Gotowa bibliografia na temat „Stream Processing System”
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Artykuły w czasopismach na temat "Stream Processing System"
Shuiying Yu, Shuiying Yu, Yinting Zheng Shuiying Yu, Fan Zhang Yinting Zheng, Hanhua Chen Fan Zhang i 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, nr 2 (marzec 2023): 475–85. http://dx.doi.org/10.53106/160792642023032402024.
Pełny tekst źródłaShi, Peng, i Li Li. "Design of Network Analysis System Based on Stream Computing". Journal of Computational and Theoretical Nanoscience 14, nr 1 (1.01.2017): 64–68. http://dx.doi.org/10.1166/jctn.2017.6125.
Pełny tekst źródłaBernardelli de Moraes, Matheus, i 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, nr 2 (19.05.2020): 16–27. http://dx.doi.org/10.5335/rbca.v12i2.10295.
Pełny tekst źródłaValeev, S. S., N. V. Kondratyeva, A. S. Kovtunenko, M. A. Timirov i R. R. Karimov. "Distributed stream data processing system in multi-agent safety system of infrastructure objects". Information Technology and Nanotechnology, nr 2416 (2019): 324–31. http://dx.doi.org/10.18287/1613-0073-2019-2416-324-331.
Pełny tekst źródłaYe, Qian, i Minyan Lu. "s2p: Provenance Research for Stream Processing System". Applied Sciences 11, nr 12 (15.06.2021): 5523. http://dx.doi.org/10.3390/app11125523.
Pełny tekst źródłaAl Jawarneh, Isam Mashhour, Paolo Bellavista, Antonio Corradi, Luca Foschini i Rebecca Montanari. "QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams". Sensors 21, nr 12 (17.06.2021): 4160. http://dx.doi.org/10.3390/s21124160.
Pełny tekst źródłaLi, Huiyong, Xiaofeng Wu i Yanhong Wang. "Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory". Computational Intelligence and Neuroscience 2022 (10.04.2022): 1–13. http://dx.doi.org/10.1155/2022/8322029.
Pełny tekst źródłaAkanbi, Adeyinka, i Muthoni Masinde. "A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring". Sensors 20, nr 11 (3.06.2020): 3166. http://dx.doi.org/10.3390/s20113166.
Pełny tekst źródłaOtten, Lambert. "Wetdry composting of organic municipal solid waste: current status in Canada". Canadian Journal of Civil Engineering 28, S1 (1.01.2001): 124–30. http://dx.doi.org/10.1139/l00-072.
Pełny tekst źródłaPark, Alfred J., Cheng-Hong Li, Ravi Nair, Nobuyuki Ohba, Uzi Shvadron, Ayal Zaks i Eugen Schenfeld. "Towards flexible exascale stream processing system simulation". SIMULATION 88, nr 7 (9.08.2011): 832–51. http://dx.doi.org/10.1177/0037549711412981.
Pełny tekst źródłaRozprawy doktorskie na temat "Stream Processing System"
Wladdimiro, Cottet Daniel. "Dynamic adaptation in Stream Processing Systems". Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS028.
Pełny tekst źródłaThe amount of data produced by today’s web-based systems and applications increases rapidly, due to the many interactions with users (e.g. real-time stock market transactions, multiplayer games, streaming data produced by Twitter, etc.). As a result, there is a growing demand, particularly in the fields of commerce, security and research, for systems capable of processing this data in real time and providing useful information in a short space of time. Stream processing systems (SPS) meet these needs and have been widely used for this purpose. The aim of SPSs is to process large volumes of data in real time by housing a set of operators in applications based on Directed acyclic graphs (DAG). Most existing SPSs, such as Flink or Storm, are configured prior to deployment, usually defining the DAG and the number of operator replicas in advance. Overestimating the number of replicas can lead to a waste of allocated resources. On the other hand, depending on interaction with the environment, the rate of input data can fluctuate dynamically and, as a result, operators can become overloaded, leading to a degradation in system performance. These SPSs are not capable of dynamically adapting to operator workload and input rate variations. One solution to this problem is to dynamically increase the number of resources, physical or logical, allocated to the SPS when the processing demand of one or more operators increases. This thesis presents two SPSs, RA-SPS and PA-SPS, reactive and predictive approach respectively, for dynamically modifying the number of operator replicas. The reactive approach relies on the current state of operators computed on multiple metrics, while the predictive model is based on input rate variation, operator execution time, and queued events. The two SPSs extend Storm SPS to dynamically reconfigure the number of copies without having to downtime the application. They also implement a load balancer that distributes incoming events fairly among operator replicas. Experiments on the Google Cloud Platform (GCP) were carried out with applications that process Twitter data, DNS traffic, or logs traces. Performance was evaluated with different configurations and the results were compared with those of running the same applications on the original Storm as well as with state-of-the-art work such as SPS DABS-Storm, which also adapt the number of replicas. The comparison shows that both RA-SPS and PA-SPS can significantly improve the number of events processed, while reducing costs
Hongslo, Anders. "Stream Processing in the Robot Operating System framework". Thesis, Linköpings universitet, Artificiell intelligens och integrerad datorsystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79846.
