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Artículos de revistas sobre el tema "Data structures"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 1 de agosto de 2024

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1

Tamassia, Roberto. "Data structures". ACM Computing Surveys 28, n.º 1 (marzo de 1996): 23–26. http://dx.doi.org/10.1145/234313.234323.

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2

Jarc, Duane J. "Data structures". ACM SIGCSE Bulletin 26, n.º 2 (junio de 1994): 2–4. http://dx.doi.org/10.1145/181648.181651.

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3

Biswas, Ranjit. "Data structures for big data". International Journal of Computing and Optimization 1 (2014): 73–93. http://dx.doi.org/10.12988/ijco.2014.4813.

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4

Basch, Julien, Leonidas J. Guibas y John Hershberger. "Data Structures for Mobile Data". Journal of Algorithms 31, n.º 1 (abril de 1999): 1–28. http://dx.doi.org/10.1006/jagm.1998.0988.

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5

Yarosh, Svetlana y Mark Guzdial. "Narrating data structures". Journal on Educational Resources in Computing 7, n.º 4 (enero de 2008): 1–20. http://dx.doi.org/10.1145/1316450.1316456.

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6

RUUS, H. "Lexical Data Structures". Literary and Linguistic Computing 3, n.º 3 (1 de julio de 1988): 169–76. http://dx.doi.org/10.1093/llc/3.3.169.

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7

Giles, D. "Editorial - Data Structures". Computer Journal 34, n.º 5 (1 de mayo de 1991): 385. http://dx.doi.org/10.1093/comjnl/34.5.385.

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8

Demaine, Erik D., John Iacono y Stefan Langerman. "Retroactive data structures". ACM Transactions on Algorithms 3, n.º 2 (mayo de 2007): 13. http://dx.doi.org/10.1145/1240233.1240236.

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9

Louchard, G., Claire Kenyon y R. Schott. "Data Structures' Maxima". SIAM Journal on Computing 26, n.º 4 (agosto de 1997): 1006–42. http://dx.doi.org/10.1137/s0097539791196603.

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10

Nair, Achuth Sankar S. y T. Mahalakshmi. "Conceptualizing data structures". ACM SIGCSE Bulletin 36, n.º 4 (diciembre de 2004): 97–100. http://dx.doi.org/10.1145/1041624.1041668.

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11

Munro, Ian. "Succinct Data Structures". Electronic Notes in Theoretical Computer Science 91 (febrero de 2004): 3. http://dx.doi.org/10.1016/j.entcs.2003.12.002.

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12

Butaboev, Makhammadjon Tuychiyevich y Moydinjon Abdurakhmanovich Arziyev. "ESSENCE OF BIG DATA". International Journal Of Management And Economics Fundamental 4, n.º 4 (1 de abril de 2024): 40–49. http://dx.doi.org/10.37547/ijmef/volume04issue04-06.

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In this article, we consider the rapidly developing sphere in the digital economy -big data, which in the modern world allows governments, businesses and social structures to turn tons of heterogeneous information into structured knowledge. The experience of developing the big data science in the leading countries of the world is discussed and, using their example, the possibility of implementing big data in Uzbekistan is considered.

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13

Arvind, Rishiyur S. Nikhil y Keshav K. Pingali. "I-structures: data structures for parallel computing". ACM Transactions on Programming Languages and Systems 11, n.º 4 (octubre de 1989): 598–632. http://dx.doi.org/10.1145/69558.69562.

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14

Smith, N. S. "Spatial data models and data structures". Computer-Aided Design 22, n.º 3 (abril de 1990): 184–90. http://dx.doi.org/10.1016/0010-4485(90)90077-p.

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15

Steenbergen, Marco R. y Bradford S. Jones. "Modeling Multilevel Data Structures". American Journal of Political Science 46, n.º 1 (enero de 2002): 218. http://dx.doi.org/10.2307/3088424.

