Literatura académica sobre el tema "Distributed Stream Processing Systems"
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Artículos de revistas sobre el tema "Distributed Stream Processing Systems"
K, Sornalakshmi. "Dynamic Operator Scaling for Distributed Stream Processing Systems for Fluctuating Streams". Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (25 de julio de 2020): 2815–21. http://dx.doi.org/10.5373/jardcs/v12sp7/20202422.
Texto completoWei, Xiaohui, Yuan Zhuang, Hongliang Li y Zhiliang Liu. "Reliable stream data processing for elastic distributed stream processing systems". Cluster Computing 23, n.º 2 (21 de mayo de 2019): 555–74. http://dx.doi.org/10.1007/s10586-019-02939-9.
Texto completoShuiying Yu, Shuiying Yu, Yinting Zheng Shuiying Yu, Fan Zhang Yinting Zheng, Hanhua Chen Fan Zhang y Hai Jin Hanhua Chen. "TriJoin: A Time-Efficient and Scalable Three-Way Distributed Stream Join System". 網際網路技術學刊 24, n.º 2 (marzo de 2023): 475–85. http://dx.doi.org/10.53106/160792642023032402024.
Texto completoShukla, Anshu y Yogesh Simmhan. "Model-driven scheduling for distributed stream processing systems". Journal of Parallel and Distributed Computing 117 (julio de 2018): 98–114. http://dx.doi.org/10.1016/j.jpdc.2018.02.003.
Texto completoBernardelli de Moraes, Matheus y André Leon Sampaio Gradvohl. "Evaluating the impact of a coordinated checkpointing in distributed data streams processing systems using discrete event simulation". Revista Brasileira de Computação Aplicada 12, n.º 2 (19 de mayo de 2020): 16–27. http://dx.doi.org/10.5335/rbca.v12i2.10295.
Texto completoTran, Tri Minh y Byung Suk Lee. "Distributed stream join query processing with semijoins". Distributed and Parallel Databases 27, n.º 3 (6 de marzo de 2010): 211–54. http://dx.doi.org/10.1007/s10619-010-7062-7.
Texto completoHildrum, Kirsten, Fred Douglis, Joel L. Wolf, Philip S. Yu, Lisa Fleischer y Akshay Katta. "Storage optimization for large-scale distributed stream-processing systems". ACM Transactions on Storage 3, n.º 4 (febrero de 2008): 1–28. http://dx.doi.org/10.1145/1326542.1326547.
Texto completoEskandari, Leila, Jason Mair, Zhiyi Huang y David Eyers. "I-Scheduler: Iterative scheduling for distributed stream processing systems". Future Generation Computer Systems 117 (abril de 2021): 219–33. http://dx.doi.org/10.1016/j.future.2020.11.011.
Texto completoLiu, Xunyun y Rajkumar Buyya. "Resource Management and Scheduling in Distributed Stream Processing Systems". ACM Computing Surveys 53, n.º 3 (5 de julio de 2020): 1–41. http://dx.doi.org/10.1145/3355399.
Texto completoShukla, Anshu, Shilpa Chaturvedi y Yogesh Simmhan. "RIoTBench: An IoT benchmark for distributed stream processing systems". Concurrency and Computation: Practice and Experience 29, n.º 21 (4 de octubre de 2017): e4257. http://dx.doi.org/10.1002/cpe.4257.
Texto completoTesis sobre el tema "Distributed Stream Processing Systems"
Vijayakumar, Nithya Nirmal. "Data management in distributed stream processing systems". [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278228.
Texto completoSource: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6093. Adviser: Beth Plale. Title from dissertation home page (viewed May 9, 2008).
Drougas, Ioannis. "Rate allocation in distributed stream processing systems". Diss., [Riverside, Calif.] : University of California, Riverside, 2008. http://proquest.umi.com/pqdweb?index=0&did=1663077971&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268240766&clientId=48051.
Texto completoIncludes abstract. Title from first page of PDF file (viewed March 10, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 93-98). Also issued in print.
Bordin, Maycon Viana. "A benchmark suite for distributed stream processing systems". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/163441.
