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Academic literature on the topic 'Cardinality Estimation in Database Systems'

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Published: 6 September 2023

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Journal articles on the topic "Cardinality Estimation in Database Systems"

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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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Qi, Kaiyang, Jiong Yu, and Zhenzhen He. "A Cardinality Estimator in Complex Database Systems Based on TreeLSTM." Sensors 23, no. 17 (August 23, 2023): 7364. http://dx.doi.org/10.3390/s23177364.

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Cardinality estimation is critical for database management systems (DBMSs) to execute query optimization tasks, which can guide the query optimizer in choosing the best execution plan. However, traditional cardinality estimation methods cannot provide accurate estimates because they cannot accurately capture the correlation between multiple tables. Several recent studies have revealed that learning-based cardinality estimation methods can address the shortcomings of traditional methods and provide more accurate estimates. However, the learning-based cardinality estimation methods still have large errors when an SQL query involves multiple tables or is very complex. To address this problem, we propose a sampling-based tree long short-term memory (TreeLSTM) neural network to model queries. The proposed model addresses the weakness of traditional methods when no sampled tuples match the predicates and considers the join relationship between multiple tables and the conjunction and disjunction operations between predicates. We construct subexpressions as trees using operator types between predicates and improve the performance and accuracy of cardinality estimation by capturing the join-crossing correlations between tables and the order dependencies between predicates. In addition, we construct a new loss function to overcome the drawback that Q-error cannot distinguish between large and small cardinalities. Extensive experimental results from real-world datasets show that our proposed model improves the estimation quality and outperforms traditional cardinality estimation methods and the other compared deep learning methods in three evaluation metrics: Q-error, MAE, and SMAPE.

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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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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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Lan, Hai, Zhifeng Bao, and Yuwei Peng. "A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration." Data Science and Engineering 6, no. 1 (January 15, 2021): 86–101. http://dx.doi.org/10.1007/s41019-020-00149-7.

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AbstractQuery optimizer is at the heart of the database systems. Cost-based optimizer studied in this paper is adopted in almost all current database systems. A cost-based optimizer introduces a plan enumeration algorithm to find a (sub)plan, and then uses a cost model to obtain the cost of that plan, and selects the plan with the lowest cost. In the cost model, cardinality, the number of tuples through an operator, plays a crucial role. Due to the inaccuracy in cardinality estimation, errors in cost model, and the huge plan space, the optimizer cannot find the optimal execution plan for a complex query in a reasonable time. In this paper, we first deeply study the causes behind the limitations above. Next, we review the techniques used to improve the quality of the three key components in the cost-based optimizer, cardinality estimation, cost model, and plan enumeration. We also provide our insights on the future directions for each of the above aspects.

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Wang, Xiaoying, Changbo Qu, Weiyuan Wu, Jiannan Wang, and Qingqing Zhou. "Are we ready for learned cardinality estimation?" Proceedings of the VLDB Endowment 14, no. 9 (May 2021): 1640–54. http://dx.doi.org/10.14778/3461535.3461552.

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Cardinality estimation is a fundamental but long unresolved problem in query optimization. Recently, multiple papers from different research groups consistently report that learned models have the potential to replace existing cardinality estimators. In this paper, we ask a forward-thinking question: Are we ready to deploy these learned cardinality models in production? Our study consists of three main parts. Firstly, we focus on the static environment (i.e., no data updates) and compare five new learned methods with nine traditional methods on four real-world datasets under a unified workload setting. The results show that learned models are indeed more accurate than traditional methods, but they often suffer from high training and inference costs. Secondly, we explore whether these learned models are ready for dynamic environments (i.e., frequent data updates). We find that they cannot catch up with fast data updates and return large errors for different reasons. For less frequent updates, they can perform better but there is no clear winner among themselves. Thirdly, we take a deeper look into learned models and explore when they may go wrong. Our results show that the performance of learned methods can be greatly affected by the changes in correlation, skewness, or domain size. More importantly, their behaviors are much harder to interpret and often unpredictable. Based on these findings, we identify two promising research directions (control the cost of learned models and make learned models trustworthy) and suggest a number of research opportunities. We hope that our study can guide researchers and practitioners to work together to eventually push learned cardinality estimators into real database systems.

