Relevant bibliographies by topics / Distributed Stream Processing Systems

Contents

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

Academic literature on the topic 'Distributed Stream Processing Systems'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Distributed Stream Processing Systems"

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K, Sornalakshmi. "Dynamic Operator Scaling for Distributed Stream Processing Systems for Fluctuating Streams." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 2815–21. http://dx.doi.org/10.5373/jardcs/v12sp7/20202422.

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Wei, Xiaohui, Yuan Zhuang, Hongliang Li, and Zhiliang Liu. "Reliable stream data processing for elastic distributed stream processing systems." Cluster Computing 23, no. 2 (2019): 555–74. http://dx.doi.org/10.1007/s10586-019-02939-9.

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Shuiying Yu, Shuiying Yu, Yinting Zheng Shuiying Yu, Fan Zhang Yinting Zheng, Hanhua Chen Fan Zhang, and 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, no. 2 (2023): 475–85. http://dx.doi.org/10.53106/160792642023032402024.

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<p>Stream join is one of the most fundamental operations in data stream processing applications. Existing distributed stream join systems can support efficient two-way join, which is a join operation between two streams. Based the two-way join, implementing a three-way join require to be split into double two-way joins, where the second two-way join needs to wait for the join result transmitted from the first two-way join. We show through experiments that such a design raises prohibitively high processing latency. To solve this problem, we propose TriJoin, a time-efficient three-way dist
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Shukla, Anshu, and Yogesh Simmhan. "Model-driven scheduling for distributed stream processing systems." Journal of Parallel and Distributed Computing 117 (July 2018): 98–114. http://dx.doi.org/10.1016/j.jpdc.2018.02.003.

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Bernardelli de Moraes, Matheus, and 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, no. 2 (2020): 16–27. http://dx.doi.org/10.5335/rbca.v12i2.10295.

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Data Streams Processing systems process continuous flows of data under Quality of Service requirements. Data streams often contain critical information which requires real-time processing. To guarantee systems' dependability and avoid information loss, one must use a fault-tolerance strategy. However, there are several strategies available, and the proper evaluation of which mechanism is better for each system architecture is challenging, especially in large-scale distributed systems. In this paper, we propose a discrete simulation model for investigating the impacts of the Coordinated Checkpo
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Tran, Tri Minh, and Byung Suk Lee. "Distributed stream join query processing with semijoins." Distributed and Parallel Databases 27, no. 3 (2010): 211–54. http://dx.doi.org/10.1007/s10619-010-7062-7.

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Hildrum, Kirsten, Fred Douglis, Joel L. Wolf, Philip S. Yu, Lisa Fleischer, and Akshay Katta. "Storage optimization for large-scale distributed stream-processing systems." ACM Transactions on Storage 3, no. 4 (2008): 1–28. http://dx.doi.org/10.1145/1326542.1326547.

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Eskandari, Leila, Jason Mair, Zhiyi Huang, and David Eyers. "I-Scheduler: Iterative scheduling for distributed stream processing systems." Future Generation Computer Systems 117 (April 2021): 219–33. http://dx.doi.org/10.1016/j.future.2020.11.011.

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Liu, Xunyun, and Rajkumar Buyya. "Resource Management and Scheduling in Distributed Stream Processing Systems." ACM Computing Surveys 53, no. 3 (2020): 1–41. http://dx.doi.org/10.1145/3355399.

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Shukla, Anshu, Shilpa Chaturvedi, and Yogesh Simmhan. "RIoTBench: An IoT benchmark for distributed stream processing systems." Concurrency and Computation: Practice and Experience 29, no. 21 (2017): e4257. http://dx.doi.org/10.1002/cpe.4257.

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Dissertations / Theses on the topic "Distributed Stream Processing Systems"

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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.

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Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2007.<br>Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6093. Adviser: Beth Plale. Title from dissertation home page (viewed May 9, 2008).
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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.

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Thesis (Ph. D.)--University of California, Riverside, 2008.<br>Includes 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.
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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.

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Um dado por si só não possui valor algum, a menos que ele seja interpretado, contextualizado e agregado com outros dados, para então possuir valor, tornando-o uma informação. Em algumas classes de aplicações o valor não está apenas na informação, mas também na velocidade com que essa informação é obtida. As negociações de alta frequência (NAF) são um bom exemplo onde a lucratividade é diretamente proporcional a latência (LOVELESS; STOIKOV; WAEBER, 2013). Com a evolução do hardware e de ferramentas de processamento de dados diversas aplicações que antes levavam horas para produzir resultados, h
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Kakkad, Vasvi. "Curracurrong: a stream processing system for distributed environments." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12861.