Texto completoRecently a new application domain characterized by the continuous and low-latency processing of large volumes of data has been gaining attention. The growing number of applications of such genre has led to the creation of Stream Processing Systems (SPSs), systems that abstract the details of real-time applications from the developer. More recently, the ever increasing volumes of data to be processed gave rise to distributed SPSs. Currently there are in the market several distributed SPSs, however the existing benchmarks designed for the evaluation this kind of system covers only a few applications and workloads, while these systems have a much wider set of applications. In this work a benchmark for stream processing systems is proposed. Based on a survey of several papers with real-time and stream applications, the most used applications and areas were outlined, as well as the most used metrics in the performance evaluation of such applications. With these information the metrics of the benchmark were selected as well as a list of possible application to be part of the benchmark. Those passed through a workload characterization in order to select a diverse set of applications. To ease the evaluation of SPSs a framework was created with an API to generalize the application development and collect metrics, with the possibility of extending it to support other platforms in the future. To prove the usefulness of the benchmark, a subset of the applications were executed on Storm and Spark using the Azure Platform and the results have demonstrated the usefulness of the benchmark suite in comparing these systems.
Kakkad, Vasvi. "Curracurrong: a stream processing system for distributed environments". Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12861.
Texto completoAl-Sinayyid, Ali. "JOB SCHEDULING FOR STREAMING APPLICATIONS IN HETEROGENEOUS DISTRIBUTED PROCESSING SYSTEMS". OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1868.
Texto completoBalazinska, Magdalena. "Fault-tolerance and load management in a distributed stream processing system". Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/35287.
Texto completoThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 187-199).
Advances in monitoring technology (e.g., sensors) and an increased demand for online information processing have given rise to a new class of applications that require continuous, low-latency processing of large-volume data streams. These "stream processing applications" arise in many areas such as sensor-based environment monitoring, financial services, network monitoring, and military applications. Because traditional database management systems are ill-suited for high-volume, low-latency stream processing, new systems, called stream processing engines (SPEs), have been developed. Furthermore, because stream processing applications are inherently distributed, and because distribution can improve performance and scalability, researchers have also proposed and developed distributed SPEs. In this dissertation, we address two challenges faced by a distributed SPE: (1) faulttolerant operation in the face of node failures, network failures, and network partitions, and (2) federated load management. For fault-tolerance, we present a replication-based scheme, called Delay, Process, and Correct (DPC), that masks most node and network failures.
(cont.) When network partitions occur, DPC addresses the traditional availability-consistency trade-off by maintaining, when possible, a desired availability specified by the application or user, but eventually also delivering the correct results. While maintaining the desired availability bounds, DPC also strives to minimize the number of inaccurate results that must later be corrected. In contrast to previous proposals for fault tolerance in SPEs, DPC simultaneously supports a variety of applications that differ in their preferred trade-off between availability and consistency. For load management, we present a Bounded-Price Mechanism (BPM) that enables autonomous participants to collaboratively handle their load without individually owning the resources necessary for peak operation. BPM is based on contracts that participants negotiate offline. At runtime, participants move load only to partners with whom they have a contract and pay each other the contracted price. We show that BPM provides incentives that foster participation and leads to good system-wide load distribution. In contrast to earlier proposals based on computational economies, BPM is lightweight, enables participants to develop and exploit preferential relationships, and provides stability and predictability.
(cont.) Although motivated by stream processing, BPM is general and can be applied to any federated system. We have implemented both schemes in the Borealis distributed stream processing engine. They will be available with the next release of the system.
by Magdalena Balazinska.
Ph.D.
Bustamante, Fabián Ernesto. "The active streams approach to adaptive distributed applications and services". Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/15481.
Texto completoPenczek, Frank. "Static guarantees for coordinated components : a statically typed composition model for stream-processing networks". Thesis, University of Hertfordshire, 2012. http://hdl.handle.net/2299/9046.
Texto completoChen, Liang. "A grid-based middleware for processing distributed data streams". Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1157990530.
Texto completoSree, Kumar Sruthi. "External Streaming State Abstractions and Benchmarking". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291338.
Texto completoDistribuerad dataströmsbehandling är ett populärt forskningsområde och är ett av de lovande paradigmen för snabbare och effektivare datahantering. Applicationstate är en förstklassig medborgare i nästan alla strömbehandlingssystem. Numera är strömbearbetning per definition statlig. För en strömbehandlingsapplikation backar staten operationer som aggregeringar, sammanfogningar och windows. Apache Flink är ett av de mest accepterade och mest använda strömbehandlingssystemen i branschen. En av de främsta anledningarna till att ingenjörer väljer ApacheFlink för att skriva och distribuera kontinuerliga applikationer är dess unika kombination av flexibilitet och skalbarhet för statlig programmerbarhet, och företaget garanterar att systemet säkerställer. Apache Flinks garantier gör alltid dess tillstånd korrekt och konsekvent även när noder misslyckas eller när antalet uppgifter ändras. Flink-tillstånd kan skala upp till dess beräkningsnods hårddiskgränser genom att använda inbäddade databaser för att lagra och hämta data. I allmänna tillståndsstöd som officiellt stöds av Flink är staten dock alltid tillgänglig lokalt för att beräkna uppgifter. Även om detta gör installationen bekvämare, skapar det andra utmaningar som icke-trivial tillståndskonfiguration och felåterställning. Samtidigt måste beräkning och tillstånd vara tätt kopplade. Den här strategin leder också till överanvändning och är kontraintuitiv för statligt intensiva endast arbetsbelastningar eller beräkningsintensiva endast arbetsbelastningar. Denna avhandling undersöker en alternativ statsbackendarkitektur, FlinkNDB, som kan hantera dessa utmaningar. FlinkNDB frikopplar tillstånd och beräknar med hjälp av en distribuerad databas för att lagra tillståndet. Avhandlingen täcker utmaningarna med befintliga statliga backends och designval och den nya implementeringen av statebackend. Vi har utvärderat genomförandet av FlinkNDBagainst befintliga statliga backends som erbjuds av Apache Flink.