Pełny tekst źródłaKakkad, Vasvi. "Curracurrong: a stream processing system for distributed environments". Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12861.
Pełny tekst źródłaTokmouline, Timur. "A signal oriented stream processing system for pipeline monitoring". Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37106.
Pełny tekst źródłaIncludes bibliographical references (p. 115-117).
In this thesis, we develop SignalDB, a framework for composing signal processing applications from primitive stream and signal processing operators. SignalDB allows the user to focus on the signal processing task and avoid needlessly spending time on learning a particular application programming interface (API). We use SignalDB to express acoustic and pressure transient methods for water pipeline monitoring as query plans consisting of signal processing operators.
by Timur Tokmouline.
M.Eng.
Robakowski, Mikolaj. "Comparison of State Backends for Modern Stream Processing System". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290597.
Pełny tekst źródłaDistribuerad strömbehandling är ett mycket populärt dataparadigm som användsi olika moderna datorsystem. En viktig aspekt av distribuerad strömbearbetningssystem är hur de hanterar data som är större än system minne.Detta löses ofta genom användning av en backend – en databas, vanligtvis eninbäddad, som hanterar lagringen. Detta gör dock att hela systemets prestandablir beroende av databasens prestanda för den angivna arbetsbelastningen.Loggstrukturerad merge-tree-baserade lösningar används ofta i strömbehandlingssystemsom en backend för alla typer av belastningar. Vi postulerar attanvända olika backends för olika arbetsbelastningar ger mycket bättre prestanda.I det här arbetet implementerar vi flera backends för Arcon, en modernströmbehandlings runtime skriven i Rust och utvecklad vid KTH. Avhandlingengår över implementeringsprocessen och gränssnittet för backends med flerakonkreta implementationer. Vi utvärderar experimentellt implementationernamot varandra och visar att vissa presterar bättre än andra beroende på arbetsbelastningen.I synnerhet visar vi att under läs-tungt arbete, så ser vi att sled,en inbäddad Bw-Tree databas skriven i Rust presterar bättre än den vanligaLSM-baserade RocksDB.
Mousavi, Bamdad. "Scalable Stream Processing and Management for Time Series Data". Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42295.
Pełny tekst źródłaBalazinska, 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.
Pełny tekst źródłaThis 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.
Ahmed, Abdulbasit. "Online network intrusion detection system using temporal logic and stream data processing". Thesis, University of Liverpool, 2013. http://livrepository.liverpool.ac.uk/12153/.
Pełny tekst źródłaAl-Sinayyid, Ali. "JOB SCHEDULING FOR STREAMING APPLICATIONS IN HETEROGENEOUS DISTRIBUTED PROCESSING SYSTEMS". OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1868.
Pełny tekst źródłaAddimando, Alessio. "Progettazione di un intrusion detection system su piattaforma big data". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16755/.
Pełny tekst źródłaKsiążki na temat "Stream Processing System"
Fredericks, Jeffrey W. Decision support system for conjunctive stream-aquifer management. Fort Collins, Colo: Colorado Water Resources Research Institute, 1995.
Znajdź pełny tekst źródłaBabbitt, Ronald E. Improved streamflow and water quality monitoring using a microprocessor-based system. Ogden, UT]: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1987.
