Relevant bibliographies by topics / Large Scale Processing

Academic literature on the topic 'Large Scale Processing'

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Published: 6 September 2023

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Journal articles on the topic "Large Scale Processing"

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Fulton, Scott P. "Large-scale processing of macromolecules." Current Opinion in Biotechnology 5, no. 2 (April 1994): 201–5. http://dx.doi.org/10.1016/s0958-1669(05)80037-0.

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Siegel, Howard Jay, Thomas Schwederski, David G. Meyer, and William Tsun-yuk Hsu. "Large-scale parallel processing systems." Microprocessors and Microsystems 11, no. 1 (January 1987): 3–20. http://dx.doi.org/10.1016/0141-9331(87)90325-5.

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MIKI, Mitsunori. "Large-scale Simulation and Parallel Processing." Journal of the Society of Powder Technology, Japan 35, no. 3 (1998): 192–97. http://dx.doi.org/10.4164/sptj.35.192.

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Lee, Daewoo, Jin-Soo Kim, and Seungryoul Maeng. "Large-scale incremental processing with MapReduce." Future Generation Computer Systems 36 (July 2014): 66–79. http://dx.doi.org/10.1016/j.future.2013.09.010.

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Ganetsos, G., and P. E. Barker. "Large-scale chromatography in industrial processing." Journal of Chemical Technology & Biotechnology 50, no. 1 (April 24, 2007): 101–8. http://dx.doi.org/10.1002/jctb.280500111.

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Sterken, Yvonne, Alexander Toet, and Yen-Lee Yap. "Factors Limiting Large-Scale Localisation." Perception 23, no. 6 (June 1994): 709–26. http://dx.doi.org/10.1068/p230709.

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The mechanisms mediating relative spatial localisation in the visual system are still unclear. There is a growing amount of evidence that this capability is not merely limited by the processing of the front-end visual system. Models of localisation should, therefore, include higher-level processing stages. A careful study of the sources of error in localisation tasks may further our understanding of the nature of these processes. A study is reported in which the possible role of higher-order processing in relative spatial localisation is explicitly addressed. For this purpose the error sources of threshold performance were investigated for two similar relative-spatial-localisation tasks: two-dot separation discrimination and two-dot orientation discrimination. Fovea-centred stimuli with large dot separations were used. The front-end processing for these stimuli is probably identical in both tasks. Hence, differential effects of the variation of the experimental parameters on threshold performance for both tasks may reveal the characteristics of the higher-level processing involved. The effects of dot separation, stimulus orientation, and experimental procedure (single-stimulus binary forced choice versus two-alternative forced choice) on threshold performance for both tasks are reported. The results show that thresholds for both tasks increase proportionally with dot separation. However, separation-discrimination thresholds are always significantly higher than orientation-discrimination thresholds. Thresholds for separation discrimination are independent of stimulus orientation. In contrast, orientation-discrimination thresholds show an oblique effect: thresholds are consistently lower for horizontal stimuli. Both tasks also show a different dependency of threshold behaviour on the experimental procedure. For a horizontal stimulus orientation, separation discrimination is better with an explicit (physical) reference standard, whereas orientation discrimination is better with an implicit referent. These differential effects cannot be explained by any of the known characteristics of the front-end visual system. They suggest that large-scale spatial-localisation performance is probably limited at a processing level at which spatial relations are explicitly represented.
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Liu, Ning, Dong-sheng Li, Yi-ming Zhang, and Xiong-lve Li. "Large-scale graph processing systems: a survey." Frontiers of Information Technology & Electronic Engineering 21, no. 3 (March 2020): 384–404. http://dx.doi.org/10.1631/fitee.1900127.

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K¨ampf, Mirko, and Jan W. Kantelhardt. "Hadoop. TS: Large-Scale Time-Series Processing." International Journal of Computer Applications 74, no. 17 (July 26, 2013): 1–8. http://dx.doi.org/10.5120/12974-0233.