Libros sobre el tema "Distributed Stream Processing Systems"
J, Mullender Sape, ed. Distributed systems. New York, N.Y: ACM Press, 1989.
Buscar texto completoW, Chu Wesley, ed. Distributed systems. Dedham, MA: Artech House, 1986.
Buscar texto completoLangsford, Alwyn. Distributed systems management. Wokingham, Eng: Addison-Wesley, 1993.
Buscar texto completoCrowcroft, Jon. Open distributed systems. London: UCL Press, 1995.
Buscar texto completoOpen distributed systems. Boston: Artech House, 1995.
Buscar texto completoBal, H. E. Programming distributed systems. Summit, NJ, USA: Silicon Press, 1990.
Buscar texto completoT, Brazier F. M., Johansen D y Institute of Electrical and Electronics Engineers., eds. Distributed open systems. Los Alamitos, Calif: IEEE Computer Society Press, 1994.
Buscar texto completoEngineering, University of Sheffield Department of Automatic Control and Systems. Parallel processing & distributed systems. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1992.
Buscar texto completoDistributed systems integration. Rijswijk, the Netherlands: Cap Gemini, 1991.
Buscar texto completoKhalil, Drira, Martelli Andrea y Villemur Thierry, eds. Cooperative environments for distributed systems engineering: The distributed systems environment report. Berlin: Springer, 2001.
Buscar texto completoCapítulos de libros sobre el tema "Distributed Stream Processing Systems"
Eibel, Christopher, Christian Gulden, Wolfgang Schröder-Preikschat y Tobias Distler. "Strome: Energy-Aware Data-Stream Processing". En Distributed Applications and Interoperable Systems, 40–57. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93767-0_4.
Texto completoXia, Cathy H., James A. Broberg, Zhen Liu y Li Zhang. "Distributed Resource Allocation in Stream Processing Systems". En Lecture Notes in Computer Science, 489–504. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11864219_34.
Texto completoKuralenok, Igor E., Artem Trofimov, Nikita Marshalkin y Boris Novikov. "Deterministic Model for Distributed Speculative Stream Processing". En Advances in Databases and Information Systems, 233–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98398-1_16.
Texto completoCai, Rijun, Weigang Wu, Ning Huang y Lihui Wu. "Processing Partially Ordered Requests in Distributed Stream Processing Systems". En Algorithms and Architectures for Parallel Processing, 211–19. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49583-5_16.
Texto completoZacheilas, Nikos y Vana Kalogeraki. "DIsCO: DynamIc Data COmpression in Distributed Stream Processing Systems". En Distributed Applications and Interoperable Systems, 19–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59665-5_2.
Texto completoBattulga, Davaadorj, Daniele Miorandi y Cédric Tedeschi. "SpecK: Composition of Stream Processing Applications over Fog Environments". En Distributed Applications and Interoperable Systems, 38–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78198-9_3.
Texto completoChen, Fei, Song Wu y Hai Jin. "Network-Aware Grouping in Distributed Stream Processing Systems". En Algorithms and Architectures for Parallel Processing, 3–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05051-1_1.
Texto completoWang, Xiaotong, Cheng Jiang, Junhua Fang, Ke Shu, Rong Zhang, Weining Qian y Aoying Zhou. "Evaluating Fault Tolerance of Distributed Stream Processing Systems". En Web and Big Data, 101–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60290-1_8.
Texto completoGorawski, Marcin, Pawel Marks y Michal Gorawski. "Modeling Data Stream Intensity in Distributed Stream Processing System". En Computer Networks, 372–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38865-1_38.
Texto completoSegarra, Carlos, Ricard Delgado-Gonzalo, Mathieu Lemay, Pierre-Louis Aublin, Peter Pietzuch y Valerio Schiavoni. "Using Trusted Execution Environments for Secure Stream Processing of Medical Data". En Distributed Applications and Interoperable Systems, 91–107. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22496-7_6.
