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Статті в журналах з теми "Data reduction and analysis"

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Автор: Grafiati

Опубліковано: 20 квітня 2024

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1

Liu, Songbin, Xiaomeng Huang, Haohuan Fu, Guangwen Yang, and Zhenya Song. "Data Reduction Analysis for Climate Data Sets." International Journal of Parallel Programming 43, no. 3 (October 18, 2013): 508–27. http://dx.doi.org/10.1007/s10766-013-0287-0.

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2

Niebling, Gerhard, Robert Aigner, Dietrich Wabner, and Reinhard Menzel. "Data reduction for curve analysis." Sensors and Actuators B: Chemical 34, no. 1-3 (August 1996): 481–86. http://dx.doi.org/10.1016/s0925-4005(97)80020-2.

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3

Kitamura, Akihiro, Sinya Nakamichi, and Takashi Okada. "ICONE15-10590 DATA ANALYSIS ON GLOVEBOX SIZE REDUCTION ACTIVITY IN GLOVEBOX DISMANTLING FACILITY." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2007.15 (2007): _ICONE1510. http://dx.doi.org/10.1299/jsmeicone.2007.15._icone1510_318.

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4

Amirteimoori, Alireza, Dimitris K. Despotis, and Sohrab Kordrostami. "Variables reduction in data envelopment analysis." Optimization 63, no. 5 (May 4, 2012): 735–45. http://dx.doi.org/10.1080/02331934.2012.684354.

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5

ROMERO, Julian, Luis DIAGO, Junichi SHINODA, and Ichiro HAGIWARA. "Comparison of Data Reduction Methods for the Analysis of Iyashi Expressions using Brain Signals." Journal of Advanced Simulation in Science and Engineering 2, no. 2 (2015): 349–66. http://dx.doi.org/10.15748/jasse.2.349.

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6

Li, S., N. Marsaglia, C. Garth, J. Woodring, J. Clyne, and H. Childs. "Data Reduction Techniques for Simulation, Visualization and Data Analysis." Computer Graphics Forum 37, no. 6 (March 30, 2018): 422–47. http://dx.doi.org/10.1111/cgf.13336.

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7

Peron, L. "Statistical analysis of sensory profiling data: data reduction and generalised Procrustes analysis." Food Quality and Preference 11, no. 1-2 (January 2000): 155–57. http://dx.doi.org/10.1016/s0950-3293(99)00070-1.

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8

Sarlin, Peter. "Data and dimension reduction for visual financial performance analysis." Information Visualization 14, no. 2 (October 10, 2013): 148–67. http://dx.doi.org/10.1177/1473871613504102.

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Анотація:

This article assesses the suitability of data and dimension reduction methods, and data–dimension reduction combinations, for visual financial performance analysis. Motivated by no comparable quantitative measure of all aspects of dimension reductions, this article attempts to capture the suitability of methods for the task through a qualitative comparison and illustrative experiments. While the discussion deals with differences of data–dimension reduction combinations in terms of their properties, the experiments illustrate their general applicability for financial performance analysis. The main conclusion is that topology-preserving data–dimension reduction combinations with predefined, regular grid shapes, such as the self-organizing map, are ideal tools for this task. We illustrate advantages of these types of methods with a visual financial performance analysis of large European banks.

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9

Neves, Julio Cesar, and Raad Qassim. "Supplier base reduction using data envelopment analysis." International Journal of Management and Decision Making 5, no. 1 (2004): 59. http://dx.doi.org/10.1504/ijmdm.2004.005009.

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10

Raux, J. "Supernova photometric data analysis and reduction details." New Astronomy Reviews 48, no. 7-8 (May 2004): 641–45. http://dx.doi.org/10.1016/j.newar.2003.12.041.

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11

Salem, Nema, and Sahar Hussein. "Data dimensional reduction and principal components analysis." Procedia Computer Science 163 (2019): 292–99. http://dx.doi.org/10.1016/j.procs.2019.12.111.

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12

Everett, J. E. "Block model data reduction and sensitivity analysis." Applied Earth Science 126, no. 1 (June 20, 2016): 2–10. http://dx.doi.org/10.1080/03717453.2016.1195051.

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13

Zabielin, Stanislav I. "Big Data analysis via model reduction methods." System research and information technologies, no. 2 (June 20, 2018): 35–41. http://dx.doi.org/10.20535/srit.2308-8893.2018.2.04.

