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Journal articles on the topic 'Large Scale Processing'

Author: Grafiati
Published: 6 September 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

JONES, ALEX K., DARREN J. KERBYSON, RAM RAJAMONY, and CHARLES WEEMS. "GUEST EDITOR'S NOTE: LARGE SCALE PARALLEL PROCESSING." Parallel Processing Letters 19, no. 04 (December 2009): 487–90. http://dx.doi.org/10.1142/s0129626409000377.

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12

WEEMS, CHARLES C., DARREN J. KERBYSON, RAM RAJAMONY, and ALEX K. JONES. "GUEST EDITOR'S NOTE: LARGE-SCALE PARALLEL PROCESSING." Parallel Processing Letters 20, no. 04 (December 2010): 289–91. http://dx.doi.org/10.1142/s0129626410000247.

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13

WEEMS, CHARLES C., DARREN J. KERBYSON, and RAM RAJAMONY. "GUEST EDITOR'S NOTE: LARGE-SCALE PARALLEL PROCESSING." Parallel Processing Letters 21, no. 03 (September 2011): 275–77. http://dx.doi.org/10.1142/s0129626411000217.

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14

WEEMS, CHARLES C., DARREN J. KERBYSON, and RAM RAJAMONY. "GUEST EDITORS' NOTE: LARGE-SCALE PARALLEL PROCESSING." Parallel Processing Letters 23, no. 04 (December 2013): 1302002. http://dx.doi.org/10.1142/s0129626413020027.

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15

Cheng, Long, and Spyros Kotoulas. "Scale-Out Processing of Large RDF Datasets." IEEE Transactions on Big Data 1, no. 4 (December 1, 2015): 138–50. http://dx.doi.org/10.1109/tbdata.2015.2505719.

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16

Mason, Timothy J. "Large scale sonochemical processing: aspiration and actuality." Ultrasonics Sonochemistry 7, no. 4 (October 2000): 145–49. http://dx.doi.org/10.1016/s1350-4177(99)00041-3.

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17

Wylie, K. P., and M. F. Regner. "Large-Scale Network Involvement in Language Processing." Journal of Neuroscience 34, no. 47 (November 19, 2014): 15505–7. http://dx.doi.org/10.1523/jneurosci.3539-14.2014.

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18

Riedel, E., C. Faloutsos, G. A. Gibson, and D. Nagle. "Active disks for large-scale data processing." Computer 34, no. 6 (June 2001): 68–74. http://dx.doi.org/10.1109/2.928624.

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19

Mills, John A. "Large scale interoperability and distributed transaction processing." Journal of Systems Integration 3, no. 3-4 (September 1993): 351–69. http://dx.doi.org/10.1007/bf01975520.

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20

Cooke, Ayanna, Murray Grossman, Christian DeVita, Julio Gonzalez-Atavales, Peachie Moore, Willis Chen, James Gee, and John Detre. "Large-scale neural network for sentence processing." Brain and Language 96, no. 1 (January 2006): 14–36. http://dx.doi.org/10.1016/j.bandl.2005.07.072.

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21

De Pauw, Wim, and Henrique Andrade. "Visualizing Large-Scale Streaming Applications." Information Visualization 8, no. 2 (January 22, 2009): 87–106. http://dx.doi.org/10.1057/ivs.2009.5.

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Stream processing is a new and important computing paradigm. Innovative streaming applications are being developed in areas ranging from scientific applications (for example, environment monitoring), to business intelligence (for example, fraud detection and trend analysis), to financial markets (for example, algorithmic trading systems). In this paper we describe Streamsight, a new visualization tool built to examine, monitor and help understand the dynamic behavior of streaming applications. Streamsight can handle the complex, distributed and large-scale nature of stream processing applications by using hierarchical graphs, multi-perspective visualizations, and de-cluttering strategies. To address the dynamic and adaptive nature of these applications, Streamsight also provides real-time visualization as well as the capability to record and replay. All these features are used for debugging, for performance optimization, and for management of resources, including capacity planning. More than 100 developers, both inside and outside IBM, have been using Streamsight to help design and implement large-scale stream processing applications.
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22

Dai, Guohao, Tianhao Huang, Yuze Chi, Jishen Zhao, Guangyu Sun, Yongpan Liu, Yu Wang, Yuan Xie, and Huazhong Yang. "GraphH: A Processing-in-Memory Architecture for Large-Scale Graph Processing." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 38, no. 4 (April 2019): 640–53. http://dx.doi.org/10.1109/tcad.2018.2821565.