Texto completoActas de conferencias sobre el tema "Distributed Stream Processing Systems"
Drougas, Yannis y Vana Kalogeraki. "Accommodating bursts in distributed stream processing systems". En Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5161015.
Texto completoKarimov, Jeyhun, Tilmann Rabl, Asterios Katsifodimos, Roman Samarev, Henri Heiskanen y Volker Markl. "Benchmarking Distributed Stream Data Processing Systems". En 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00169.
Texto completoZvara, Zoltan, Peter G. N. Szabo, Gabor Hermann y Andras Benczur. "Tracing Distributed Data Stream Processing Systems". En 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W). IEEE, 2017. http://dx.doi.org/10.1109/fas-w.2017.153.
Texto completoPacaci, Anil y M. Tamer Özsu. "Distribution-Aware Stream Partitioning for Distributed Stream Processing Systems". En SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3206333.3206338.
Texto completoAffetti, Lorenzo. "Consistent Stream Processing". En DEBS '17: The 11th ACM International Conference on Distributed and Event-based Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3093742.3093900.
Texto completoEskandari, Leila, Jason Mair, Zhiyi Huang y David Eyers. "Iterative Scheduling for Distributed Stream Processing Systems". En DEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3210284.3219768.
Texto completoXie, Xing, Indrakshi Ray, Waruna Ranasinghe, Philips A. Gilbert, Pramod Shashidhara y Anoop Yadav. "Distributed Multilevel Secure Data Stream Processing". En 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 2013. http://dx.doi.org/10.1109/icdcsw.2013.64.
Texto completoLi, Kejian, Gang Liu y Minhua Lu. "A Holistic Stream Partitioning Algorithm for Distributed Stream Processing Systems". En 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE, 2019. http://dx.doi.org/10.1109/pdcat46702.2019.00046.
Texto completoYongluan Zhou, Karl Aberer, Ali Salehi y Kian-Lee Tan. "Rethinking the design of distributed stream processing systems". En 2008 IEEE 24th International Conference on Data Engineeing workshop (ICDE Workshop 2008). IEEE, 2008. http://dx.doi.org/10.1109/icdew.2008.4498314.
Texto completoTuraga, Deepak S., Hyunggon Park, Rong Yan y Olivier Verscheure. "Adaptive Multimedia Mining on Distributed Stream Processing Systems". En 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.159.
Texto completoInformes sobre el tema "Distributed Stream Processing Systems"
Popek, Gerald J. y Wesley W. Chu. Very Large Scale Distributed Information Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1991. http://dx.doi.org/10.21236/ada243983.
Texto completoCho, Kilseok, Alan D. George, Raj Subramaniyan y Keonwook Kim. Parallel Algorithms for Adaptive Matched-Field Processing in Distributed Array Systems. Fort Belvoir, VA: Defense Technical Information Center, enero de 2003. http://dx.doi.org/10.21236/ada465545.
Texto completoCho, Kilseok, Alan D. George y Raj Subramaniyan. Fault-Tolerant Parallel Algorithms for Adaptive Matched-Field Processing on Distributed Array Systems. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2004. http://dx.doi.org/10.21236/ada466282.
Texto completoSmith, Bradley W. Distributed Computing for Signal Processing: Modeling of Asynchronous Parallel Computation. Appendix G. On the Design and Modeling of Special Purpose Parallel Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, mayo de 1985. http://dx.doi.org/10.21236/ada167622.
Texto completoSchmitt, Harry. Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, marzo de 2006. http://dx.doi.org/10.21236/ada444037.
Texto completoSchmitt, Harry A. Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2006. http://dx.doi.org/10.21236/ada454039.
Texto completoNavathe, Shamkant B. A Knowledge-Based Approach to Integrating and Querying Distributed Information Systems Heterogeneous Intelligent Processing for Engineering Design (HIPED). Fort Belvoir, VA: Defense Technical Information Center, agosto de 1997. http://dx.doi.org/10.21236/ada341697.
Texto completoSchmitt, Harry A. Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Netowrks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, febrero de 2007. http://dx.doi.org/10.21236/ada464278.
Texto completoSchmitt, Harry A. Integrated Sensing and Processing (ISP) Phase 2: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2007. http://dx.doi.org/10.21236/ada468089.
Texto completoChristopher, David A. y Avihai Danon. Plant Adaptation to Light Stress: Genetic Regulatory Mechanisms. United States Department of Agriculture, mayo de 2004. http://dx.doi.org/10.32747/2004.7586534.bard.
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