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14

Collins, Martin. "The Data Reduction Approach to Survey Analysis." Market Research Society. Journal. 34, no. 2 (March 1992): 1–14. http://dx.doi.org/10.1177/147078539203400203.

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15

Hunsberger, B., C. B. Bagwell, D. Herbert, C. Bray, and M. Langweiler. "Effects of resolution reduction on data analysis." Cytometry 53A, no. 2 (May 19, 2003): 103–11. http://dx.doi.org/10.1002/cyto.a.10044.

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16

Leonarski, Filip. "On-the-Fly Data Analysis and Reduction." Synchrotron Radiation News 36, no. 4 (July 4, 2023): 2. http://dx.doi.org/10.1080/08940886.2023.2245730.

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17

Tang, Yunbo, Dan Chen, and Xiaoli Li. "Dimensionality Reduction Methods for Brain Imaging Data Analysis." ACM Computing Surveys 54, no. 4 (July 2021): 1–36. http://dx.doi.org/10.1145/3448302.

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Анотація:

The past century has witnessed the grand success of brain imaging technologies, such as electroencephalography and magnetic resonance imaging, in probing cognitive states and pathological brain dynamics for neuroscience research and neurology practices. Human brain is “the most complex object in the universe,” and brain imaging data ( BID ) are routinely of multiple/many attributes and highly non-stationary. These are determined by the nature of BID as the recordings of the evolving processes of the brain(s) under examination in various views. Driven by the increasingly high demands for precision, efficiency, and reliability in neuro-science and engineering tasks, dimensionality reduction has become a priority issue in BID analysis to handle the notoriously high dimensionality and large scale of big BID sets as well as the enormously complicated interdependencies among data elements. This has become particularly urgent and challenging in this big data era. Dimensionality reduction theories and methods manifest unrivaled potential in revealing key insights to BID via offering the low-dimensional/tiny representations/features, which may preserve critical characterizations of massive neuronal activities and brain functional and/or malfunctional states of interest. This study surveys the most salient work along this direction conforming to a 3-dimensional taxonomy with respect to (1) the scale of BID , of which the design with this consideration is important for the potential applications; (2) the order of BID , in which a higher order denotes more BID attributes manipulatable by the method; and (3) linearity , in which the method’s degree of linearity largely determines the “fidelity” in BID exploration. This study defines criteria for qualitative evaluations of these works in terms of effectiveness, interpretability, efficiency, and scalability. The classifications and evaluations based on the taxonomy provide comprehensive guides to (1) how existing research and development efforts are distributed and (2) their performance, features, and potential in influential applications especially when involving big data. In the end, this study crystallizes the open technical issues and proposes research challenges that must be solved to enable further researches in this area of great potential.

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18

Kaur, Rupinder, and Smriti Sehgal. "Dimension Reduction of Multispectral Data using Canonical Analysis." International Journal of Computer Applications 70, no. 21 (May 31, 2013): 18–21. http://dx.doi.org/10.5120/12191-8283.

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19

Hasan, Mohammed Munif, and Shabudin Mat. "Data Reduction Analysis on UTM-LST External Balance." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (October 31, 2022): 952–59. http://dx.doi.org/10.22214/ijraset.2022.47097.

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Abstract: The effective use of wind-tunnel testing in determining aerodynamic properties of a body is very much dependent upon the reliability and speed with which wind-tunnel data can be reduced. The operating efficiency of the wind tunnels is substantially improved by the capability of providing lower aerodynamic coefficients in real time, or online, which decreases the expensive wind-tunnel time necessary for each test. This paper describes a system for presenting reduced wind-tunnel data in real time for UTM-LST. The requirements for data-handling equipment and data reduction procedures for wind tunnels are quite diverse, and depend upon the wind tunnel design and the type of tests for which they are used. The supersonic wind tunnels mentioned in this description have a variety of force-balance systems and are used for force tests, pressure tests, and other research projects. Consequently, the problems associated with in order to solve this diversity we build a computerized program where we can find the transformation of axis and aerodynamic characteristics at ease. This program can find the values of different aerodynamic coefficients with certain angle of attacks.

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20

Hsu, Chung-Chian, and Jhen-Wei Wu. "Visualized mixed-type data analysis via dimensionality reduction." Intelligent Data Analysis 22, no. 5 (September 26, 2018): 981–1007. http://dx.doi.org/10.3233/ida-173480.