Znajdź pełny tekst źródłaBabbitt, Ronald E. Improved streamflow and water quality monitoring using a microprocessor-based system. [Ogden, UT]: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1987.
Znajdź pełny tekst źródłaDatacasting: How to stream databases over the Internet. New York: McGraw-Hill, 1998.
Znajdź pełny tekst źródłaIFIP World Computer Congress (17th 2002 Montreál, Québec). Intelligent information processing: IFIP 17th World Computer Congress-TC12 stream on intelligent information processing, August 25-30, 2002, Montreál, Québec, Canada. Boston: Kluwer Academic, 2002.
Znajdź pełny tekst źródłaEric, Yen, i ebrary Inc, red. Oracle 11g Streams implementer's guide: Design, implement, and maintain a distributed environment with Oracle Streams. Birmingham, U.K: Packt Pub., 2010.
Znajdź pełny tekst źródłaCorporation, International Business Machines, red. IBM Infosphere Streams harnessing data in motion. [S.l.]: Vervante, 2010.
Znajdź pełny tekst źródłaLane, Norman E. Users manual for the Automated Performance Test System (APTS). Orlando, FL: Essex Corp., 1990.
Znajdź pełny tekst źródłaA, Hemstrom Miles, i Pacific Northwest Research Station (Portland, Or.), red. Midscale analysis of streamside characteristics in the upper Grande Ronde subbasin, northeastern Oregon. Portland, OR: U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2002.
Znajdź pełny tekst źródłaKrivoyekov, Syergyey, i Roman Ayzman. Psychophysiology. ru: INFRA-M Academic Publishing LLC., 2015. http://dx.doi.org/10.12737/10884.
Pełny tekst źródłaCzęści książek na temat "Stream Processing System"
Gorawski, Marcin, Pawel Marks i Michal Gorawski. "Modeling Data Stream Intensity in Distributed Stream Processing System". W Computer Networks, 372–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38865-1_38.
Pełny tekst źródłaGilani, Altaf, Satyajeet Sonune, Balakumar Kendai i Sharma Chakravarthy. "The Anatomy of a Stream Processing System". W Flexible and Efficient Information Handling, 232–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11788911_20.
Pełny tekst źródłaLe, Jia-jin, i Jian-wei Liu. "DDSQP: A WSRF-Based Distributed Data Stream Query System". W Parallel and Distributed Processing and Applications, 833–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11576235_83.
Pełny tekst źródłaZrilic, Djuro G. "A Δ-Σ Digital Amplitude Modulation System". W Functional Processing of Delta-Sigma Bit-Stream, 75–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47648-9_8.
Pełny tekst źródłaNishii, Shunsuke, i Toyotaro Suzumura. "Highly Scalable Speech Processing on Data Stream Management System". W Database Systems for Advanced Applications, 203–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29035-0_14.
Pełny tekst źródłaWang, Xiaotong, Junhua Fang, Yuming Li, Rong Zhang i Aoying Zhou. "Cost-Effective Data Partition for Distributed Stream Processing System". W Database Systems for Advanced Applications, 623–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55699-4_39.
Pełny tekst źródłaZrilic, Djuro G. "A Δ-Σ Digital Stereo Multiplexing–Demultiplexing System". W Functional Processing of Delta-Sigma Bit-Stream, 67–74. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47648-9_7.
Pełny tekst źródłaZou, Beiji, Tao Zhang, Chengzhang Zhu, Ling Xiao, Meng Zeng i Zhi Chen. "Alps: An Adaptive Load Partitioning Scaling Solution for Stream Processing System on Skewed Stream". W Lecture Notes in Computer Science, 17–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12426-6_2.
Pełny tekst źródłaChakravarthy, Sharma, i Qingchun Jiang. "NFMi: AN INTER-DOMAIN NETWORK FAULT MANAGEMENT SYSTEM". W Stream Data Processing: A Quality of Service Perspective, 167–86. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-71003-7_8.
Pełny tekst źródłaJiang, Jiawei, Zhipeng Zhang, Bin Cui, Yunhai Tong i Ning Xu. "StroMAX: Partitioning-Based Scheduler for Real-Time Stream Processing System". W Database Systems for Advanced Applications, 269–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55699-4_17.