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16

Chen, Victor, Elena Grigorescu y Ronald de Wolf. "Error-Correcting Data Structures". SIAM Journal on Computing 42, n.º 1 (enero de 2013): 84–111. http://dx.doi.org/10.1137/110834949.

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17

Persch, G. "Editing IDL data structures". ACM SIGPLAN Notices 22, n.º 11 (noviembre de 1987): 79–86. http://dx.doi.org/10.1145/39305.39313.

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18

Mäkinen, Erkki y Markku Siermala. "Restricted universe data structures". ACM SIGCSE Bulletin 32, n.º 2 (junio de 2000): 22–24. http://dx.doi.org/10.1145/355354.355365.

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19

Solar-Lezama, Armando, Christopher Grant Jones y Rastislav Bodik. "Sketching concurrent data structures". ACM SIGPLAN Notices 43, n.º 6 (30 de mayo de 2008): 136–48. http://dx.doi.org/10.1145/1379022.1375599.

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20

Smaragdakis, Yannis. "High-level data structures". Communications of the ACM 55, n.º 12 (diciembre de 2012): 90. http://dx.doi.org/10.1145/2380656.2380676.

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21

Hartel, Pieter. "Purely functional data structures". Science of Computer Programming 34, n.º 1 (abril de 1999): 75–77. http://dx.doi.org/10.1016/s0167-6423(99)00002-7.

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22

Aspnes, James, David Eisenstat y Yitong Yin. "Low-contention data structures". Journal of Parallel and Distributed Computing 72, n.º 5 (mayo de 2012): 705–15. http://dx.doi.org/10.1016/j.jpdc.2011.10.018.

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23

Wallis, Peter. "Algorithms and data structures". Science of Computer Programming 12, n.º 2 (julio de 1989): 157–58. http://dx.doi.org/10.1016/0167-6423(89)90040-3.

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24

Gagie, Travis. "Preface – Compact Data Structures". Journal of Discrete Algorithms 43 (marzo de 2017): 1. http://dx.doi.org/10.1016/j.jda.2017.04.002.

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25

Xu, Guoqing. "Finding reusable data structures". ACM SIGPLAN Notices 47, n.º 10 (15 de noviembre de 2012): 1017–34. http://dx.doi.org/10.1145/2398857.2384690.

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26

Driscoll, James R., Neil Sarnak, Daniel D. Sleator y Robert E. Tarjan. "Making data structures persistent". Journal of Computer and System Sciences 38, n.º 1 (febrero de 1989): 86–124. http://dx.doi.org/10.1016/0022-0000(89)90034-2.

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27

Miller, Andrew, Michael Hicks, Jonathan Katz y Elaine Shi. "Authenticated data structures, generically". ACM SIGPLAN Notices 49, n.º 1 (13 de enero de 2014): 411–23. http://dx.doi.org/10.1145/2578855.2535851.

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28

Adams, Joel. "Test-driven data structures". ACM SIGCSE Bulletin 41, n.º 1 (4 de marzo de 2009): 143–47. http://dx.doi.org/10.1145/1539024.1508920.

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29

Kienel, C. y S. Kimeswenger. "Covered data structures I". Astronomy and Astrophysics Supplement Series 127, n.º 2 (enero de 1998): 319–25. http://dx.doi.org/10.1051/aas:1998370.

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30

Taylor, David J. y James P. Black. "Experimenting with data structures". Software: Practice and Experience 16, n.º 5 (mayo de 1986): 443–56. http://dx.doi.org/10.1002/spe.4380160506.

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31

Rosenberg, J. B. "Geographical Data Structures Compared: A Study of Data Structures Supporting Region Queries". IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 4, n.º 1 (enero de 1985): 53–67. http://dx.doi.org/10.1109/tcad.1985.1270098.

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32

A Alnoshan Hessah, Ayshah. "Evaluation the Performance of Data Structures: A Comparative Approach". International Journal of Science and Research (IJSR) 12, n.º 8 (5 de agosto de 2023): 880–85. http://dx.doi.org/10.21275/sr23807001651.