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Ahad, Rafiul, K. V. Bapa, and Dennis McLeod. "On estimating the cardinality of the projection of a database relation." ACM Transactions on Database Systems 14, no. 1 (March 1989): 28–40. http://dx.doi.org/10.1145/62032.62034.

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Mukkamala, Ravi, and Sushil Jajodia. "A note on estimating the cardinality of the projection of a database relation." ACM Transactions on Database Systems 16, no. 3 (September 1991): 564–66. http://dx.doi.org/10.1145/111197.111218.

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Li, Guoliang, Xuanhe Zhou, Ji Sun, Xiang Yu, Yue Han, Lianyuan Jin, Wenbo Li, Tianqing Wang, and Shifu Li. "openGauss." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 3028–42. http://dx.doi.org/10.14778/3476311.3476380.

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Although learning-based database optimization techniques have been studied from academia in recent years, they have not been widely deployed in commercial database systems. In this work, we build an autonomous database framework and integrate our proposed learning-based database techniques into an open-source database system openGauss. We propose effective learning-based models to build learned optimizers (including learned query rewrite, learned cost/cardinality estimation, learned join order selection and physical operator selection) and learned database advisors (including self-monitoring, self-diagnosis, self-configuration, and self-optimization). We devise an effective validation model to validate the effectiveness of learned models. We build effective training data management and model management platforms to easily deploy learned models. We have evaluated our techniques on real-world datasets and the experimental results validated the effectiveness of our techniques. We also provide our learnings of deploying learning-based techniques.

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Woltmann, Lucas, Dominik Olwig, Claudio Hartmann, Dirk Habich, and Wolfgang Lehner. "PostCENN." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2715–18. http://dx.doi.org/10.14778/3476311.3476327.

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In this demo, we present PostCENN , an enhanced PostgreSQL database system with an end-to-end integration of machine learning (ML) models for cardinality estimation. In general, cardinality estimation is a topic with a long history in the database community. While traditional models like histograms are extensively used, recent works mainly focus on developing new approaches using ML models. However, traditional as well as ML models have their own advantages and disadvantages. With PostCENN , we aim to combine both to maximize their potentials for cardinality estimation by introducing ML models as a novel means to increase the accuracy of the cardinality estimation for certain parts of the database schema. To achieve this, we integrate ML models as first class citizen in PostgreSQL with a well-defined end-to-end life cycle. This life cycle consists of creating ML models for different sub-parts of the database schema, triggering the training, using ML models within the query optimizer in a transparent way, and deleting ML models.

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Dissertations / Theses on the topic "Cardinality Estimation in Database Systems"

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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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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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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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Xu, Zichen. "Power-Performance Tradeoffs in Database Systems." Scholar Commons, 2009. https://scholarcommons.usf.edu/etd/95.

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With the total energy consumption of computing systems increasing at a steep rate, much attention had been paid to the design of energy-efficient computing systems and applications. So far, database system design has focused on improving the performance of query processing. The objective of this study is to explore the potential of energy conservation in relational database management systems. The hypothesis is: by modifying the query optimizer in a Database management system (DBMS) to take the energy cost of query plans into consideration, we will be able to reduce the energy usage of database servers and control the tradeoffs between energy consumption and system performance. In this thesis, we provide an in-depth anatomy of typical queries in various benchmarks and qualitatively analyze the energy profile of such queries. The results of extensive experiments show that power savings in the range of 11% to 22% can be achieved by equipping the DBMS with a simple query optimizer that selects query plans based on both estimated processing time and energy requirements. We advocate more research efforts be invested into the design and evaluation of power-aware DBMSs in hope to reach higher level of energy efficiency.

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Järvelin, Kalervo. "User charge estimation in numeric online databases a methodology /." Tampere : University of Tampere, 1986. http://catalog.hathitrust.org/api/volumes/oclc/18665006.html.

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Candell, Richard. "Performance Estimation, Testing, and Control of Cyber-Physical Systems Employing Non-ideal Communications Networks." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCK017.

