Relevant bibliographies by topics / Distributed Event Processing

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

Academic literature on the topic 'Distributed Event Processing'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Distributed Event Processing"

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Terletska, Khrystyna. "Data Consistency in Distributed Multi-Stage Event Processing Pipelines." American Journal of Engineering and Technology 07, no. 06 (2025): 127–34. https://doi.org/10.37547/tajet/volume07issue06-14.

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The article examines the problem of ensuring end-to-end data consistency in distributed multi-stage event processing pipelines, which are actively used in modern real-time systems. The relevance of the study is determined by the rapid growth of streaming analytics needs and the widespread use of Apache Kafka, making message latency, duplication, and disorder critical factors for industries ranging from fintech to IoT. The goal of this work is to propose a formal model that unifies an extended event representation and a set of invariants that guarantee correct processing even in the presence of
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Jayasekara, Sachini, Sameera Kannangara, Tishan Dahanayakage, Isuru Ranawaka, Srinath Perera, and Vishaka Nanayakkara. "Wihidum: Distributed complex event processing." Journal of Parallel and Distributed Computing 79-80 (May 2015): 42–51. http://dx.doi.org/10.1016/j.jpdc.2015.03.002.

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Wang, Y. H., K. Cao, and X. M. Zhang. "Complex event processing over distributed probabilistic event streams." Computers & Mathematics with Applications 66, no. 10 (2013): 1808–21. http://dx.doi.org/10.1016/j.camwa.2013.06.032.

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Xuanling, Chen Yongheng Wang*. "A DISTRIBUTED COMPLEX EVENT PROCESSING METHOD BASED ON EVENT STREAM PARTITION." Global Journal of Engineering Science and Research Management 4, no. 3 (2017): 41–46. https://doi.org/10.5281/zenodo.401006.

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The CEP technology not only can extract valuable information, but also support real-time analysis and decision-making. And simple CEP technology is more to deal with a single complex event or a small amount of complex events, so we propose a distributed complex event processing method. In this paper, we propose a distributed complex event processing framework and an event partitioning strategy based on query event type and an operational distributed method for partitioning complex queries. And the experimental results show that our method is more effective.
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Zorin, Arsenij Andreevich, and Irina Evgenyevna Chernetskaya. "Deployment Approaches in Distributed Complex Event Processing." Proceedings of the Institute for System Programming of the RAS 35, no. 3 (2023): 71–82. http://dx.doi.org/10.15514/ispras-2023-35(3)-5.

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Big Data technologies have traditionally focused on processing human-generated data, while neglecting the vast amounts of data generated by Machine-to-Machine (M2M) interactions and Internet-of-Things (IoT) platforms. These interactions generate real-time data streams that are highly structured, often in the form of a series of event occurrences. In this paper, we aim to provide a comprehensive overview of the main research issues in Complex Event Processing (CEP) techniques, with a special focus on optimizing the distribution of event handlers between working nodes. We introduce and compare d
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Xiao, Fuyuan, Cheng Zhan, Hong Lai, Li Tao, and Zhiguo Qu. "New parallel processing strategies in complex event processing systems with data streams." International Journal of Distributed Sensor Networks 13, no. 8 (2017): 155014771772862. http://dx.doi.org/10.1177/1550147717728626.

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Sensor network–based application has gained increasing attention where data streams gathered from distributed sensors need to be processed and analyzed with timely responses. Distributed complex event processing is an effective technology to handle these data streams by matching of incoming events to persistent pattern queries. Therefore, a well-managed parallel processing scheme is required to improve both system performance and the quality-of-service guarantees of the system. However, the specific properties of pattern operators increase the difficulties of implementing parallel processing.
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Cugola, Gianpaolo, and Alessandro Margara. "Deployment strategies for distributed complex event processing." Computing 95, no. 2 (2012): 129–56. http://dx.doi.org/10.1007/s00607-012-0217-9.

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Akili, Samira, and Matthias Weidlich. "Reasoning on the Efficiency of Distributed Complex Event Processing." Fundamenta Informaticae 179, no. 2 (2021): 113–34. http://dx.doi.org/10.3233/fi-2021-2017.