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23

ZHOU, Ao-Ying, Min-Qi ZHOU, Wei-Ning QIAN, and Rong ZHANG. "Complex Query Processing in Large-Scale Distributed System." Chinese Journal of Computers 31, no. 9 (October 11, 2009): 1563–72. http://dx.doi.org/10.3724/sp.j.1016.2008.01563.

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24

Haw, Su-Cheng, and G. S. V. Radha Kris. "Path Query Processing in Large-Scale XML Databases." Journal of Applied Sciences 7, no. 19 (September 15, 2007): 2736–43. http://dx.doi.org/10.3923/jas.2007.2736.2743.

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25

Tampubolon, W., and W. Reinhardt. "UAV Data Processing for Large Scale Topographical Mapping." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5 (June 6, 2014): 565–72. http://dx.doi.org/10.5194/isprsarchives-xl-5-565-2014.

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Large scale topographical mapping in the third world countries is really a prominent challenge in geospatial industries nowadays. On one side the demand is significantly increasing while on the other hand it is constrained by limited budgets available for mapping projects. Since the advent of Act Nr.4/yr.2011 about Geospatial Information in Indonesia, large scale topographical mapping has been on high priority for supporting the nationwide development e.g. detail spatial planning. Usually large scale topographical mapping relies on conventional aerial survey campaigns in order to provide high resolution 3D geospatial data sources. Widely growing on a leisure hobby, aero models in form of the so-called Unmanned Aerial Vehicle (UAV) bring up alternative semi photogrammetric aerial data acquisition possibilities suitable for relatively small Area of Interest (AOI) i.e. <5,000 hectares. For detail spatial planning purposes in Indonesia this area size can be used as a mapping unit since it usually concentrates on the basis of sub district area (kecamatan) level. In this paper different camera and processing software systems will be further analyzed for identifying the best optimum UAV data acquisition campaign components in combination with the data processing scheme. The selected AOI is covering the cultural heritage of Borobudur Temple as one of the Seven Wonders of the World. A detailed accuracy assessment will be concentrated within the object feature of the temple at the first place. Feature compilation involving planimetric objects (2D) and digital terrain models (3D) will be integrated in order to provide Digital Elevation Models (DEM) as the main interest of the topographic mapping activity. By doing this research, incorporating the optimum amount of GCPs in the UAV photo data processing will increase the accuracy along with its high resolution in 5 cm Ground Sampling Distance (GSD). Finally this result will be used as the benchmark for alternative geospatial data acquisition in the future in which it can support national large scale topographical mapping program up to the 1:1.000 map scale.
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26

TAMURA, Yoshiaki, Hiroyuki FURUSAWA, Hidenori FUJII, and Yuki MIYAMOTO. "Data Processing for Large-Scale Comoutational Mechanics Results." Journal of the Visualization Society of Japan 29-1, no. 1 (2009): 129. http://dx.doi.org/10.3154/jvs.29.129.

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27

Lee, Youngrim, Wanyong Park, Hyunchun Park, and Daesik Shin. "FAST Design for Large-Scale Satellite Image Processing." Journal of the Korea Institute of Military Science and Technology 25, no. 4 (August 5, 2022): 372–80. http://dx.doi.org/10.9766/kimst.2022.25.4.372.

