cardinality estimation plays an important role in network security. It is widely used in host cardinality calculation of high-speed network. However, the cardinality estimation algorithm itself is easy to be disturbed by random factors and produces estimation errors. How to eliminate the influence of these random factors is the key to further improving the accuracy of estimation. To solve the above problems, this paper proposes an algorithm that uses artificial neural network to predict the estimation bias and adjust the cardinality estimation value according to the prediction results. Based on the existing algorithms, the novel algorithm reduces the interference of random factors on the estimation results and improves the accuracy by adding the steps of cardinality estimation sampling, artificial neural network training, and error prediction. The experimental results show that, using the cardinality estimation algorithm proposed in this paper, the average absolute deviation of cardinality estimation can be reduced by more than 20%.
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Gao, Jintao, Zhanhuai Li, and Wenjie Liu. "A Strategy of Efficient and Accurate Cardinality Estimation Based on Query Result." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 4 (August 2018): 768–77. http://dx.doi.org/10.1051/jnwpu/20183640768.

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Cardinality estimation is an important component of query optimization. Its accuracy and efficiency directly decide effect of query optimization. Traditional cardinality estimation strategy is based on original table or sample to collect statistics, then inferring cardinality by collected statistics. It will be low-efficiency when handling big data; Statistics exist update latency and are gotten by inferring, which can not guarantee correctness; Some strategies can get the actual cardinality by executing some subqueries, but they do not keep the result, leading to low efficiency of fetching statistics. Against these problems, this paper proposes a novel cardinality estimation strategy, called cardinality estimation based on query result(CEQR). For keeping correctness of cardinality, CEQR directly gets statistics from query results, which is not related with data size; we build a cardinality table to store the statistics of basic tables and middle results under specific predicates. Cardinality table can provide cardinality services for subsequent queries, and we build a suit of rules to maintain cardinality table; To improve the efficiency of fetching statistics, we introduce the source aware strategy, which hashes cardinality item to appropriate cache. This paper gives the adaptability and deviation analytic of CEQR, and proves that CEQR is more efficient than traditional cardinality estimation strategy by experiments.
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Dissertations / Theses on the topic "Cardinality Estimation"

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Falgén, Enqvist Olle. "Cardinality estimation with a machine learning approach." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288909.

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This thesis investigates how three different machine learning models perform on cardinalty estimation for sql queries. All three models were evaluated on three different data sets. The models were tested on both estimating cardinalities when the query just takes information from one table and also a two way join case. Postgresql's own cardinality estimator was used as a baseline. The evaluated models were: Artificial neural networks, random forests and extreme gradient boosted trees. What was found is that the model that performs best is the extreme gradient boosted tree with a tweedie regression loss function. To the authors knowledge, this is the first time an extreme gradient boosted tree has been used in this context.
Denna uppsats undersöker hur tre olika maskininlärningsmodeller presterar på kardinalitetsuppskattning för sql förfrågningar till en databas. Alla tre modeller utvärderades på tre olika datauppsättningar. Modellerna fick både behandla förfrågningar från en tabell, samt en sammanslagning mellan två tabeller. Postgresql's egna kardinalitetsestimerare användes som referenspunkt. De utvärderade modellerna var följande: artificiella neurala nätverk, random forests och extreme gradient boosted trees. En slutsats var att den modellen som utförde uppgiften bäst var extreme gradient boosted trees med en tweedie-regression förlustfunktion. Såvitt författaren vet är det här första gången den här typen av extreme gradient boosted tree används på denna typ av problem.
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Qian, Chen. "Efficient cardinality counting for large-scale RFID systems /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20QIAN.

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Thiyagarajah, Murali. "Attribute cardinality maps, new query result size estimation techniques for database systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0007/NQ42810.pdf.

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Thiyagarajah, Murali (Muralitharam) Carleton University Dissertation Computer Science. "Attribute cardinality maps; new query result size estimation techniques for database systems." Ottawa, 1999.

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Nguyen, Thanh Chuyen. "Studies on Algorithms for Tag Identification and Tag Set Cardinality Estimation in Radio Frequency Identification Systems." 京都大学 (Kyoto University), 2013. http://hdl.handle.net/2433/174849.