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La technologie sans fil est un catalyseur clé des promesses de l’industrie 4.0 (fabrication intelligente). En tant que telle, la technologie sans fil sera adoptée comme mode de communication principal au sein de l’usine en général et dans les unités de production en particulier. La communication des unités de production en usine a des exigences particulières en matière de latence, de fiabilité, d’échelle et de sécurité qui doivent d’abord être satisfaites par la technologie de communication sans fil utilisée. Le sans fil est considéré comme une forme de communication non idéale dans la mesure où par rapport aux communications câblées, il est considéré comme moins fiable (avec perte) et moins sécurisé. Ces dégradations possibles entraînent un retard et une perte de données dans un système d’automatisation industrielle où le déterminisme, la sécurité et la sûreté sont considérés comme primordiaux. Cette thèse étudie les exigences d’une communication sans fil dans les unités de production et l’applicabilité de la technologie sans fil existante dans ce domaine. Elle présente une modélisation SysML de l’architecture du système et des flux de données. Elle fournit une méthode d’utilisation des bases de données de type graphe pour l’organisation et l’analyse des données de performance collectées à partir d’un environnement de test. Enfin, la thèse décrit une approche utilisant l’apprentissage automatique pour l’évaluation des performances d’un système d’objets connectés dans le domaine de fabrication
Wireless technology is a key enabler of the promises of Industry 4.0 (Smart Manufacturing). As such, wireless technology will be adopted as a principal mode of communication within the factory beginning with the factory enterprise and eventually being adopted for use within the factory workcell. Factory workcell communication has particular requirements on latency, reliability, scale, and security that must first be met by the wireless communication technology used. Wireless is considered a non-ideal form of communication in that when compared to its wired counterparts, it is considered less reliable (lossy) and less secure. These possible impairments lead to delay and loss of data in industrial automation system where determinism, security, and safety is considered paramount. This thesis investigates the wireless requirements of the factory workcell and applicability of existing wireless technology, it presents a modeling approach to discovery of architecture and data flows using SysML, it provides a method for the use of graph databases to the organization and analysis of performance data collected from a testbed environment, and finally provides an approach to using machine learning in the evaluation of cyberphysical system performance

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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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Shah, Dhrumilkumar. "Revisiting Statistical Techniques for Result Cardinality Estimation." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5163.

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The Relational Database Management Systems (RDBMS) constitute the backbone of today's information-rich society, providing a congenial environment for handling enterprise data during its entire life cycle of generation, storage, maintenance, and processing. The Structured Query Language (SQL) is the standard interface to query the information present in RDBMS-based storage. Knowing the expected size of the SQL query result, measured in terms of the output row-cardinality, prior to execution can benefit both the RDBMS system and the user in several ways. The use-cases include assessing query feasibility, approximate query answering, query progress monitoring, and resource allocation strategies. In the context of our work, we define cardinality estimation as the estimation of the result size (number of rows in the output) of the given SQL query. Unfortunately, the histogram and sampling-based techniques commonly used in industrial database engines for cardinality estimation are often woefully inaccurate in practice. This lacuna has motivated a recent slew of papers advocating the use of machine-learning/deep-learning techniques for cardinality estimation. However, these new approaches have their own limitations regarding significant training effort, inability to handle dynamic data-updates, and generalization to unseen queries. In this work, we take a relook at the traditional random sampling and investigate whether they can be made to work satisfactorily when augmented with lightweight data structures. Specifically, we present GridSam – a Grid-based Dynamic Sampling technique, which essentially augments random sampling with histograms, incorporating both algorithmic and platform innovations. From the algorithmic perspective, GridSam first creates a multi-dimensional grid overlay by partitioning the data-space on “critical” attributes, and then performs dynamic sampling from the confined query-specific region of the grid to capture correlations. A greedy methodology targeted towards reducing the Zero Sample Problem occurrence is used to determine the set of “critical” attributes as the grid dimensions. Further, insights from the Index-based Join Sampling (IBJS) technique are leveraged to direct the sampling in multi-table queries. Finally, learned-indexes are incorporated to reduce the index-probing cost for join sampling during the estimation process. From the platform perspective, GridSam leverages the massive parallelism offered by current GPU architectures to provide fast grid setup times. This parallelism is also extended to the run-time estimation process. A detailed performance study on benchmark environments indicates that GridSam computes cardinality estimates with accuracies competitive to contemporary learning-based techniques. Moreover, it does so while achieving an orders-of-magnitude reduction in setup time. Further, the estimation time is in the same ballpark as both traditional and learning-based techniques. Finally, a collateral benefit of GridSam's simple and highly parallelizable design is that, unlike learned estimators, it is amenable to dynamic data environments with frequent data-updates.

