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Relevant bibliographies by topics / Multidimensional array

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Academic literature on the topic 'Multidimensional array'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Multidimensional array"

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Lu, Meng, Marius Appel, and Edzer Pebesma. "Multidimensional Arrays for Analysing Geoscientific Data." ISPRS International Journal of Geo-Information 7, no. 8 (August 3, 2018): 313. http://dx.doi.org/10.3390/ijgi7080313.

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Geographic data is growing in size and variety, which calls for big data management tools and analysis methods. To efficiently integrate information from high dimensional data, this paper explicitly proposes array-based modeling. A large portion of Earth observations and model simulations are naturally arrays once digitalized. This paper discusses the challenges in using arrays such as the discretization of continuous spatiotemporal phenomena, irregular dimensions, regridding, high-dimensional data analysis, and large-scale data management. We define categories and applications of typical array operations, compare their implementation in open-source software, and demonstrate dimension reduction and array regridding in study cases using Landsat and MODIS imagery. It turns out that arrays are a convenient data structure for representing and analysing many spatiotemporal phenomena. Although the array model simplifies data organization, array properties like the meaning of grid cell values are rarely being made explicit in practice.

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Holm, A., W. Wu, H. S. Slaastad, L. Goullart, and D. Carrillo. "Multidimensional antibody array analysis." New Biotechnology 27 (April 2010): S74. http://dx.doi.org/10.1016/j.nbt.2010.01.211.

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Rogozovskyi, Oleg A. "Multidimensional array signal processing with multidimensional matrix operations." Journal of the Acoustical Society of America 105, no. 2 (February 1999): 1253. http://dx.doi.org/10.1121/1.426010.

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Fitzgerald, Steven M., and Rodney R. Oldehoeft. "Update-in-Place Analysis for True Multidimensional Arrays." Scientific Programming 5, no. 2 (1996): 147–60. http://dx.doi.org/10.1155/1996/493673.

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Applicative languages have been proposed for defining algorithms for parallel architectures because they are implicitly parallel and lack side effects. However, straightforward implementations of applicative-language compilers may induce large amounts of copying to preserve program semantics. The unnecessary copying of data can increase both the execution time and the memory requirements of an application. To eliminate the unnecessary copying of data, the Sisal compiler uses both build-in-place and update-in-place analyses. These optimizations remove unnecessary array copy operations through compile-time analysis. Both build-in-place and update-in-place are based on hierarchical ragged arrays, i.e., the vector-of-vectors array model. Although this array model is convenient for certain applications, many optimizations are precluded, e.g., vectorization. To compensate for this deficiency, new languages, such as Sisal 2.0, have extended array models that allow for both high-level array operations to be performed and efficient implementations to be devised. In this article, we introduce a new method to perform update-in-place analysis that is applicable to arrays stored either in hierarchical or in contiguous storage. Consequently, the array model that is appropriate for an application can be selected without the loss of performance. Moreover, our analysis is more amenable for distributed memory and large software systems.

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Edwards, H. Carter, Daniel Sunderland, Vicki Porter, Chris Amsler, and Sam Mish. "Manycore Performance-Portability: Kokkos Multidimensional Array Library." Scientific Programming 20, no. 2 (2012): 89–114. http://dx.doi.org/10.1155/2012/917630.

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Large, complex scientific and engineering application code have a significant investment in computational kernels to implement their mathematical models. Porting these computational kernels to the collection of modern manycore accelerator devices is a major challenge in that these devices have diverse programming models, application programming interfaces (APIs), and performance requirements. The Kokkos Array programming model provides library-based approach to implement computational kernels that are performance-portable to CPU-multicore and GPGPU accelerator devices. This programming model is based upon three fundamental concepts: (1) manycore compute devices each with its own memory space, (2) data parallel kernels and (3) multidimensional arrays. Kernel execution performance is, especially for NVIDIA® devices, extremely dependent on data access patterns. Optimal data access pattern can be different for different manycore devices – potentially leading to different implementations of computational kernels specialized for different devices. The Kokkos Array programming model supports performance-portable kernels by (1) separating data access patterns from computational kernels through a multidimensional array API and (2) introduce device-specific data access mappings when a kernel is compiled. An implementation of Kokkos Array is available through Trilinos [Trilinos website, http://trilinos.sandia.gov/, August 2011].

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LI, Jiang, Yusuke ATSUMARI, Hiromasa KUBO, Yuichi OGISHIMA, Satoru YOKOTA, Hakaru TAMUKOH, and Masatoshi SEKINE. "A Multidimensional Configurable Processor Array — Vocalise." IEICE Transactions on Information and Systems E98.D, no. 2 (2015): 313–24. http://dx.doi.org/10.1587/transinf.2014edp7219.