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Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process them to generate a new sequences of events. Such applications need an adequate programming model that can process large amount of data with minimal latency; for this purpose, stream programming, among other paradigms, is ide
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Al-Sinayyid, Ali. "JOB SCHEDULING FOR STREAMING APPLICATIONS IN HETEROGENEOUS DISTRIBUTED PROCESSING SYSTEMS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1868.

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The colossal amounts of data generated daily are increasing exponentially at a never-before-seen pace. A variety of applications—including stock trading, banking systems, health-care, Internet of Things (IoT), and social media networks, among others—have created an unprecedented volume of real-time stream data estimated to reach billions of terabytes in the near future. As a result, we are currently living in the so-called Big Data era and witnessing a transition to the so-called IoT era. Enterprises and organizations are tackling the challenge of interpreting the enormous amount of raw data
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Balazinska, 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.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Includes bibliographical references (p. 187-199).<br>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 application
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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.

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Penczek, 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.

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Does your program do what it is supposed to be doing? Without running the program providing an answer to this question is much harder if the language does not support static type checking. Of course, even if compile-time checks are in place only certain errors will be detected: compilers can only second-guess the programmer’s intention. But, type based techniques go a long way in assisting programmers to detect errors in their computations earlier on. The question if a program behaves correctly is even harder to answer if the program consists of several parts that execute concurrently and need
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Chen, 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.

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Sree, 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.

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Distributed data stream processing is a popular research area and is one of the promising paradigms for faster and efficient data management. Application state is a first-class citizen in nearly every stream processing system. Nowadays, stream processing is, by definition, stateful. For a stream processing application, the state is backing operations such as aggregations, joins, and windows. Apache Flink is one of the most accepted and widely used stream processing systems in the industry. One of the main reasons engineers choose Apache Flink to write and deploy continuous applications is its
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Books on the topic "Distributed Stream Processing Systems"

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J, Mullender Sape, ed. Distributed systems. ACM Press, 1989.

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W, Chu Wesley, ed. Distributed systems. Artech House, 1986.

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Langsford, Alwyn. Distributed systems management. Addison-Wesley, 1993.

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Crowcroft, Jon. Open distributed systems. UCL Press, 1995.

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Open distributed systems. Artech House, 1995.

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Bal, H. E. Programming distributed systems. Silicon Press, 1990.

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T, Brazier F. M., Johansen D, and Institute of Electrical and Electronics Engineers., eds. Distributed open systems. IEEE Computer Society Press, 1994.

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Engineering, University of Sheffield Department of Automatic Control and Systems. Parallel processing & distributed systems. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1992.

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Distributed systems integration. Cap Gemini, 1991.

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Khalil, Drira, Martelli Andrea, and Villemur Thierry, eds. Cooperative environments for distributed systems engineering: The distributed systems environment report. Springer, 2001.

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Book chapters on the topic "Distributed Stream Processing Systems"

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Eibel, Christopher, Christian Gulden, Wolfgang Schröder-Preikschat, and Tobias Distler. "Strome: Energy-Aware Data-Stream Processing." In Distributed Applications and Interoperable Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93767-0_4.

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Xia, Cathy H., James A. Broberg, Zhen Liu, and Li Zhang. "Distributed Resource Allocation in Stream Processing Systems." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11864219_34.

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Kuralenok, Igor E., Artem Trofimov, Nikita Marshalkin, and Boris Novikov. "Deterministic Model for Distributed Speculative Stream Processing." In Advances in Databases and Information Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98398-1_16.

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Cai, Rijun, Weigang Wu, Ning Huang, and Lihui Wu. "Processing Partially Ordered Requests in Distributed Stream Processing Systems." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49583-5_16.

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Zacheilas, Nikos, and Vana Kalogeraki. "DIsCO: DynamIc Data COmpression in Distributed Stream Processing Systems." In Distributed Applications and Interoperable Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59665-5_2.

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Battulga, Davaadorj, Daniele Miorandi, and Cédric Tedeschi. "SpecK: Composition of Stream Processing Applications over Fog Environments." In Distributed Applications and Interoperable Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78198-9_3.

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Chen, Fei, Song Wu, and Hai Jin. "Network-Aware Grouping in Distributed Stream Processing Systems." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05051-1_1.

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Wang, Xiaotong, Cheng Jiang, Junhua Fang, et al. "Evaluating Fault Tolerance of Distributed Stream Processing Systems." In Web and Big Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60290-1_8.

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Gorawski, Marcin, Pawel Marks, and Michal Gorawski. "Modeling Data Stream Intensity in Distributed Stream Processing System." In Computer Networks. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38865-1_38.