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33

Gog, Simon y Matthias Petri. "Optimized succinct data structures for massive data". Software: Practice and Experience 44, n.º 11 (23 de mayo de 2013): 1287–314. http://dx.doi.org/10.1002/spe.2198.

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34

tom Wörden, Henrik, Florian Spreckelsen, Stefan Luther, Ulrich Parlitz y Alexander Schlemmer. "Mapping Hierarchical File Structures to Semantic Data Models for Efficient Data Integration into Research Data Management Systems". Data 9, n.º 2 (26 de enero de 2024): 24. http://dx.doi.org/10.3390/data9020024.

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Although other methods exist to store and manage data in modern information technology, the standard solution is file systems. Therefore, keeping well-organized file structures and file system layouts can be key to a sustainable research data management infrastructure. However, file structures alone lack several important capabilities for FAIR data management: the two most significant being insufficient visualization of data and inadequate possibilities for searching and obtaining an overview. Research data management systems (RDMSs) can fill this gap, but many do not support the simultaneous use of the file system and RDMS. This simultaneous use can have many benefits, but keeping data in RDMS in synchrony with the file structure is challenging. Here, we present concepts that allow for keeping file structures and semantic data models (in RDMS) synchronous. Furthermore, we propose a specification in yaml format that allows for a structured and extensible declaration and implementation of a mapping between the file system and data models used in semantic research data management. Implementing these concepts will facilitate the re-use of specifications for multiple use cases. Furthermore, the specification can serve as a machine-readable and, at the same time, human-readable documentation of specific file system structures. We demonstrate our work using the Open Source RDMS LinkAhead (previously named “CaosDB”).

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35

Massart, B., Q. Guo, F. Questier, D. L. Massart, C. Boucon, S. de Jong y B. G. M. Vandeginste. "Data structures and data transformations for clustering chemical data". TrAC Trends in Analytical Chemistry 20, n.º 1 (enero de 2001): 35–41. http://dx.doi.org/10.1016/s0167-2940(01)90097-4.

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36

kumar, Vinesh, Dr Amit Asthana, Sunil Kumar y Dr Jayant Shekhar. "Data Representation in Big data via Succinct Data Structures". International Journal of Engineering Science and Technology 10, n.º 1 (31 de enero de 2018): 21–28. http://dx.doi.org/10.21817/ijest/2018/v10i1/181001013.

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37

Li, Shujie, Liang Li, Ruiying Geng, Min Yang, Binhua Li, Guanghu Yuan, Wanwei He et al. "Unifying Structured Data as Graph for Data-to-Text Pre-Training". Transactions of the Association for Computational Linguistics 12 (2024): 210–28. http://dx.doi.org/10.1162/tacl_a_00641.

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Abstract Data-to-text (D2T) generation aims to transform structured data into natural language text. Data-to-text pre-training has proved to be powerful in enhancing D2T generation and yields impressive performance. However, previous pre-training methods either oversimplified structured data into a sequence without considering input structures or designed training objectives tailored for a specific data structure (e.g., table or knowledge graph). In this paper, we unify different types of structured data (i.e., table, key-value data, knowledge graph) into the graph format and cast different D2T generation tasks as graph-to-text generation. To effectively exploit the structural information of the input graph, we propose a structure-enhanced pre-training method for D2T generation by designing a structure-enhanced Transformer. Concretely, we devise a position matrix for the Transformer, encoding relative positional information of connected nodes in the input graph. In addition, we propose a new attention matrix to incorporate graph structures into the original Transformer by taking the available explicit connectivity structure into account. Extensive experiments on six benchmark datasets show the effectiveness of our model. Our source codes are available at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/unid2t.

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38

Zheng, H., H. Y. Wang, N. D. Black y R. J. Winder. "Data structures, coding and classification". Technology and Health Care 18, n.º 1 (21 de febrero de 2010): 71–87. http://dx.doi.org/10.3233/thc-2010-0568.