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Flesch, Aime, and An Nguyen-Dinh. "Stack based multidimensional ultrasonic transducer array." Journal of the Acoustical Society of America 115, no. 5 (2004): 1865. http://dx.doi.org/10.1121/1.1757128.

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King, James E. "Number concepts in animals: A multidimensional array." Behavioral and Brain Sciences 11, no. 4 (December 1988): 590. http://dx.doi.org/10.1017/s0140525x00053565.

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Moran, Mark L., Roy J. Greenfield, Steven A. Arcone, and Allan J. Delaney. "Multidimensional GPR array processing using Kirchhoff migration." Journal of Applied Geophysics 43, no. 2-4 (March 2000): 281–95. http://dx.doi.org/10.1016/s0926-9851(99)00065-8.

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Chun-Yuan Lin, Jen-Shiuh Liu, and Yeh-Ching Chung. "Efficient representation scheme for multidimensional array operations." IEEE Transactions on Computers 51, no. 3 (March 2002): 327–45. http://dx.doi.org/10.1109/12.990130.

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Dissertations / Theses on the topic "Multidimensional array"

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Dehmel, Andreas. "A compression engine for multidimensional array database systems /." München : Utz, Wiss, 2002. http://www.loc.gov/catdir/toc/fy0610/2004450648.html.

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Widmann, Norbert. "Efficient operation execution on multidimensional array data." [S.l.] : [s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=962069515.

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Lustosa, Hermano Lourenço Souza. "Managing numerical simulation data using a multidimensional array representation." Laboratório Nacional de Computação Científica, 2015. https://tede.lncc.br/handle/tede/250.

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Submitted by Maria Cristina (library@lncc.br) on 2017-04-18T17:56:33Z No. of bitstreams: 1 Dissertação (Hermano Lustosa).pdf: 11841214 bytes, checksum: c30da4b19ca9fd69bf262318a593729b (MD5)
Approved for entry into archive by Maria Cristina (library@lncc.br) on 2017-04-18T17:56:51Z (GMT) No. of bitstreams: 1 Dissertação (Hermano Lustosa).pdf: 11841214 bytes, checksum: c30da4b19ca9fd69bf262318a593729b (MD5)
Made available in DSpace on 2017-04-18T17:57:04Z (GMT). No. of bitstreams: 1 Dissertação (Hermano Lustosa).pdf: 11841214 bytes, checksum: c30da4b19ca9fd69bf262318a593729b (MD5) Previous issue date: 2015-12-09
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Scientific applications, such as numerical simulations, generate an ever increasing amount of data that needs to be eficiently managed. As most traditional row-store Database Management Systems are not tailored for the analytical workload usually required by such applications, alternative approaches, e. g., columnstore and multidimensional arrays, can offer better querying processing time. In this work, we propose new techniques for managing the data produced by numerical simulations, such as those coming from HeMoLab, by using multidimensional array technologies. We take advantage of multidimensional array that nicely models the dimensions and variables used in numerical simulations. The eficient mapping of the simulation output file onto a multi-dimensional array is not simple. A naive solution may lead to sparse arrays, impacting query response time, specially when the simulation uses irregular meshes to model its physical domain. We propose novel strategies to solve these problems by defining an eficient mapping of coordinate values in numerical simulations to evenly distribute cells in array chunks with the use of equi-depth histograms and space-filling curves. We evaluated our techniques through experiments over real-world data, comparing them with a columnar and a row-store relational systems. The results indicate that multidimensional arrays and column-stores are much faster than a tradivitional row-store system for queries issued over a larger amount of simulation data. Also, the results help to identify the scenarios in which using multidimensional arrays is the most eficient approach, and the ones in which they are outperformed by the relational column-store approach.
Aplicações científicas geram uma crescente massa de dados que precisam ser analisados e gerenciados eficientemente. Uma vez que os tradicionais bancos de dados relacionais não são projetados para a carga de trabalho predominantemente analítica exigida por essas aplicações, abordagens alternativas, tais como, matrizes multidimensionais e bancos de dados colunares, podem oferecer melhores tempos de execução de consultas. Neste trabalho, propomos o uso de novas tecnologias para a gerência de dados produzidos por simulações numéricas, similares às desenvolvidas pelo HeMoLab. O modelo de matrizes multidimensionais permite a modelagem elegante de dimensões e variáveis usadas em simulações numéricas. Entretanto, o mapeamento dos dados de saída de uma simulação em uma matriz multidimensional não é simples. Uma solução ingênua pode levar a criação de matrizes excessivamente esparsas, impactando o tempo de resposta do sistema, especialmente quando a simulação utiliza uma malha irregular para modelar o seu domínio físico. Nós propomos novas estratégias para resolver esses problemas através da definição de um mapeamento eficiente de valores de coordenadas com o uso de histogramas e curvas de preenchimento espacial. Nós avaliamos nossas técnicas através de experimentos feitos com dados reais, comparando-as com bancos de dados relacionais. Os resultados indicam que tanto iv matrizes multidimensionais quanto bancos de dados colunares são muitas vezes mais rápidos que bancos de dados relacionais tradicionais para consultas avaliando uma grande quantidade de dados. Além disso, os resultados auxiliam na identificação de cenários nos quais matrizes multidimensionais são mais eficientes, e nos quais elas são superadas por uma abordagem envolvendo o uso de um banco de dados colunar.