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Complex event processing (CEP) evaluates queries over streams of event data to detect situations of interest. If the event data are produced by geographically distributed sources, CEP may exploit in-network processing that distributes the evaluation of a query among the nodes of a network. To this end, a query is modularized and individual query operators are assigned to nodes, especially those that act as data sources. Existing solutions for such operator placement, however, are limited in that they assume all query results to be gathered at one designated node, commonly referred to as a sink
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Jain, Aman, Akash Thadhani, Vaibhavi Parikh, and Akash Pati. "Advanced Event-Driven Architectures for Ultra-Low-Latency Trading: Comprehensive System Design and Performance Optimization." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2558–64. https://doi.org/10.22214/ijraset.2025.67898.

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Abstract: Financial market computational ecosystems demand unprecedented computational responsiveness, where microsecond-level performance differentiates competitive trading infrastructures. This comprehensive research presents a groundbreaking event-driven architectural paradigm designed to revolutionize high-frequency trading (HFT) computational systems through advanced distributed computing methodologies. Our investigation systematically deconstructs traditional trading system architectures, introducing a novel event processing framework that achieves:  Median event processing latency: 42
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Wallace, Richard M., Patrick Martin, and Jose Luis Vazquez-Poletti. "Regulated Condition-Event Matrices for Cloud Environments." Scalable Computing: Practice and Experience 15, no. 2 (2014): 169–85. https://doi.org/10.12694/scpe.v15i2.954.

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Distributed event-based systems (DEBS) are networks of computing devices. These systems have been successfully implemented by commercial vendors. Cloud applications depend on message passing and inter-connectivity methods exchanging data and performing inter-process communication. Both DEBS and Clouds need time-coordinated methods of control not dependent on a single time domain. While DEBS have specific implementation languages for complex events, Cloud systems do not. Clouds and DEBS have not yet presented an explicit separation of temporally based event processing from computations. Using a
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Dissertations / Theses on the topic "Distributed Event Processing"

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Brito, Andrey. "Speculation in Parallel and Distributed Event Processing Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-38911.

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Event stream processing (ESP) applications enable the real-time processing of continuous flows of data. Algorithmic trading, network monitoring, and processing data from sensor networks are good examples of applications that traditionally rely upon ESP systems. In addition, technological advances are resulting in an increasing number of devices that are network enabled, producing information that can be automatically collected and processed. This increasing availability of on-line data motivates the development of new and more sophisticated applications that require low-latency processing of l
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Zhou, Dong. "JECho - An efficient, customizable, adaptive distributed event system." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/9180.

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Liu, Guangtian. "An event service architecture in distributed real-time systems /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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O'Keeffe, Daniel Brendan. "Distributed complex event detection for pervasive computing." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609012.

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GauthierDickey, Christopher Jay. "Cheat-proof event ordering for large-scale distributed multiplayer games /." view abstract or download file of text, 2007. http://proquest.umi.com/pqdweb?did=1324389301&sid=1&Fmt=2&clientId=11238&RQT=309&VName=PQD.

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Thesis (Ph. D.)--University of Oregon, 2007.<br>Typescript. Includes vita and abstract. Includes bibliographical references (leaves 134-139). Also available for download via the World Wide Web; free to University of Oregon users.
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Riemer, Dominik [Verfasser], and R. [Akademischer Betreuer] Studer. "Methods and Tools for Management of Distributed Event Processing Applications / Dominik Riemer ; Betreuer: R. Studer." Karlsruhe : KIT-Bibliothek, 2016. http://d-nb.info/1133171826/34.

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Adhikari, Sameer. "Programming Idioms and Runtime Mechanisms for Distributed Pervasive Computing." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4820.