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21

Kline, Douglas M., and Craig S. Galbraith. "Performance analysis of the Bayesian data reduction algorithm." International Journal of Data Mining, Modelling and Management 1, no. 3 (2009): 223. http://dx.doi.org/10.1504/ijdmmm.2009.027284.

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22

Eberlein, Susan, Gigi Yates, and Niles Ritter. "Autonomous image data reduction by analysis and interpretation." Telematics and Informatics 5, no. 3 (January 1988): 241–51. http://dx.doi.org/10.1016/s0736-5853(88)80027-3.

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23

Szangolies, Klaus. "Commission II — Instrumentation for data reduction and analysis." Photogrammetria 41, no. 3 (June 1987): 204–5. http://dx.doi.org/10.1016/0031-8663(87)90035-4.

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24

Gutsche, Oliver, Luca Canali, Illia Cremer, Matteo Cremonesi, Peter Elmer, Ian Fisk, Maria Girone, et al. "CMS Analysis and Data Reduction with Apache Spark." Journal of Physics: Conference Series 1085 (September 2018): 042030. http://dx.doi.org/10.1088/1742-6596/1085/4/042030.

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25

Reddy, G. Thippa, M. Praveen Kumar Reddy, Kuruva Lakshmanna, Rajesh Kaluri, Dharmendra Singh Rajput, Gautam Srivastava, and Thar Baker. "Analysis of Dimensionality Reduction Techniques on Big Data." IEEE Access 8 (2020): 54776–88. http://dx.doi.org/10.1109/access.2020.2980942.

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26

Allen, Bruce M. "Data acquisition, reduction, and analysis using a microcomputer." Computers & Security 7, no. 5 (October 1988): 513. http://dx.doi.org/10.1016/0167-4048(88)90230-1.

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27

Ramsay, J. O. "Principal Differential Analysis: Data Reduction by Differential Operators." Journal of the Royal Statistical Society: Series B (Methodological) 58, no. 3 (September 1996): 495–508. http://dx.doi.org/10.1111/j.2517-6161.1996.tb02096.x.

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28

Sharma, Mithun J., and Song Jin Yu. "Stepwise regression data envelopment analysis for variable reduction." Applied Mathematics and Computation 253 (February 2015): 126–34. http://dx.doi.org/10.1016/j.amc.2014.12.050.

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29

Davis, Roy B., Sylvia Õunpuu, Dennis Tyburski, and James R. Gage. "A gait analysis data collection and reduction technique." Human Movement Science 10, no. 5 (October 1991): 575–87. http://dx.doi.org/10.1016/0167-9457(91)90046-z.

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30

Li, Lexin, and Xiangrong Yin. "Longitudinal data analysis using sufficient dimension reduction method." Computational Statistics & Data Analysis 53, no. 12 (October 2009): 4106–15. http://dx.doi.org/10.1016/j.csda.2009.04.018.

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31

Yamada, Y., N. Gouda, T. Yano, Y. Kobayashi, and Y. Niwa. "JASMINE data analysis." Proceedings of the International Astronomical Union 3, S248 (October 2007): 407–8. http://dx.doi.org/10.1017/s1743921308019704.

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Анотація:

AbstractJapan Astrometry Satellite Mission for Infrared Exploration (JASMINE) aims to construct a map of the Galactic bulge with a 10 μas accuracy. We use z-band CCD or K-band array detector to avoid dust absorption, and observe about 10 × 20 degrees area around the Galactic bulge region.In this poster, we show the observation strategy, reduction scheme, and error budget. We also show the basic design of the software for the end-to-end simulation of JASMINE, named JASMINE Simulator.

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32

Nickerson, J. A., and T. W. Sloan. "Data reduction techniques and hypothesis testing for analysis of benchmarking data." International Journal of Production Research 37, no. 8 (May 1999): 1717–41. http://dx.doi.org/10.1080/002075499190978.

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33

Blessing, R. H. "DREADD – data reduction and error analysis for single-crystal diffractometer data." Journal of Applied Crystallography 22, no. 4 (August 1, 1989): 396–97. http://dx.doi.org/10.1107/s0021889889001639.

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34

K, Bhargavi. "Data Dimensionality Reduction Techniques : Review." International Journal of Engineering Technology and Management Sciences 4, no. 4 (July 28, 2020): 62–65. http://dx.doi.org/10.46647/ijetms.2020.v04i04.010.