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Ko, Seyoon, and Joong-Ho Won. "Processing large-scale data with Apache Spark." Korean Journal of Applied Statistics 29, no. 6 (October 31, 2016): 1077–94. http://dx.doi.org/10.5351/kjas.2016.29.6.1077.

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JONES, ALEX K., DARREN J. KERBYSON, RAM RAJAMONY, and CHARLES WEEMS. "GUEST EDITOR'S NOTE: LARGE-SCALE PARALLEL PROCESSING." Parallel Processing Letters 18, no. 04 (December 2008): 449–51. http://dx.doi.org/10.1142/s0129626408003508.

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Dissertations / Theses on the topic "Large Scale Processing"

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Kutlu, Mucahid. "Parallel Processing of Large Scale Genomic Data." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1436355132.

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Caneill, Matthieu. "Contributions to large-scale data processing systems." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM006/document.

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Cette thèse couvre le sujet des systèmes de traitement de données àgrande échelle, et plus précisément trois approches complémentaires :la conception d'un système pour prédir des défaillances de serveursgrâce à l'analyse de leurs données de supervision; l'acheminement dedonnées dans un système à temps réel en étudiant les corrélationsentre les champs des messages pour favoriser la localité; etfinalement un environnement de développement innovateur pour concevoirdes transformations de donées en utilisant des graphes orientés deblocs.À travers le projet Smart Support Center, nous concevons unearchitecture qui passe à l'échelle, afin de stocker des sériestemporelles rapportées par des moteurs de supervision, qui vérifienten permanence la santé des systèmes informatiques. Nous utilisons cesdonnées pour effectuer des prédictions, et détecter de potentielsproblèmes avant qu'ils ne ne produisent.Nous nous plongeons ensuite dans les algorithmes d'acheminement pourles sytèmes de traitement de données en temps réel, et développons unecouche pour acheminer les messages plus efficacement, en évitant lesrebonds entre machines. Dans ce but, nous identifions en temps réelles corrélations qui apparaissent entre les champs de ces messages,tels les mots-clics et leur localisation géographique, par exempledans le cas de micromessages. Nous utilisons ces corrélations pourcréer des tables d'acheminement qui favorisent la colocation desacteurs traitant ces messages.Pour finir, nous présentons λ-blocks, un environnement dedéveloppement pour effectuer des tâches de transformations de donnéessans écrire de code source, mais en créant des graphes de blocs decode. L'environnement est rapide, et est distribué avec des pilesincluses: libraries de blocs, modules d'extension, et interfaces deprogrammation pour l'étendre. Il est également capable de manipulerdes graphes d'exécution, pour optimisation, analyse, vérification, outout autre but
This thesis covers the topic of large-scale data processing systems,and more precisely three complementary approaches: the design of asystem to perform prediction about computer failures through theanalysis of monitoring data; the routing of data in a real-time systemlooking at correlations between message fields to favor locality; andfinally a novel framework to design data transformations usingdirected graphs of blocks.Through the lenses of the Smart Support Center project, we design ascalable architecture, to store time series reported by monitoringengines, which constantly check the health of computer systems. We usethis data to perform predictions, and detect potential problems beforethey arise.We then dive in routing algorithms for stream processing systems, anddevelop a layer to route messages more efficiently, by avoiding hopsbetween machines. For that purpose, we identify in real-time thecorrelations which appear in the fields of these messages, such ashashtags and their geolocation, for example in the case of tweets. Weuse these correlations to create routing tables which favor theco-location of actors handling these messages.Finally, we present λ-blocks, a novel programming framework to computedata processing jobs without writing code, but rather by creatinggraphs of blocks of code. The framework is fast, and comes withbatteries included: block libraries, plugins, and APIs to extendit. It is also able to manipulate computation graphs, foroptimization, analyzis, verification, or any other purposes
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Wang, Liqiang. "An Efficient Platform for Large-Scale MapReduce Processing." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/963.