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The emergence of pervasive computing power and networking infrastructure is enabling new applications. Still, many milestones need to be reached before pervasive computing becomes an integral part of our lives. An important missing piece is the middleware that allows developers to easily create interesting pervasive computing applications. This dissertation explores the middleware needs of distributed pervasive applications. The main contributions of this thesis are the design, implementation, and evaluation of tw
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Wermund, Rahul [Verfasser], Ralf [Akademischer Betreuer] Steinmetz, and Bernd [Akademischer Betreuer] Freisleben. "Privacy-Aware and Reliable Complex Event Processing in the Internet of Things - Trust-Based and Flexible Execution of Event Processing Operators in Dynamic Distributed Environments / Rahul Wermund ; Ralf Steinmetz, Bernd Freisleben." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2018. http://d-nb.info/1151638897/34.

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Sanli, Ozgur. "Rule-based In-network Processing For Event-driven Applications In Wireless Sensor Networks." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613389/index.pdf.

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Wireless sensor networks are application-specific networks that necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. The most important challenge related to wireless sensor networks is the limited energy and computational resources of the battery powered sensor nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the
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Lillethun, David. "ssIoTa: A system software framework for the internet of things." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53531.

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Sensors are widely deployed in our environment, and their number is increasing rapidly. In the near future, billions of devices will all be connected to each other, creating an Internet of Things. Furthermore, computational intelligence is needed to make applications involving these devices truly exciting. In IoT, however, the vast amounts of data will not be statically prepared for batch processing, but rather continually produced and streamed live to data consumers and intelligent algorithms. We refer to applications that perform live analysis on live data streams, bringing intelligence to I
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Books on the topic "Distributed Event Processing"

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Helmer, Sven. Reasoning in Event-Based Distributed Systems. Springer-Verlag Berlin Heidelberg, 2011.

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Luckham, David C. Event processing for business: Organizing the real time strategy enterprise. Wiley, 2012.

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Inc, ebrary, ed. IBM InfoSphere replication server and data event publisher: Design, implement, and monitor a successful Q replication and event publishing project. Packt Pub., 2010.

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Guerrero, Pablo, Alejandro P. Buchmann, Ilia Petrov, and Kai Sachs. From active data management to event-based systems and more: Papers in honor of Alejandro Buchmann on the occasion of his 60th birthday. Springer, 2010.

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Kumar, P. R. Discrete Event Systems, Manufacturing Systems, and Communication Networks. Springer New York, 1995.

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Mohamed, Jmaiel, Charfi Anis, Graupner Sven, et al., eds. Service-Oriented Computing - ICSOC 2011 Workshops: ICSOC 2011, International Workshops WESOA, NFPSLAM-SOC, and Satellite Events, Paphos, Cyprus, December 5-8, 2011. Revised Selected Papers. Springer Berlin Heidelberg, 2012.

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Mühl, Gero, Ludger Fiege, and Peter Pietzuch. Distributed Event-Based Systems. Springer Berlin / Heidelberg, 2010.

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Mühl, Gero, Ludger Fiege, and Peter Pietzuch. Distributed Event-Based Systems. Springer, 2006.

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Tropper, Carl. Parallel and Distributed Discrete Event Simulation. Nova Science Publishers, 2002.

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Luckham, David C. Event Processing for Business. Wiley & Sons, Incorporated, John, 2012.

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Book chapters on the topic "Distributed Event Processing"

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Etzion, Opher, Yonit Magid, Ella Rabinovich, Inna Skarbovsky, and Nir Zolotorevsky. "Context-Based Event Processing Systems." In Reasoning in Event-Based Distributed Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19724-6_12.

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Gal, Avigdor, Segev Wasserkrug, and Opher Etzion. "Event Processing over Uncertain Data." In Reasoning in Event-Based Distributed Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19724-6_13.

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Jerzak, Zbigniew, Anja Klein, and Gregor Hackenbroich. "GINSENG Data Processing Framework." In Reasoning in Event-Based Distributed Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19724-6_6.

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Jander, Kai, Lars Braubach, and W. Lamersdorf. "Distributed Event Processing for Goal-Oriented Workflows." In Intelligent Distributed Computing VIII. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10422-5_7.