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Tan, Chor Min. "Multidimensional channel characterisation with efficient high-resolution array signal processing algorithms." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414133.

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Seneviratne, Vishwa. "Design and Rapid-prototyping of Multidimensional-DSP Beamformers Using the ROACH-2 FPGA Platform." University of Akron / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1488149940846702.

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Lau, Kwok-Ho. "Dependence structures in multidimensional arrays." Diss., University of Pretoria, 2016. http://hdl.handle.net/2263/60821.

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In the process of data acquisition the information obtained are more than often contaminated by noise. To purify the data smoothers are designed to remove the noise. The LULU operators are such smoothers, more speci cally, they are designed to remove impulsive noise. Carl Rohwer and his collaborators devel- oped the LULU operators in one dimension in the last four decades and, more recently, the operators have been extended to higher dimensions by Roumen Anguelov and Inger Fabris-Rotelli [2]. The prop- erties in shape preservation and total variation preservation are extended from one-dimensional LULU operators. This allows for smoothing with the operators in images. However, because their de nition uses a morphological concept of a connection, the question of how complex the connectivity should be therefore arises. Using the results from correlation analysis, we explore the extent at which the pixels of an image depend on its neighbours and establish the complexity of the connectivity for LULU operators in two-dimensions. In addition, as a measure of how e ective the LULU smoothers remove noise, we examine the noise extractions by the operators for images.
Dissertation (MSc)--University of Pretoria, 2016.
Statistics
MSc
Unrestricted

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Ritsch, Roland. "Optimization and evaluation of array queries in database management systems." [S.l. : s.n.], 1999. http://deposit.ddb.de/cgi-bin/dokserv?idn=959772502.

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Suh, Young-Joo. "Realizations of efficient collective communication in multidimensional processor arrays." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13343.

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Dahlberg, Timoteus. "Compact Representation and Efficient Manipulation of Sparse Multidimensional Arrays." Thesis, Umeå universitet, Institutionen för datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-92841.

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Efficient manipulation of sparse multidimensional arrays, or tensors, is of interest because their decompositions have applications in many different areas. These areas include neuroscience, machine learning, psychometrics, data mining, numerical analysis, and more. This thesis aims to develop the performance-critical parts of a library for manipulating sparse multidimensional arrays by focusing on sorting them in one or more dimensions—a fundamental operation on which many other operations can be built. High performance is achieved by tailoring algorithms to a compact representation scheme. Evaluation is done on different algorithms and implementation techniques. The result is shown to be 20 to 70 times faster than qsort in the C standard library. The resulting library is open source.

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Reiner, Bernd. "HEAVEN eine hierarchische Speicher- und Archivierungsumgebung für multidimensionale Array-Datenbankmanamgement-Systeme." Saarbrücken VDM Verlag Dr. Müller, 2005. http://d-nb.info/988496577/04.

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Books on the topic "Multidimensional array"

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Dehmel, Andreas. A compression engine for multidimensional array database systems. München: Utz Verlag, 2002.

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Efficient one-, two-, and multidimensional high resolution array signal processing. Aachen: Shaker, 1997.

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Richards, Randall George. A beam pattern design procedure for multidimensional sonar arrays employing minimum variance beamforming. Springfield, Va: Available from the National Technical Information Service, 1990.

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Kim, Youn, and Sander L. Gilman, eds. The Oxford Handbook of Music and the Body. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190636234.001.0001.

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The presence of the phenomenological body is central to music in all of its varieties and contradictions. With the explosion of scholarly works on the body in virtually every field in the humanities, the social as well as the biomedical sciences, the question of how such a complex understanding of the body is related to music, with its own complexity, has been investigated within specific disciplinary perspectives. The Oxford Handbook of Music and the Body brings together these particular aspects of such relationships in a broad context and provides a platform for the discussion of the multidimensional interfaces of music and the body. It is organized into six sections, each discussing the topics that define the field: the moving and performing body; the musical brain and psyche; embodied mind, embodied rhythm; the disabled and sexual body; music as medicine; and the multimodal body. Connecting a wide array of diverse perspectives and presenting a survey of research and practice highlighting different facets, the Handbook provides an introduction into the rich world of music and the body.