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In this thesis we proposed and implemented the MMR, a new and open-source MapRe- duce model with MPI for parallel and distributed programing. MMR combines Pthreads, MPI and the Google's MapReduce processing model to support multi-threaded as well as dis- tributed parallelism. Experiments show that our model signi cantly outperforms the leading open-source solution, Hadoop. It demonstrates linear scaling for CPU-intensive processing and even super-linear scaling for indexing-related workloads. In addition, we designed a MMR live DVD which facilitates the automatic installation and con guration of a Linux cluster with integrated MMR library which enables the development and execution of MMR applications.
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Larsson, Carl-Johan. "Movie Recommendation System Using Large Scale Graph-Processing." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200601.

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Gardner, Tara Conti. "Delipidation Treatments for Large-Scale Protein Purification Processing." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36512.

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Triglycerides are the majority lipid component of most biochemical mixtures and are virtually water insoluble. Lipid removal is desired prior to protein purification processing to decrease nonspecific fouling of downstream chromatographic matrices. Transgenic pig milk was used as a model system to study delipidation from therapeutic protein sources. The majority of triglycerides was extracted from stable lipid micelles and removed with a method that can be incorporated in downstream protein purification processing without denaturing the target protein. An efficient delipidation treatment used TNBP, a non-polar solvent, to extract lipid micelles and then phase transfer milk lipids into a TNBP-swelled dextran particulate. A batch incubation of a whey/TNBP mixture with pre-swollen Sephadex LH-20 or hydroxyalkoxypropyl dextran (HAPD) beads at 4 C for 24 hours removed 67 + 2 % (0.645 mg triglycerides/ml Sephadex LH-20) and 71 o + 1 % (0.628 mg triglycerides/ml HAPD) of the triglycerides present in the skimmed transgenic whey, respectively. Fully swollen beads removed 20% more triglycerides than beads which were wetted but not swollen in TNBP, indicating that a larger phase volume and internal adsorption of the lipids onto the Sephadex matrix dominates over surface adsorption. Polyclonal ELISAs indicated that 89 + 6% of the recombinant human Protein C was still present in the transgenic whey after this delipidation treatment, indicating this treatment did not denature or harm the target protein.
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Wang, Jiayin. "Building Efficient Large-Scale Big Data Processing Platforms." Thesis, University of Massachusetts Boston, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10262281.

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In the era of big data, many cluster platforms and resource management schemes are created to satisfy the increasing demands on processing a large volume of data. A general setting of big data processing jobs consists of multiple stages, and each stage represents generally defined data operation such as ltering and sorting. To parallelize the job execution in a cluster, each stage includes a number of identical tasks that can be concurrently launched at multiple servers. Practical clusters often involve hundreds or thousands of servers processing a large batch of jobs. Resource management, that manages cluster resource allocation and job execution, is extremely critical for the system performance.

Generally speaking, there are three main challenges in resource management of the new big data processing systems. First, while there are various pending tasks from dierent jobs and stages, it is difficult to determine which ones deserve the priority to obtain the resources for execution, considering the tasks' different characteristics such as resource demand and execution time. Second, there exists dependency among the tasks that can be concurrently running. For any two consecutive stages of a job, the output data of the former stage is the input data of the later one. The resource management has to comply with such dependency. The third challenge is the inconsistent performance of the cluster nodes. In practice, run-time performance of every server is varying. The resource management needs to dynamically adjust the resource allocation according to the performance change of each server.

The resource management in the existing platforms and prior work often rely on fixed user-specic congurations, and assumes consistent performance in each node. The performance, however, is not satisfactory under various workloads. This dissertation aims to explore new approaches to improving the eciency of large-scale big data processing platforms. In particular, the run-time dynamic factors are carefully considered when the system allocates the resources. New algorithms are developed to collect run-time data and predict the characteristics of jobs and the cluster. We further develop resource management schemes that dynamically tune the resource allocation for each stage of every running job in the cluster. New findings and techniques in this dissertation will certainly provide valuable and inspiring insights to other similar problems in the research community.

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Clifford, Raphael. "Indexed strings for large scale genomic analysis." Thesis, Imperial College London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268368.

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Schaeppi, Reto. "Large scale processing of microarray data a diploma thesis /." Zurich : Information and Communication Systems Research Group, Institute of Information Systems, Swiss Federal Institute of Technology, 2002. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=48.