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Poosala, Viswanath. "Histogram-based estimation techniques in database systems." 1997. http://catalog.hathitrust.org/api/volumes/oclc/37585530.html.

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Thesis (Ph. D.)--University of Wisconsin--Madison, 1997.
Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 209-220).

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Books on the topic "Cardinality Estimation in Database Systems"

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

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Branch, Australasian Institute of Mining and Metallurgy Illawarra. The resource database towards 2000: Towards 2000 AusIMM mineral resources and ore reserves estimation seminars. Wollongong]: The AusIMM Illawarra Branch, 1997.

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Mete, Celik, and SpringerLink (Online service), eds. Spatial AutoRegression (SAR) Model: Parameter Estimation Techniques. Boston, MA: Springer US, 2012.

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1935-, Lasker G. E., International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics and Cybernetics (13th : 2001 : Baden-Baden, Germany), eds. Advances in database and expert systems: Data mining and data warehousing techniques, similarity search for reusable database components, performance assessment of learning algorithms, estimation of models for expert systems, complexity evaluation of software processes, multi-agent systems, multi-agent approach to coalition formation, communication between software agents in distributed information systems, expert systems for fault diagnosis, parameter modulated fractals generators, information management in Intranet and Extranet environments. Windsor, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2001.

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Book chapters on the topic "Cardinality Estimation in Database Systems"

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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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Manegold, Stefan. "Cost Estimation." In Encyclopedia of Database Systems, 1–7. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_857-2.

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Pitoura, Evaggelia. "Selectivity Estimation." In Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_862-2.

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Manegold, Stefan. "Cost Estimation." In Encyclopedia of Database Systems, 506–11. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_857.

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Pitoura, Evaggelia. "Selectivity Estimation." In Encyclopedia of Database Systems, 2548. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_862.

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Manegold, Stefan. "Cost Estimation." In Encyclopedia of Database Systems, 647–53. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_857.

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Pitoura, Evaggelia. "Selectivity Estimation." In Encyclopedia of Database Systems, 3371–72. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_862.

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Ramanath, Maya, Juliana Freire, and Neoklis Polyzotis. "XML Selectivity Estimation." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_801-2.

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Ramanath, Maya, Juliana Freire, and Neoklis Polyzotis. "XML Selectivity Estimation." In Encyclopedia of Database Systems, 3623–27. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_801.

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Kollios, George. "Spatiotemporal Selectivity Estimation." In Encyclopedia of Database Systems, 3650–53. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_363.

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Conference papers on the topic "Cardinality Estimation in Database Systems"

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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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Absalyamov, Ildar, Michael J. Carey, and Vassilis J. Tsotras. "Lightweight Cardinality Estimation in LSM-based Systems." In SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3183713.3183761.

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Qian, Chen, Hoilun Ngan, and Yunhao Liu. "Cardinality Estimation for Large-scale RFID Systems." In 2008 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 2008. http://dx.doi.org/10.1109/percom.2008.77.

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Shah-Mansouri, Vahid, and Vincent W. S. Wong. "Anonymous Cardinality Estimation in RFID Systems with Multiple Readers." In GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference. IEEE, 2009. http://dx.doi.org/10.1109/glocom.2009.5425505.

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Zheng, Yuanqing, and Mo Li. "ZOE: Fast cardinality estimation for large-scale RFID systems." In IEEE INFOCOM 2013 - IEEE Conference on Computer Communications. IEEE, 2013. http://dx.doi.org/10.1109/infcom.2013.6566879.