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Cosma, Ioana Ada. "Dimension reduction of streaming data via random projections." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:09eafd84-8cb3-4e54-8daf-18db7832bcfc.

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A data stream is a transiently observed sequence of data elements that arrive unordered, with repetitions, and at very high rate of transmission. Examples include Internet traffic data, networks of banking and credit transactions, and radar derived meteorological data. Computer science and engineering communities have developed randomised, probabilistic algorithms to estimate statistics of interest over streaming data on the fly, with small computational complexity and storage requirements, by constructing low dimensional representations of the stream known as data sketches. This thesis combines techniques of statistical inference with algorithmic approaches, such as hashing and random projections, to derive efficient estimators for cardinality, l_{alpha} distance and quasi-distance, and entropy over streaming data. I demonstrate an unexpected connection between two approaches to cardinality estimation that involve indirect record keeping: the first using pseudo-random variates and storing selected order statistics, and the second using random projections. I show that l_{alpha} distances and quasi-distances between data streams, and entropy, can be recovered from random projections that exploit properties of alpha-stable distributions with full statistical efficiency. This is achieved by the method of L-estimation in a single-pass algorithm with modest computational requirements. The proposed estimators have good small sample performance, improved by the methods of trimming and winsorising; in other words, the value of these summary statistics can be approximated with high accuracy from data sketches of low dimension. Finally, I consider the problem of convergence assessment of Markov Chain Monte Carlo methods for simulating from complex, high dimensional, discrete distributions. I argue that online, fast, and efficient computation of summary statistics such as cardinality, entropy, and l_{alpha} distances may be a useful qualitative tool for detecting lack of convergence, and illustrate this with simulations of the posterior distribution of a decomposable Gaussian graphical model via the Metropolis-Hastings algorithm.
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Lo, Bianco Accou Giovanni Christian. "Estimating the number of solutions on cardinality constraints." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2019. http://www.theses.fr/2019IMTA0155/document.

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La richesse de la programmation par contraintes repose sur la très large variété des algorithmes qu’elle utilise en puisant dans les grands domaines de l’Intelligence Artificielle, de la Programmation Logique et de la Recherche Opérationnelle. Cependant, cette richesse, qui offre aux spécialistes une palette quasi-illimitée de configurations possibles pour attaquer des problèmes combinatoires, devient une frein à la diffusion plus large du paradigme, car les outils actuels sont très loin d’une boîte noire, et leur utilisation suppose une bonne connaissance du domaine, notamment en ce qui concerne leur paramétrage. Dans cette thèse, nous proposons d’analyser le comportement des contraintes de cardinalité avec des modèles probabilistes et des outils de dénombrement, pour paramétrer automatiquement les solveurs de contraintes : heuristiques de choix de variables et de choix de valeurs et stratégies de recherche
The main asset of constraint programming is its wide variety of algorithms that comes from the major areas of artificial intelligence, logic programming and operational research. It offers specialists a limitless range of possible configurations to tackle combinatorial problems, but it becomes an obstacle to the wider diffusion of the paradigm. The current tools are very far from being used as a black-box tool, and it assumes a good knowledge of the field, in particular regarding the parametrization of solvers.In this thesis, we propose to analyze the behavior of cardinality constraints with probabilistic models and counting tools, to automatically parameterize constraint solvers: heuristics of choice of variables and choice of values and search strategies
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Yasnitsky, Irena. "Query cardinality estimation in relational databases /." 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR19658.

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Thesis (M.Sc.)--York University, 2006. Graduate Programme in Computer Science.
Typescript. Includes bibliographical references (leaves 261-287). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR19658
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Yu, Xiaohui. "Techniques for cardinality estimation in relational database systems." 2007. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=478910&T=F.

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Books on the topic "Cardinality Estimation"

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Cardinality Estimation Techniques in Relational Database Systems. Saarbrücken: VDM Verlag Dr. Müller, 2008.

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Book chapters on the topic "Cardinality Estimation"

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Fernandez, Gregory, and Chabane Djeraba. "Partition Cardinality Estimation in Image Repositories." In Mining Multimedia and Complex Data, 232–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39666-6_15.