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Mesmoudi, Amin. "Declarative parallel query processing on large scale astronomical databases." Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10326.

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Les travaux de cette thèse s'inscrivent dans le cadre du projet Petasky. Notre objectif est de proposer des outils permettant de gérer des dizaines de Peta-octets de données issues d'observations astronomiques. Nos travaux se focalisent essentiellement sur la conception des nouveaux systèmes permettant de garantir le passage à l'échelle. Dans cette thèse, nos contributions concernent trois aspects : Benchmarking des systèmes existants, conception d'un nouveau système et optimisation du système. Nous avons commencé par analyser la capacité des systèmes fondés sur le modèle MapReduce et supportant SQL à gérer les données LSST et leurs capacités d'optimisation de certains types de requêtes. Nous avons pu constater qu'il n'y a pas de technique « magique » pour partitionner, stocker et indexer les données mais l'efficacité des techniques dédiées dépend essentiellement du type de requête et de la typologie des données considérées. Suite à notre travail de Benchmarking, nous avons retenu quelques techniques qui doivent être intégrées dans un système de gestion de données à large échelle. Nous avons conçu un nouveau système de façon à garantir la capacité dudit système à supporter plusieurs mécanismes de partitionnement et plusieurs opérateurs d'évaluation. Nous avons utilisé BSP (Bulk Synchronous Parallel) comme modèle de calcul. Les données sont représentées logiquement par des graphes. L'évaluation des requêtes est donc faite en explorant le graphe de données en utilisant les arcs entrants et les arcs sortants. Les premières expérimentations ont montré que notre approche permet une amélioration significative des performances par rapport aux systèmes Map/Reduce
This work is carried out in framework of the PetaSky project. The objective of this project is to provide a set of tools allowing to manage Peta-bytes of data from astronomical observations. Our work is concerned with the design of a scalable approach. We first started by analyzing the ability of MapReduce based systems and supporting SQL to manage the LSST data and ensure optimization capabilities for certain types of queries. We analyzed the impact of data partitioning, indexing and compression on query performance. From our experiments, it follows that there is no “magic” technique to partition, store and index data but the efficiency of dedicated techniques depends mainly on the type of queries and the typology of data that are considered. Based on our work on benchmarking, we identified some techniques to be integrated to large-scale data management systems. We designed a new system allowing to support multiple partitioning mechanisms and several evaluation operators. We used the BSP (Bulk Synchronous Parallel) model as a parallel computation paradigm. Unlike MapeReduce model, we send intermediate results to workers that can continue their processing. Data is logically represented as a graph. The evaluation of queries is performed by exploring the data graph using forward and backward edges. We also offer a semi-automatic partitioning approach, i.e., we provide the system administrator with a set of tools allowing her/him to choose the manner of partitioning data using the schema of the database and domain knowledge. The first experiments show that our approach provides a significant performance improvement with respect to Map/Reduce systems
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Dreibelbis, Harold N., Dennis Kelsch, and Larry James. "REAL-TIME TELEMETRY DATA PROCESSING and LARGE SCALE PROCESSORS." International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612912.

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International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada
Real-time data processing of telemetry data has evolved from a highly centralized single large scale computer system to multiple mini-computers or super mini-computers tied together in a loosely coupled distributed network. Each mini-computer or super mini-computer essentially performing a single function in the real-time processing sequence of events. The reasons in the past for this evolution are many and varied. This paper will review some of the more significant factors in that evolution and will present some alternatives to a fully distributed mini-computer network that appear to offer significant real-time data processing advantages.
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Books on the topic "Large Scale Processing"

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1960-, Morishita Shinichi, ed. Large-scale genome sequence processing. London: Imperial College Press, 2006.

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Workshop on Large-Scale Numerical Optimization (1989 Cornell University). Large-scale numerical optimization. Philadelphia: Society for Industrial and Applied Mathematics, 1990.

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J, Offen R., ed. VLSIimage processing. London: Collins, 1985.