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This study proposes a distributed parallel processing system, called the Fast Analysis System for remote sensing daTa(FAST), for large-scale satellite image processing and analysis. FAST is a system that designs jobs in vertices and sequences, and distributes and processes them simultaneously. FAST manages data based on the Hadoop Distributed File System, controls entire jobs based on Apache Spark, and performs tasks in parallel in multiple slave nodes based on a docker container design. FAST enables the high-performance processing of progressively accumulated large-volume satellite images. Because the unit task is performed based on Docker, it is possible to reuse existing source codes for designing and implementing unit tasks. Additionally, the system is robust against software/hardware faults. To prove the capability of the proposed system, we performed an experiment to generate the original satellite images as ortho-images, which is a pre-processing step for all image analyses. In the experiment, when FAST was configured with eight slave nodes, it was found that the processing of a satellite image took less than 30 sec. Through these results, we proved the suitability and practical applicability of the FAST design.
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28

Fungtammasan, Arkarachai, Alexandra Lee, Jaclyn Taroni, Kurt Wheeler, Chen-Shan Chin, Sean Davis, and Casey Greene. "Ten simple rules for large-scale data processing." PLOS Computational Biology 18, no. 2 (February 10, 2022): e1009757. http://dx.doi.org/10.1371/journal.pcbi.1009757.

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29

Fulton, Scott P., Ahmad J. Shahidi, Neal F. Gordon, and Noubar B. Afeyan. "Large–Scale Processing & High–Throughput Perfusion Chromatography." Nature Biotechnology 10, no. 6 (June 1992): 635–39. http://dx.doi.org/10.1038/nbt0692-635.

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30

Keil, Andreas, Margaret M. Bradley, Olaf Hauk, Brigitte Rockstroh, Thomas Elbert, and Peter J. Lang. "Large-scale neural correlates of affective picture processing." Psychophysiology 39, no. 5 (September 2002): 641–49. http://dx.doi.org/10.1111/1469-8986.3950641.

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31

Tung, Vincent C., Matthew J. Allen, Yang Yang, and Richard B. Kaner. "High-throughput solution processing of large-scale graphene." Nature Nanotechnology 4, no. 1 (November 9, 2008): 25–29. http://dx.doi.org/10.1038/nnano.2008.329.

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32

Pavlo, Andrew. "Emerging Hardware Trends in Large-Scale Transaction Processing." IEEE Internet Computing 19, no. 3 (May 2015): 68–71. http://dx.doi.org/10.1109/mic.2015.59.

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33

Vildario, Alfrido, Fitriyani, and Galih Nugraha Nurkahfi. "Large-Scale Graph Processing Analysis using Supercomputer Cluster." Journal of Physics: Conference Series 801 (January 2017): 012079. http://dx.doi.org/10.1088/1742-6596/801/1/012079.

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34

Zhao, Qianqian. "Image Processing of Large-Scale Pollution on Water." Journal of Physics: Conference Series 1486 (April 2020): 042019. http://dx.doi.org/10.1088/1742-6596/1486/4/042019.

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35

Masuda, Hiroshi, and Hiroaki Date. "Special Issue on Large-Scale Point Cloud Processing." International Journal of Automation Technology 12, no. 3 (May 1, 2018): 327. http://dx.doi.org/10.20965/ijat.2018.p0327.

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Recently, terrestrial laser scanners have been significantly improved in terms of accuracy, measurement distance, measurement speed, and resolution. They enable us to capture dense 3D point clouds of large-scale objects and fields, such as factories, engineering plants, large equipment, and transport ships. In addition, the mobile mapping system, which is a vehicle equipped with laser scanners and GPSs, can be used for capturing large-scale point clouds from a wide range of roads, buildings, and roadside objects. Large-scale point clouds are useful in a variety of applications, such as renovation and maintenance of facilities, engineering simulation, asset management, and 3D mapping. To realize these applications, new techniques must be developed for processing large-scale point clouds. So far, point processing has been studied mainly for relatively small objects in the field of computer-aided design and computer graphics. However, in recent years, the application areas of point clouds are not limited to conventional domains, but also include manufacturing, civil engineering, construction, transportation, forestry, and so on. This is because the state-of-the-art laser scanner can be used to represent large objects or fields as dense point clouds. We believe that discussing new techniques and applications related to large-scale point clouds beyond the boundaries of traditional academic fields is very important.This special issue addresses the latest research advances in large-scale point cloud processing. This covers a wide area of point processing, including shape reconstruction, geometry processing, object recognition, registration, visualization, and applications. The papers will help readers explore and share their knowledge and experience in technologies and development techniques.All papers were refereed through careful peer reviews. We would like to express our sincere appreciation to the authors for their submissions and to the reviewers for their invaluable efforts for ensuring the success of this special issue.
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36