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Анотація:

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured. Data science is related to computer science, but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data. Because of the large amounts of data modern companies and organizations maintain, data science has become an integral part of IT. For example, a company that has petabytes of user data may use data science to develop effective ways to store, manage, and analyze the data. The company may use the scientific method to run tests and extract results that can provide meaningful insights about their users.

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35

Vats, Deepak, and Avinash Sharma. "Dimensionality Reduction Techniques: Comparative Analysis." Journal of Computational and Theoretical Nanoscience 17, no. 6 (June 1, 2020): 2684–88. http://dx.doi.org/10.1166/jctn.2020.8967.

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It has been spotted an exponential growth in terms of dimension in real world data. Some example of higher dimensional data may includes speech signal, sensor data, medical data, criminal data and data related to recommendation process for different field like news, movies (Netflix) and e-commerce. To empowering learning accuracy in the area of machine learning and enhancing mining performance one need to remove redundant feature and feature not relevant for mining and learning task from this high dimension dataset. There exist many supervised and unsupervised methodologies in literature to perform dimension reduction. The objective of paper is to present most prominent methodologies related to the field of dimension reduction and highlight advantages along with disadvantages of these algorithms which can act as starting point for beginners of this field.

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36

Hu, B., J. Rose, and B. C. Wang. "An analysis of data collection strategies and data reduction software for image-plate data." Acta Crystallographica Section A Foundations of Crystallography 52, a1 (August 8, 1996): C22. http://dx.doi.org/10.1107/s0108767396098133.

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37

Parthasarathy, G., D. C. Tomar, and Blessy John. "Analysis of Bug Triage using Data Preprocessing (Reduction) Techniques." International Journal of Computer Applications 125, no. 9 (September 17, 2015): 8–15. http://dx.doi.org/10.5120/ijca2015903002.

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38

Hu, Baoxin, Qingmou Li, and A. Smith. "Noise reduction of hyperspectral data using singular spectral analysis." International Journal of Remote Sensing 30, no. 9 (May 2009): 2277–96. http://dx.doi.org/10.1080/01431160802549344.

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39

Hu, Qi, Peng Liu, and Michael C. Huang. "Threads and Data Mapping: Affinity Analysis for Traffic Reduction." IEEE Computer Architecture Letters 15, no. 2 (July 1, 2016): 133–36. http://dx.doi.org/10.1109/lca.2015.2451172.

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40

Bevington, Philip R., D. Keith Robinson, J. Morris Blair, A. John Mallinckrodt, and Susan McKay. "Data Reduction and Error Analysis for the Physical Sciences." Computers in Physics 7, no. 4 (1993): 415. http://dx.doi.org/10.1063/1.4823194.

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41

Lazarus, Steven M., Michael E. Splitt, Michael D. Lueken, Rahul Ramachandran, Xiang Li, Sunil Movva, Sara J. Graves, and Bradley T. Zavodsky. "Evaluation of Data Reduction Algorithms for Real-Time Analysis." Weather and Forecasting 25, no. 3 (June 1, 2010): 837–51. http://dx.doi.org/10.1175/2010waf2222296.1.

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Анотація:

Abstract Data reduction tools are developed and evaluated using a data analysis framework. Simple (nonadaptive) and intelligent (adaptive) thinning algorithms are applied to both synthetic and real data and the thinned datasets are ingested into an analysis system. The approach is motivated by the desire to better represent high-impact weather features (e.g., fronts, jets, cyclones, etc.) that are often poorly resolved in coarse-resolution forecast models and to efficiently generate a set of initial conditions that best describes the current state of the atmosphere. As a precursor to real-data applications, the algorithms are applied to one- and two-dimensional synthetic datasets. Information gleaned from the synthetic experiments is used to create a thinning algorithm that combines the best aspects of the intelligent methods (i.e., their ability to detect regions of interest) while reducing the impacts of spatial irregularities in the data. Both simple and intelligent thinning algorithms are then applied to Atmospheric Infrared Sounder (AIRS) temperature and moisture profiles. For a given retention rate, background, and observation error, the optimal 1D analyses (i.e., lowest MSE) tend to have observations that are near regions of large curvature and gradients. Observation error leads to the selection of spurious data in homogeneous regions of the intelligent algorithms. In the 2D experiments, simple thinning tends to perform better within the homogeneous data regions. Analyses produced using AIRS data demonstrate that observations selected via a combination of the simple and intelligent approaches reduce clustering, provide a more even distribution along the satellite swath edges, and, in general, have lower error and comparable computational requirements compared to standard operational thinning methodologies.