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Song, Jinhua, Ching-Hsien Hsu, Mianxiong Dong, and Daqiang Zhang. "Vehicle Cardinality Estimation in VANETs by Using RFID Tag Estimator." In Internet of Vehicles - Safe and Intelligent Mobility, 3–15. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27293-1_1.

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Cichoń, Jacek, Jakub Lemiesz, and Marcin Zawada. "On Cardinality Estimation Protocols for Wireless Sensor Networks." In Ad-hoc, Mobile, and Wireless Networks, 322–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22450-8_25.

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Paradies, Marcus, Elena Vasilyeva, Adrian Mocan, and Wolfgang Lehner. "Robust Cardinality Estimation for Subgraph Isomorphism Queries on Property Graphs." In Lecture Notes in Computer Science, 184–98. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41576-5_14.

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Douik, Ahmed, Salah A. Aly, Tareq Y. Al-Naffouri, and Mohamed-Slim Alouini. "Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks." In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017, 569–78. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64861-3_53.

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Willkomm, Jens, Martin Schäler, and Klemens Böhm. "Accurate Cardinality Estimation of Co-occurring Words Using Suffix Trees." In Database Systems for Advanced Applications, 721–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73197-7_50.

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Khachatryan, Andranik, and Klemens Böhm. "Accurate Cost Estimation Using Distribution-Based Cardinality Estimates for Multi-dimensional Queries." In Lecture Notes in Computer Science, 581–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22351-8_46.

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Chinnaiah, Balarengadurai. "Protection of DDoS Attacks at the Application Layer: HyperLogLog (HLL) Cardinality Estimation." In Cognitive Informatics and Soft Computing, 595–604. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1056-1_46.

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Zhu, Daobing, Dongsheng He, Shuhuan Fan, Jianming Liao, and Mengshu Hou. "GACE: Graph-Attention-Network-Based Cardinality Estimator." In Lecture Notes in Computer Science, 332–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86475-0_32.

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Ito, Ryuichi, Chuan Xiao, and Makoto Onizuka. "Robust Cardinality Estimator by Non-autoregressive Model." In Communications in Computer and Information Science, 55–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93849-9_3.

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Conference papers on the topic "Cardinality Estimation"

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Cai, Walter, Magdalena Balazinska, and Dan Suciu. "Pessimistic Cardinality Estimation." In SIGMOD/PODS '19: International Conference on Management of Data. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3299869.3319894.

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Zhang, Zhenjie, Yin Yang, Ruichu Cai, Dimitris Papadias, and Anthony Tung. "Kernel-based skyline cardinality estimation." In the 35th SIGMOD international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1559845.1559899.

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Thiele, Maik, Tim Kiefer, and Wolfgang Lehner. "Cardinality estimation in ETL processes." In Proceeding of the ACM twelfth international workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1651291.1651302.

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Xie, JiangCheng, Le Tang, and Jing He. "Unsupervised Model with Cardinality Estimation." In 2022 International Seminar on Computer Science and Engineering Technology (SCSET). IEEE, 2022. http://dx.doi.org/10.1109/scset55041.2022.00056.

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Najjar, F., and Y. Slimani. "Cardinality estimation of distributed join queries." In Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99. IEEE, 1999. http://dx.doi.org/10.1109/dexa.1999.795147.

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Sun, Ji, Guoliang Li, and Nan Tang. "Learned Cardinality Estimation for Similarity Queries." In SIGMOD/PODS '21: International Conference on Management of Data. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3448016.3452790.

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Liu, Xiulong, Bin Xiao, Keqiu Li, Jie Wu, Alex X. Liu, Heng Qi, and Xin Xie. "RFID cardinality estimation with blocker tags." In IEEE INFOCOM 2015 - IEEE Conference on Computer Communications. IEEE, 2015. http://dx.doi.org/10.1109/infocom.2015.7218548.

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Lucchese, Riccardo, and Damiano Varagnolo. "Networks cardinality estimation using order statistics." In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7171924.

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Ré, Christopher, and Dan Suciu. "Understanding cardinality estimation using entropy maximization." In the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1807085.1807095.

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Woltmann, Lucas, Claudio Hartmann, Maik Thiele, Dirk Habich, and Wolfgang Lehner. "Cardinality estimation with local deep learning models." In the Second International Workshop. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3329859.3329875.

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