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39

Janićijević, Stefana y Vojkan Nikolić. "Graph structures for data visualizations". Serbian Journal of Engineering Management 6, n.º 2 (2021): 24–31. http://dx.doi.org/10.5937/sjem2102024j.

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Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

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40

Manjula, V. "Graph Applications to Data Structures". Advanced Materials Research 433-440 (enero de 2012): 3297–301. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3297.

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This paper presents a topic on Graph theory and its application to data Structures which I consider basic and useful to students in APPLIED MATHEMATICS and ENGINEERING.This paper gives an elementary introduction of Graph theory and its application to data structures. Elements of Graph theory are indispensable in almost all computer Science areas .It can be used in Some areas such as syntactic analysis, fault detection, diagnosis in computers and minimal path problems. The computer representation and manipulation of graph are also discussed so that certain algorithms can be included .A major theme of this paper is to study Graph theory and its Application to data structures Furthermore I hope the students not only learn the course but also develop their analogy perceive, formulate and to solve mathematical programs Thus Graphs especially trees, binary trees are used widely in the representation of data structures this course one can develop mathematical maturity, ability to understand and create mathematical argumentsMethod of derivation is procedure given in the text books with necessary formulae and their application . Concepts and notations from discrete mathematics are useful in studying and describing objects and problems in branches of computer science, such as computer algorithms, programming languages.

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41

He, Meng, J. Ian Munro y Gelin Zhou. "Data Structures for Path Queries". ACM Transactions on Algorithms 12, n.º 4 (2 de septiembre de 2016): 1–32. http://dx.doi.org/10.1145/2905368.

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42

d'Aubigny, Gérard. "Data Analysis of Asymmetric Structures". Journal of the Royal Statistical Society: Series A (Statistics in Society) 170, n.º 3 (julio de 2007): 858–60. http://dx.doi.org/10.1111/j.1467-985x.2007.00485_9.x.

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43

Feldman, Michael B. "Teaching data structures with Ada". ACM SIGCSE Bulletin 22, n.º 2 (junio de 1990): 21–29. http://dx.doi.org/10.1145/126445.126450.

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44

Nguyen, Dung. "Design patterns for data structures". ACM SIGCSE Bulletin 30, n.º 1 (marzo de 1998): 336–40. http://dx.doi.org/10.1145/274790.274325.

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45

Hargreaves, Sarah. "Data bank for carbohydrate structures". Nature 324, n.º 6094 (noviembre de 1986): 208. http://dx.doi.org/10.1038/324208a0.

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46

Bukhovets, A. G. y E. A. Bukhovets. "Modeling of fractal data structures". Automation and Remote Control 73, n.º 2 (febrero de 2012): 381–85. http://dx.doi.org/10.1134/s0005117912020154.

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47

Pape, Tobias, Vasily Kirilichev, Carl Friedrich Bolz y Robert Hirschfeld. "Record data structures in racket". ACM SIGAPP Applied Computing Review 16, n.º 4 (13 de enero de 2017): 25–37. http://dx.doi.org/10.1145/3040575.3040578.

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48

Georgiadis, Loukas, Haim Kaplan, Nira Shafrir, Robert E. Tarjan y Renato F. Werneck. "Data structures for mergeable trees". ACM Transactions on Algorithms 7, n.º 2 (marzo de 2011): 1–30. http://dx.doi.org/10.1145/1921659.1921660.

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49

McNamara, Timothy P. "Data Structures for Cognitive Science". Contemporary Psychology 45, n.º 2 (abril de 2000): 226–27. http://dx.doi.org/10.1037/004738.

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50

Ernst, Daniel J., Daniel E. Stevenson y Paul J. Wagner. "Hybrid and custom data structures". ACM SIGCSE Bulletin 41, n.º 3 (25 de agosto de 2009): 213–17. http://dx.doi.org/10.1145/1595496.1562945.

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