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Sakr, Sherif, Faisal Moeen Orakzai, Ibrahim Abdelaziz, and Zuhair Khayyat. Large-Scale Graph Processing Using Apache Giraph. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47431-1.

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Cecil, J. E. Small, medium and large-scale starch processing. Rome: Food and Agriculture Organization of the United Nations, 1992.

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Cecil, J. E. Small, medium and large-scale starch processing. Rome: Food and Agriculture Organization of the United Nations, 1992.

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Application architecture: Modern large-scale information processing. New York: Wiley, 1990.

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Cecil, J. E. Small-, medium-, and large-scale starch processing. Rome: Food and Agriculture Organization of the United Nations, 1992.

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J, Offen R., ed. VLSI image processing. London: Collins, 1985.

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Large scale and big data: Processing and management. Boca Raton: Taylor & Francis, 2014.

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Book chapters on the topic "Large Scale Processing"

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Atanassov, E. I., D. Georgiev, T. Gurov, A. Karaivanova, and Y. Nikolova. "Distributed System for Query Processing with Grid Authentication." In Large-Scale Scientific Computing, 467–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43880-0_53.

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Sakr, Sherif. "Large-Scale Graph Processing Systems." In Big Data 2.0 Processing Systems, 59–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44187-6_4.

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Sakr, Sherif. "Large-Scale Stream Processing Systems." In Big Data 2.0 Processing Systems, 95–115. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44187-6_5.

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van der Stockt, Stefan, Aaron K. Baughman, and Michael Perlitz. "Large-Scale Biometric Multimedia Processing." In Multimedia Data Mining and Analytics, 177–204. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_8.

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Sakr, Sherif. "Large-Scale Graph Processing Systems." In Big Data 2.0 Processing Systems, 53–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38776-5_4.

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Sakr, Sherif. "Large-Scale Stream Processing Systems." In Big Data 2.0 Processing Systems, 75–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38776-5_5.

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Nagel, Kai, Marcus Rickert, and Christopher L. Barrett. "Large scale traffic simulations." In Vector and Parallel Processing — VECPAR'96, 380–402. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62828-2_131.

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Dechevsky, Lubomir T., Børre Bang, Joakim Gundersen, Arne Lakså, and Arnt R. Kristoffersen. "Solving Non-linear Systems of Equations on Graphics Processing Units." In Large-Scale Scientific Computing, 719–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12535-5_86.

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Dechevsky, Lubomir, Joakim Gundersen, and Børre Bang. "Computing n-Variate Orthogonal Discrete Wavelet Transforms on Graphics Processing Units." In Large-Scale Scientific Computing, 730–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12535-5_87.

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Zhou, Rong, and Liqing Zhang. "Contour-Based Large Scale Image Retrieval." In Neural Information Processing, 565–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24965-5_64.

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Conference papers on the topic "Large Scale Processing"

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Chen, Kang, Yubing Yin, and Weimin Zheng. "Teaching large scale data processing." In the 1st ACM Summit on Computing Education in China. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1517632.1517635.

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Margan, Domagoj, and Peter Pietzuch. "Large-Scale Stream Graph Processing." In DEBS '17: The 11th ACM International Conference on Distributed and Event-based Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3093742.3093907.

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Verstoep, Kees, Henri E. Bal, Jiri Barnat, and Lubos Brim. "Efficient large-scale model checking." In Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5161000.

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Liu, Alex X., Ke Shen, and Eric Torng. "Large scale Hamming distance query processing." In 2011 IEEE International Conference on Data Engineering (ICDE 2011). IEEE, 2011. http://dx.doi.org/10.1109/icde.2011.5767831.

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van der Zant, Tijn, Lambert Schomaker, and Edwin Valentijn. "Large scale parallel document image processing." In Electronic Imaging 2008, edited by Berrin A. Yanikoglu and Kathrin Berkner. SPIE, 2008. http://dx.doi.org/10.1117/12.765482.

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Heindl, Christoph, Gernot Stübl, Thomas Pönitz, Andreas Pichler, and Josef Scharinger. "Visual large-scale industrial interaction processing." In UbiComp '19: The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3341162.3343769.