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Book chapters on the topic "Multidimensional array"

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van Ballegooij, Alex R. "RAM: A Multidimensional Array DBMS." In Current Trends in Database Technology - EDBT 2004 Workshops, 154–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30192-9_15.

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Amir, Amihood, and Gad M. Landau. "Fast Parallel and Serial Multidimensional Approximate Array Matching." In Sequences, 3–24. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3352-7_1.

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Masudul Ahsan, Sk Md, and K. M. Azharul Hasan. "An Implementation Scheme for Multidimensional Extendable Array Operations and Its Evaluation." In Informatics Engineering and Information Science, 136–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25462-8_12.

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Reiner, Bernd, Karl Hahn, Gabriele Höfling, and Peter Baumann. "Hierarchical Storage Support and Management for Large-Scale Multidimensional Array Database Management Systems." In Lecture Notes in Computer Science, 689–700. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46146-9_68.

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Reiner, Bernd, and Karl Hahn. "HEAVEN: A Hierarchical Storage and Archive Environment for Multidimensional Array Database Management Systems." In Advances in Database Technology - EDBT 2004, 854–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24741-8_57.

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Prettyman, Steve. "Multidimensional Arrays." In PHP Arrays, 41–56. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2556-1_3.

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de Carvalho Junior, Francisco Heron, Cenez Araújo Rezende, Jefferson de Carvalho Silva, Francisco José Lins Magalhães, and Renato Caminha Juaçaba-Neto. "On the Performance of Multidimensional Array Representations in Programming Languages Based on Virtual Execution Machines." In Programming Languages, 31–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40922-6_3.

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Ogihara, Mitsunori. "Multidimensional Arrays." In Fundamentals of Java Programming, 357–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89491-1_14.

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Sutherland, Stuart. "Multidimensional arrays." In Verilog — 2001, 38–39. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1713-9_17.

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Winnie, Doug. "Multidimensional Arrays." In Essential Java for AP CompSci, 219–23. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6183-5_46.

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Conference papers on the topic "Multidimensional array"

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Machlin, Rona. "Index-based multidimensional array queries." In the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1265530.1265555.

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Roy, R., M. Goldburg, B. Ottersten, L. Swindlehurst, M. Viberg, and T. Kailath. "Recent advances in multidimensional sensor array signal processing." In Sixth Multidimensional Signal Processing Workshop,. IEEE, 1989. http://dx.doi.org/10.1109/mdsp.1989.97081.

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Boyer, Remy, and Pierre Comon. "Rectified ALS algorithm for multidimensional harmonic retrieval." In 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2016. http://dx.doi.org/10.1109/sam.2016.7569678.

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Otoo, E. J., Hairong Wang, and Gideon Nimako. "Multidimensional Sparse Array Storage for Data Analytics." In 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2016. http://dx.doi.org/10.1109/hpcc-smartcity-dss.2016.0216.

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Tsuji, Tatsuo, Akihiro Hara, and Ken Higuchi. "An extendible multidimensional array system for MOLAP." In the 2006 ACM symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1141277.1141394.

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Sorensen, Mikael, and Lieven De Lathauwer. "Multidimensional ESPRIT: A coupled canonical polyadic decomposition approach." In 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2014. http://dx.doi.org/10.1109/sam.2014.6882437.

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de Oliveira Castro, Pablo, Stéphane Louise, and Denis Barthou. "A Multidimensional Array Slicing DSL for Stream Programming." In 2010 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS). IEEE, 2010. http://dx.doi.org/10.1109/cisis.2010.135.

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Zhao, Yihong, Prasad M. Deshpande, and Jeffrey F. Naughton. "An array-based algorithm for simultaneous multidimensional aggregates." In the 1997 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/253260.253288.

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Seamons, Kent E., and Marianne Winslett. "An efficient abstract interface for multidimensional array I/O." In the 1994 ACM/IEEE conference. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/602770.602877.

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Ge, Tingjian, David Grabiner, and Stan Zdonik. "Monte Carlo query processing of uncertain multidimensional array data." In 2011 IEEE International Conference on Data Engineering (ICDE 2011). IEEE, 2011. http://dx.doi.org/10.1109/icde.2011.5767887.

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Reports on the topic "Multidimensional array"

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Rotem, Doron, Ekow J. Otoo, and Sridhar Seshadri. Chunking of Large Multidimensional Arrays. Office of Scientific and Technical Information (OSTI), February 2007. http://dx.doi.org/10.2172/927033.

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