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Segarra, Carlos, Ricard Delgado-Gonzalo, Mathieu Lemay, Pierre-Louis Aublin, Peter Pietzuch, and Valerio Schiavoni. "Using Trusted Execution Environments for Secure Stream Processing of Medical Data." In Distributed Applications and Interoperable Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22496-7_6.

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Conference papers on the topic "Distributed Stream Processing Systems"

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Drougas, Yannis, and Vana Kalogeraki. "Accommodating bursts in distributed stream processing systems." In Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5161015.

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Karimov, Jeyhun, Tilmann Rabl, Asterios Katsifodimos, Roman Samarev, Henri Heiskanen, and Volker Markl. "Benchmarking Distributed Stream Data Processing Systems." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00169.

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Zvara, Zoltan, Peter G. N. Szabo, Gabor Hermann, and Andras Benczur. "Tracing Distributed Data Stream Processing Systems." In 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.

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Pacaci, Anil, and M. Tamer Özsu. "Distribution-Aware Stream Partitioning for Distributed Stream Processing Systems." In SIGMOD/PODS '18: International Conference on Management of Data. ACM, 2018. http://dx.doi.org/10.1145/3206333.3206338.

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Affetti, Lorenzo. "Consistent Stream Processing." In DEBS '17: The 11th ACM International Conference on Distributed and Event-based Systems. ACM, 2017. http://dx.doi.org/10.1145/3093742.3093900.

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Eskandari, Leila, Jason Mair, Zhiyi Huang, and David Eyers. "Iterative Scheduling for Distributed Stream Processing Systems." In DEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems. ACM, 2018. http://dx.doi.org/10.1145/3210284.3219768.

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Xie, Xing, Indrakshi Ray, Waruna Ranasinghe, Philips A. Gilbert, Pramod Shashidhara, and Anoop Yadav. "Distributed Multilevel Secure Data Stream Processing." In 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 2013. http://dx.doi.org/10.1109/icdcsw.2013.64.

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Li, Kejian, Gang Liu, and Minhua Lu. "A Holistic Stream Partitioning Algorithm for Distributed Stream Processing Systems." In 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE, 2019. http://dx.doi.org/10.1109/pdcat46702.2019.00046.

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Yongluan Zhou, Karl Aberer, Ali Salehi, and Kian-Lee Tan. "Rethinking the design of distributed stream processing systems." In 2008 IEEE 24th International Conference on Data Engineeing workshop (ICDE Workshop 2008). IEEE, 2008. http://dx.doi.org/10.1109/icdew.2008.4498314.

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Turaga, Deepak S., Hyunggon Park, Rong Yan, and Olivier Verscheure. "Adaptive Multimedia Mining on Distributed Stream Processing Systems." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.159.

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Reports on the topic "Distributed Stream Processing Systems"

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Popek, Gerald J., and Wesley W. Chu. Very Large Scale Distributed Information Processing Systems. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada243983.

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Cho, Kilseok, Alan D. George, Raj Subramaniyan, and Keonwook Kim. Parallel Algorithms for Adaptive Matched-Field Processing in Distributed Array Systems. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada465545.

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Cho, Kilseok, Alan D. George, and Raj Subramaniyan. Fault-Tolerant Parallel Algorithms for Adaptive Matched-Field Processing on Distributed Array Systems. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada466282.

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Smith, 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. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada167622.

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Schmitt, Harry. Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada444037.

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Schmitt, Harry A. Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada454039.

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Navathe, Shamkant B. A Knowledge-Based Approach to Integrating and Querying Distributed Information Systems Heterogeneous Intelligent Processing for Engineering Design (HIPED). Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada341697.

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Schmitt, Harry A. Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Netowrks and Missile Seeker Systems. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada464278.

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Schmitt, Harry A. Integrated Sensing and Processing (ISP) Phase 2: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada468089.

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Christopher, David A., and Avihai Danon. Plant Adaptation to Light Stress: Genetic Regulatory Mechanisms. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7586534.bard.

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Original Objectives: 1. Purify and biochemically characterize RB60 orthologs in higher plant chloroplasts; 2. Clone the gene(s) encoding plant RB60 orthologs and determine their structure and expression; 3. Manipulate the expression of RB60; 4. Assay the effects of altered RB60 expression on thylakoid biogenesis and photosynthetic function in plants exposed to different light conditions. In addition, we also examined the gene structure and expression of RB60 orthologs in the non-vascular plant, Physcomitrella patens and cloned the poly(A)-binding protein orthologue (43 kDa RB47-like protein).
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