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Im, ChangJin, Jae-Heon Jeong, and Chang-Sung Jeong. "Parallel Large-Scale Image Processing for Orthorectification." In TENCON 2018 - 2018 IEEE Region 10 Conference. IEEE, 2018. http://dx.doi.org/10.1109/tencon.2018.8650289.

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Pernpeintner, Reinhold, Angelika Fröhlich, and Thomas Lippert. "CFRP Infusion Processing on Large-Scale Cylindrica..." In 56th International Astronautical Congress of the International Astronautical Federation, the International Academy of Astronautics, and the International Institute of Space Law. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.iac-05-c2.1.a.04.

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Herbst, Ludolf, and Jan Brune. "Micro-scale large-area UV laser processing." In SPIE LASE: Lasers and Applications in Science and Engineering, edited by Wilhelm Pfleging, Yongfeng Lu, Kunihiko Washio, Willem Hoving, and Jun Amako. SPIE, 2009. http://dx.doi.org/10.1117/12.807828.

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Filho, P. Souza, L. Felipe, P. Aragäo, L. Bejarano, D. Thomé de Paula, A. Sardinha, A. Azambuja, and F. Sierra. "Large Scale Seismic Processing in Public Cloud." In 82nd EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202011916.

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Reports on the topic "Large Scale Processing"

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

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Whiting, M. A. Object-oriented design: Deriving conceptual solutions to large-scale information processing problems. Office of Scientific and Technical Information (OSTI), May 1990. http://dx.doi.org/10.2172/6895465.

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Bilgutay, Nihat M. Computer Facilities for High-Speed Data Acquisition, Signal Processing and Large Scale System Simulation. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada170935.

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Galiani, Sebastian, Ramiro Gálvez, and Ian Nachman. Unveiling Specialization Trends in Economics Research: A Large-Scale Study Using Natural Language Processing and Citation Analysis. Cambridge, MA: National Bureau of Economic Research, June 2023. http://dx.doi.org/10.3386/w31295.

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Henz, Brian J., John Lazorisak, Jaroslaw Knap, Jason Livingston, and Dale R. Shires. Installation to Production of a Large-Scale General Purpose Graphics Processing Unit (GPGPU) Cluster at the U.S. Army Research Laboratory: Thufir. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada610234.

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Hobbs, D. T., D. Chai, D. Hartsough, and D. Genders. Final Report on the Large Scale Demonstration for the Electrochemical Processing of Hanford and Savannah River Site High-Level Waste Simulants. Office of Scientific and Technical Information (OSTI), September 1995. http://dx.doi.org/10.2172/607121.

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Salter, R., Quyen Dong, Cody Coleman, Maria Seale, Alicia Ruvinsky, LaKenya Walker, and W. Bond. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40203.

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The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.
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Thompson. L52208 Coating and Backfill System Optimisation. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 2004. http://dx.doi.org/10.55274/r0010964.

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This project focuses specifically on the mechanical properties of a pipeline coating in relation to the need to use imported or site-processed bedding and padding materials to create the pipelines habitat. This project assesses whether mainline and field joint coatings, typically utilized for pipeline protection, possessed sufficient mechanical resistance to withstand backfilling, commissioning and service with bedding and padding materials that had undergone minimal processing. The project was carried out in two phases, the first involved a series of large-scale tests to assess the impact, penetration and abrasion resistance of three mainline and two field joint coatings. The second phase involved a series of small-scale laboratory tests to assess the same three mechanical properties. Includes a spreadhseet to help cost optimize backfilling pipelines.
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SIEGFRIED, MATTHEW, WILLIAM RAMSEY, and MATTHEW WILLIAMS. PERMANGANATE OXIDATION OF DEFENSE WASTE PROCESSING FACILITY (DWPF) RECYCLE COLLECTION TANK (RCT) SIMULANTS LARGER SCALE PROTOCOL RUNS - CHEMICAL PROCESS CELL (CPC) NOMINAL AND FOAMOVER CONDITIONS. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1701701.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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