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Zhou, Zeyu, and Bruce Hajek. "Per-Flow Cardinality Estimation Based On Virtual LogLog Sketching." In 2019 53rd Annual Conference on Information Sciences and Systems (CISS). IEEE, 2019. http://dx.doi.org/10.1109/ciss.2019.8692884.

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Li, Binbin, Yuan He, and Wenyuan Liu. "Towards Constant-Time Cardinality Estimation for Large-Scale RFID Systems." In 2015 44th International Conference on Parallel Processing (ICPP). IEEE, 2015. http://dx.doi.org/10.1109/icpp.2015.90.

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Hou, Yuxiao, Jiajue Ou, Yuanqing Zheng, and Mo Li. "PLACE: Physical layer cardinality estimation for large-scale RFID systems." In IEEE INFOCOM 2015 - IEEE Conference on Computer Communications. IEEE, 2015. http://dx.doi.org/10.1109/infocom.2015.7218579.

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Jiang, Wenchao, and Yanmin Zhu. "A Unified Approach for Fast and Accurate Cardinality Estimation in RFID Systems." In 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2014. http://dx.doi.org/10.1109/mass.2014.34.

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Ozmen, Oguzhan, Kenneth Salem, Mustafa Uysal, and M. Hossein Sheikh Attar. "Storage workload estimation for database management systems." In the 2007 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1247480.1247523.

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Reports on the topic "Cardinality Estimation in Database Systems"

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Chien, Stanley, Yaobin Chen, Lauren Christopher, Mei Qiu, and Zhengming Ding. Road Condition Detection and Classification from Existing CCTV Feed. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317364.

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The Indiana Department of Transportation (INDOT) has approximately 500 digital cameras along highways in populated areas of Indiana. These cameras are used to monitor traffic conditions around the clock, all year round. Currently, the videos from these cameras are observed one-by-one by human operators looking for traffic conditions and incidents. The main objective of this research was to develop an automatic, real-time system to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the Traffic Management Center of INDOT developed a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and classification of vehicles involved in an incident. The research team designed the system, including the hardware and software components added to the existing INDOT CCTV system; the relationship between the added system and the currently existing INDOT system; the database structure for traffic data extracted from the videos; and a user-friendly, web-based server for showing the incident locations automatically. The specific work in this project includes vehicle-detection, road boundary detection, lane detection, vehicle count over time, flow-rate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The preliminary prototype of some system components has been implemented in the Development of Automated Incident Detection System Using Existing ATMS CCT (SPR-4305).

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Chien, Stanley, Lauren Christopher, Yaobin Chen, Mei Qiu, and Wei Lin. Integration of Lane-Specific Traffic Data Generated from Real-Time CCTV Videos into INDOT's Traffic Management System. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317400.

Full text

Abstract:

The Indiana Department of Transportation (INDOT) uses about 600 digital cameras along populated Indiana highways in order to monitor highway traffic conditions. The videos from these cameras are currently observed by human operators looking for traffic conditions and incidents. However, it is time-consuming for the operators to scan through all video data from all the cameras in real-time. The main objective of this research was to develop an automatic and real-time system and implement the system at INDOT to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the INDOT Traffic Management Center have worked together to research and develop a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and the classification of vehicles involved in an incident. The goal was to develop a system and prepare for future implementation. The research team designed the new system, including the hardware and software components, the currently existing INDOT CCTV system, the database structure for traffic data extracted from the videos, and a user-friendly web-based server for identifying individual lanes on the highway and showing vehicle flowrates of each lane automatically. The preliminary prototype of some system components was implemented in the 2018–2019 JTRP projects, which provided the feasibility and structure of the automatic traffic status extraction from the video feeds. The 2019–2021 JTRP project focused on developing and improving many features’ functionality and computation speed to make the program run in real-time. The specific work in this 2021–2022 JTRP project is to improve the system further and implement it on INDOT’s premises. The system has the following features: vehicle-detection, road boundary detection, lane detection, vehicle count and flowrate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The research team has installed the system on one computer in INDOT for daily road traffic monitoring operations.

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