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Stefanidis, Vassilis, Yiannis Verginadis, Ioannis Patiniotakis, and Gregoris Mentzas. "Distributed Complex Event Processing in Multiclouds." In Service-Oriented and Cloud Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99819-0_8.

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Dahl, Jörgen, Malolan Chetlur, and Philip A. Wilsey. "Event List Management in Distributed Simulation." In Euro-Par 2001 Parallel Processing. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44681-8_68.

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Anicic, Darko, Paul Fodor, Sebastian Rudolph, Roland Stühmer, Nenad Stojanovic, and Rudi Studer. "ETALIS: Rule-Based Reasoning in Event Processing." In Reasoning in Event-Based Distributed Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19724-6_5.

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Eckert, Michael, François Bry, Simon Brodt, Olga Poppe, and Steffen Hausmann. "A CEP Babelfish: Languages for Complex Event Processing and Querying Surveyed." In Reasoning in Event-Based Distributed Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19724-6_3.

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Garnier, Alexandre, Jean-Marc Menaud, and Nicolas Montavont. "Bringing Complex Event Processing into Multitree Modelling of Sensors." In Distributed Applications and Interoperable Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39577-7_16.

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Li, Xuhui, Jiannong Cao, Yanxiang He, and Jingyang Zhou. "A Discrete Event System Model for Simulating Mobile Agent." In Parallel and Distributed Processing and Applications. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11576235_71.

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Conference papers on the topic "Distributed Event Processing"

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Schilling, Björn, Boris Koldehofe, Udo Pletat, and Kurt Rothermel. "Distributed heterogeneous event processing." In the Fourth ACM International Conference. ACM Press, 2010. http://dx.doi.org/10.1145/1827418.1827453.

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Wang, Yongheng, and Xiaoming Zhang. "Complex Event Processing over distributed probabilistic event streams." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234083.

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XinLong, Zhang, Wang Yongheng, and Zhang XiaoMing. "Complex Event Processing over Distributed Uncertain Event Streams." In 3rd International Conference on Computer Science and Service System. Atlantis Press, 2014. http://dx.doi.org/10.2991/csss-14.2014.84.

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Suresh, Visalakshmi, Paul Ezhilchelvan, Paul Watson, Cuong Pham, Dan Jackson, and Patrick Olivier. "Distributed event processing for activity recognition." In the 5th ACM international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2002259.2002315.

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Sturzrehm, Heiko, Pascal Felber, and Christof Fetzer. "TM-Stream: An STM framework for distributed event stream processing." In Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5161084.

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Schultz-Møller, Nicholas Poul, Matteo Migliavacca, and Peter Pietzuch. "Distributed complex event processing with query rewriting." In the Third ACM International Conference. ACM Press, 2009. http://dx.doi.org/10.1145/1619258.1619264.

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Sun, Kang, YongHeng Wang, and ShuGuang Peng. "Distributed complex event processing using rule deployment." In International Conference on Education, Management and Computing Technology (ICEMCT-15). Atlantis Press, 2015. http://dx.doi.org/10.2991/icemct-15.2015.255.

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Saleh, Omran, and Kai-Uwe Sattler. "Distributed Complex Event Processing in Sensor Networks." In 2013 14th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2013. http://dx.doi.org/10.1109/mdm.2013.60.

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Strom, R., C. Dorai, G. Buttner, and Ying Li. "SMILE - distributed middleware for event stream processing." In Sixth International Symposium on Information Processing in Sensor Networks. IEEE, 2007. http://dx.doi.org/10.1109/ipsn.2007.4379722.

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Koch, Gerald G., Muhammad Adnan Tariq, Boris Koldehofe, and Kurt Rothermel. "Event processing for large-scale distributed games." In the Fourth ACM International Conference. ACM Press, 2010. http://dx.doi.org/10.1145/1827418.1827440.

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Reports on the topic "Distributed Event Processing"

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Ishihara, T. Exploring the Use of a Reliable IP Multicast to Distribute BaBar's Online Event Processing and Filter Software to a Large Number of Farm Machines. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/826446.

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