Pełny tekst źródłaStreszczenia konferencji na temat "Stream Processing System"
Park, Alfred J., Cheng-Hong Li, Ravi Nair, Nobuyuki Ohba, Uzi Shvadron, Ayal Zaks i Eugen Schenfeld. "Flow: A Stream Processing System Simulator". W 2010 IEEE 24th Workshop on Principles of Advanced and Distributed Simulation (PADS 2010). IEEE, 2010. http://dx.doi.org/10.1109/pads.2010.5471658.
Pełny tekst źródłaLee, Myungcheol, Miyoung Lee, Sung Jin Hur i Ikkyun Kim. "Load adaptive distributed stream processing system for explosive stream data". W 2015 17th International Conference on Advanced Communication Technology (ICACT). IEEE, 2015. http://dx.doi.org/10.1109/icact.2015.7224896.
Pełny tekst źródłaKwon, Oje, Yong-Soo Song, Jae-Hun Kim i Ki-Joune Li. "SCONSTREAM: A Spatial Context Stream Processing System". W 2010 International Conference on Computational Science and Its Applications. IEEE, 2010. http://dx.doi.org/10.1109/iccsa.2010.48.
Pełny tekst źródłaKörber, Michael, Jakob Eckstein, Nikolaus Glombiewski i Bernhard Seeger. "Event Stream Processing on Heterogeneous System Architecture". W the 15th International Workshop. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3329785.3329933.
Pełny tekst źródłaMichalak, Peter, i Paul Watson. "PATH2iot: A Holistic, Distributed Stream Processing System". W 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2017. http://dx.doi.org/10.1109/cloudcom.2017.35.
Pełny tekst źródłaWladdimiro, Daniel, Luciana Arantes, Pierre Sens i Nicolas Hidalgo. "A Multi-Metric Adaptive Stream Processing System". W 2021 IEEE 20th International Symposium on Network Computing and Applications (NCA). IEEE, 2021. http://dx.doi.org/10.1109/nca53618.2021.9685871.
Pełny tekst źródła"ITAIPU DATA STREAM MANAGEMENT SYSTEM - A Stream Processing System with Business Users in Mind". W 3rd International Conference on Software and Data Technologies. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001882000540064.
Pełny tekst źródłaWang, Shengxiang, Hansheng Lu, Zhiyun Gao i Shanfeng Hou. "Multifunctional video stream processing system based on DSP". W Photonics Asia 2002, redaktorzy LiWei Zhou, Chung-Sheng Li i Yoshiji Suzuki. SPIE, 2002. http://dx.doi.org/10.1117/12.481567.
Pełny tekst źródłaAlves de Souza Ramos, Thatyene Louise, Rodrigo Silva Oliveira, Ana Paula de Carvalho, Renato Antonio Celso Ferreira i Wagner Meira Jr. "Watershed: A High Performance Distributed Stream Processing System". W 2011 23rd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). IEEE, 2011. http://dx.doi.org/10.1109/sbac-pad.2011.31.
Pełny tekst źródłaVogler, Michael, Johannes M. Schleicher, Christian Inzinger, Bernhard Nickel i Schahram Dustdar. "Non-intrusive Monitoring of Stream Processing Applications". W 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE). IEEE, 2016. http://dx.doi.org/10.1109/sose.2016.11.
Pełny tekst źródłaRaporty organizacyjne na temat "Stream Processing System"
Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li i A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, listopad 2001. http://dx.doi.org/10.32747/2001.7580664.bard.
Pełny tekst źródłaChristopher, David A., i Avihai Danon. Plant Adaptation to Light Stress: Genetic Regulatory Mechanisms. United States Department of Agriculture, maj 2004. http://dx.doi.org/10.32747/2004.7586534.bard.
Pełny tekst źródłaRon, Eliora, i Eugene Eugene Nester. Global functional genomics of plant cell transformation by agrobacterium. United States Department of Agriculture, marzec 2009. http://dx.doi.org/10.32747/2009.7695860.bard.
Pełny tekst źródłaSteffens, John C., i Eithan Harel. Polyphenol Oxidases- Expression, Assembly and Function. United States Department of Agriculture, styczeń 1995. http://dx.doi.org/10.32747/1995.7571358.bard.
Pełny tekst źródła