Tahsir Ahmed Munna, Md, Shaikh Muhammad Allayear, Mirza Mohtashim Alam, Sheikh Shah Mohammad Motiur Rahman, Md Samadur Rahman, and M. Mesbahuddin Sarker. "Simplified Mapreduce Mechanism for Large Scale Data Processing." International Journal of Engineering & Technology 7, no. 3.8 (July 7, 2018): 16. http://dx.doi.org/10.14419/ijet.v7i3.8.15211.

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MapReduce has become a popular programming model for processing and running large-scale data sets with a parallel, distributed paradigm on a cluster. Hadoop MapReduce is needed especially for large scale data like big data processing. In this paper, we work to modify the Hadoop MapReduce Algorithm and implement it to reduce processing time.
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37

Zhang, Mengxue, and Philippe Roth. "Flow photochemistry — from microreactors to large-scale processing." Current Opinion in Chemical Engineering 39 (March 2023): 100897. http://dx.doi.org/10.1016/j.coche.2023.100897.

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38

Polyakov, A. Yu, T. V. Lyutyy, S. Denisov, V. V. Reva, and P. Hänggi. "Large-scale ferrofluid simulations on graphics processing units." Computer Physics Communications 184, no. 6 (June 2013): 1483–89. http://dx.doi.org/10.1016/j.cpc.2013.01.016.

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39

Alles, Guilherme Rezende, João L. D. Comba, Jean-Marc Vincent, Shin Nagai, and Lucas Mello Schnorr. "Measuring phenology uncertainty with large scale image processing." Ecological Informatics 59 (September 2020): 101109. http://dx.doi.org/10.1016/j.ecoinf.2020.101109.

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40

Cappellari, Paolo, Mark Roantree, and Soon Ae Chun. "Optimizing data stream processing for large-scale applications." Software: Practice and Experience 48, no. 9 (June 19, 2018): 1607–41. http://dx.doi.org/10.1002/spe.2596.

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41

GANETSOS, G., and P. E. BARKER. "ChemInform Abstract: Large-Scale Chromatography in Industrial Processing." ChemInform 22, no. 8 (August 23, 2010): no. http://dx.doi.org/10.1002/chin.199108345.

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42

Premalatha, M., and G. Baskaran. "Bootstrap Based Large Scale Data Processing Using Cluster." International Journal of Advance Research and Innovation 3, no. 2 (2015): 36–39. http://dx.doi.org/10.51976/ijari.321509.

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Cloud computing is model in which large groups of remote servers are networked to allow centralized data storage and online access to computer services or resources. Clouds can be classified as public, private. A corporate network is a group of computers, connected together in a building or in a particular area, which are all owned by the same. The corporate network is often used for sharing information among the participating companies and facilitating collaboration in a certain industry sector where companies share a common interest, there is so challenges and some security issues appeared .So in the proposed System it implement the Best Peer++, a system which delivers elastic data sharing services for corporate network applications in the cloud based on Best Peer—a peer-to-peer (P2P) based data management platform. A data management platform is the backbone of data-driven marketing, and serves as a unifying platform to collect, organize, and activate your first- and third-party audience data from any source, including online, offline, or mobile. A true Data Management Platform should have the ability to collect unstructured audience data from any source, including mobile web and app, web analytic tools, CRM, point of sale, social, online video, and other available offline data sources.
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43

Seshamani, Sharmishtaa, Camilo Laiton, Gabor Kovacs, Nicholas Lusk, Cameron Arshadi, Adam Glaser, Jayaram Chandrashekar, and David Feng. "Cloud Pipelines for Large Scale Lightsheet Image Processing." Microscopy and Microanalysis 29, Supplement_1 (July 22, 2023): 998. http://dx.doi.org/10.1093/micmic/ozad067.501.