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42

COOK, WADE D., and JOE ZHU. "Output deterioration with input reduction in data envelopment analysis." IIE Transactions 35, no. 3 (March 2003): 309–20. http://dx.doi.org/10.1080/07408170304361.

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43

Imani, Maryam, and Hassan Ghassemian. "Feature space discriminant analysis for hyperspectral data feature reduction." ISPRS Journal of Photogrammetry and Remote Sensing 102 (April 2015): 1–13. http://dx.doi.org/10.1016/j.isprsjprs.2014.12.024.

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44

Wang, Zheng-ming. "The reduction and analysis of GPS common view data." Chinese Astronomy and Astrophysics 25, no. 4 (October 2001): 490–98. http://dx.doi.org/10.1016/s0275-1062(01)00102-3.

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45

Poskitt, D. S., and Arivalzahan Sengarapillai. "Description length and dimensionality reduction in functional data analysis." Computational Statistics & Data Analysis 58 (February 2013): 98–113. http://dx.doi.org/10.1016/j.csda.2011.03.018.

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46

Kniess, Janine, and Samuel Oliveira. "Data reduction in sensor networks based on dispersion analysis." Computing 102, no. 5 (February 5, 2020): 1159–70. http://dx.doi.org/10.1007/s00607-020-00795-9.

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47

Deconinck, Bernard, and David O. Lovit. "Data analysis and reduction using stationary solutions of the NLS equation." Applicable Analysis 89, no. 4 (April 2010): 611–26. http://dx.doi.org/10.1080/00036810903569481.

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48

Zhang, Yaowu, Liping Zhu, and Yanyuan Ma. "Efficient dimension reduction for multivariate response data." Journal of Multivariate Analysis 155 (March 2017): 187–99. http://dx.doi.org/10.1016/j.jmva.2017.01.001.

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49

Greenacre, Michael. "Compositional Data Analysis." Annual Review of Statistics and Its Application 8, no. 1 (March 7, 2021): 271–99. http://dx.doi.org/10.1146/annurev-statistics-042720-124436.

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Анотація:

Compositional data are nonnegative data carrying relative, rather than absolute, information—these are often data with a constant-sum constraint on the sample values, for example, proportions or percentages summing to 1% or 100%, respectively. Ratios between components of a composition are important since they are unaffected by the particular set of components chosen. Logarithms of ratios (logratios) are the fundamental transformation in the ratio approach to compositional data analysis—all data thus need to be strictly positive, so that zero values present a major problem. Components that group together based on domain knowledge can be amalgamated (i.e., summed) to create new components, and this can alleviate the problem of data zeros. Once compositional data are transformed to logratios, regular univariate and multivariate statistical analysis can be performed, such as dimension reduction and clustering, as well as modeling. Alternative methodologies that come close to the ideals of the logratio approach are also considered, especially those that avoid the problem of data zeros, which is particularly acute in large bioinformatic data sets.

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50

Tao, Ran, Zhaoya Gong, Qiwei Ma, and Jean-Claude Thill. "Boosting Computational Effectiveness in Big Spatial Flow Data Analysis with Intelligent Data Reduction." ISPRS International Journal of Geo-Information 9, no. 5 (May 6, 2020): 299. http://dx.doi.org/10.3390/ijgi9050299.

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Анотація:

One of the enduring issues of spatial origin-destination (OD) flow data analysis is the computational inefficiency or even the impossibility to handle large datasets. Despite the recent advancements in high performance computing (HPC) and the ready availability of powerful computing infrastructure, we argue that the best solutions are based on a thorough understanding of the fundamental properties of the data. This paper focuses on overcoming the computational challenge through data reduction that intelligently takes advantage of the heavy-tailed distributional property of most flow datasets. We specifically propose the classification technique of head/tail breaks to this end. We test this approach with representative algorithms from three common method families, namely flowAMOEBA from flow clustering, Louvain from network community detection, and PageRank from network centrality algorithms. A variety of flow datasets are adopted for the experiments, including inter-city travel flows, cellphone call flows, and synthetic flows. We propose a standard evaluation framework to evaluate the applicability of not only the selected three algorithms, but any given method in a systematic way. The results prove that head/tail breaks can significantly improve the computational capability and efficiency of flow data analyses while preserving result quality, on condition that the analysis emphasizes the “head” part of the dataset or the flows with high absolute values. We recommend considering this easy-to-implement data reduction technique before analyzing a large flow dataset.

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