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44

Nguyen, Khanh Luan P., and Naveen Ashish. "Large Scale, Complex Processing of Health Data with MapReduce." Journal of Information & Knowledge Management 13, no. 01 (March 2014): 1450009. http://dx.doi.org/10.1142/s0219649214500099.

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The article describes a solution to process large volumes of unstructured health social media data in a scalable fashion using the MapReduce framework. Our work is in the context of health informatics applications involving complex text and language processing as well as large resources such as ontologies, due to which the text processing of a single unit of text takes time. Even with a throughput of an order processing time of one second per unit, it takes over a week to process a million units, which is unacceptable. We present a solution where we take the processing to a MapReduce framework and achieve significant improvement in processing performance by dividing the processing across a cluster of processors. This paper describes the technical details of our work in terms of the design, modeling, and implementation of such an approach. We also present experimental results demonstrating the effectiveness of our approach.
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45

YU, Ge, Yu GU, Yu-Bin BAO, and Zhi-Gang WANG. "Large Scale Graph Data Processing on Cloud Computing Environments." Chinese Journal of Computers 34, no. 10 (October 28, 2011): 1753–67. http://dx.doi.org/10.3724/sp.j.1016.2011.01753.

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46

Koh, Youngsol, and Yung-Hsiang Lu. "Large-scale Image Processing using Amazon EC2 Spot Instances." Electronic Imaging 2016, no. 13 (February 14, 2016): 1–6. http://dx.doi.org/10.2352/issn.2470-1173.2016.13.iqsp-226.

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47

Jain, Sudhir, Jack Murray, and Manoj Pandya. "FIELDBUS TECHNOLOGY ON A LARGE SCALE MINERAL PROCESSING PROJECT." IFAC Proceedings Volumes 40, no. 11 (2007): 29–32. http://dx.doi.org/10.3182/20070821-3-ca-2919.00005.

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48

Pásztor, László, J. Szabó, and Zs Bakacsi. "GIS Processing of Large-scale Soil Maps in Hungary." Agrokémia és Talajtan 51, no. 1-2 (March 1, 2002): 273–82. http://dx.doi.org/10.1556/agrokem.51.2002.1-2.32.

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GIS adaptation and digital reambulation of large-scale soil information originating from various agrogeological surveys has become a key issue in Hungary due the recent challenges. The national programme initiated by L. Kreybig for the systematic, 1:25,000 scale practical soil mapping of Hungary was carried out between 1935 and 1951, and provided detailed soil information (1:25,000 scale maps and complementary database in the form of explanatory booklets) for the whole country. Later farm level (1:10,000 scale) soil surveys fulfilled the practical requirements of Hungarian agriculture, producing a huge quantity of map based, soil related data. These archives still represent a valuable treasure of soil information at present. Their digital reambulation and GIS adaptation is a challenging task, which was initiated by RISSAC GIS Lab in co-operation with various institutions. The aim of these activities is the development of large-scale soil modules of a Hungarian production database for the determination of the optimal functions of agriculture in a given region, together with the harmonization of agricultural production and the protection of land and environment.
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49

Hou, Ke, Jing Zhang, and Xing Fang. "Review of Large-Scale RDF Data Processing in MapReduce." Journal of Software Engineering 9, no. 1 (December 15, 2014): 195–202. http://dx.doi.org/10.3923/jse.2015.195.202.

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50

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 (February 2008): 1–28. http://dx.doi.org/10.1145/1326542.1326547.

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