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Journal articles on the topic 'Data structures'

To see the other types of publications on this topic, follow the link: Data structures.

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Tamassia, Roberto. "Data structures." ACM Computing Surveys 28, no. 1 (March 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, no. 2 (June 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, and John Hershberger. "Data Structures for Mobile Data." Journal of Algorithms 31, no. 1 (April 1999): 1–28. http://dx.doi.org/10.1006/jagm.1998.0988.

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5

Yarosh, Svetlana, and Mark Guzdial. "Narrating data structures." Journal on Educational Resources in Computing 7, no. 4 (January 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, no. 3 (July 1, 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, no. 5 (May 1, 1991): 385. http://dx.doi.org/10.1093/comjnl/34.5.385.

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8

Demaine, Erik D., John Iacono, and Stefan Langerman. "Retroactive data structures." ACM Transactions on Algorithms 3, no. 2 (May 2007): 13. http://dx.doi.org/10.1145/1240233.1240236.

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9

Louchard, G., Claire Kenyon, and R. Schott. "Data Structures' Maxima." SIAM Journal on Computing 26, no. 4 (August 1997): 1006–42. http://dx.doi.org/10.1137/s0097539791196603.

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10

Nair, Achuth Sankar S., and T. Mahalakshmi. "Conceptualizing data structures." ACM SIGCSE Bulletin 36, no. 4 (December 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 (February 2004): 3. http://dx.doi.org/10.1016/j.entcs.2003.12.002.

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12

Butaboev, Makhammadjon Tuychiyevich, and Moydinjon Abdurakhmanovich Arziyev. "ESSENCE OF BIG DATA." International Journal Of Management And Economics Fundamental 4, no. 4 (April 1, 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, and Keshav K. Pingali. "I-structures: data structures for parallel computing." ACM Transactions on Programming Languages and Systems 11, no. 4 (October 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, no. 3 (April 1990): 184–90. http://dx.doi.org/10.1016/0010-4485(90)90077-p.

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15

Steenbergen, Marco R., and Bradford S. Jones. "Modeling Multilevel Data Structures." American Journal of Political Science 46, no. 1 (January 2002): 218. http://dx.doi.org/10.2307/3088424.

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16

Chen, Victor, Elena Grigorescu, and Ronald de Wolf. "Error-Correcting Data Structures." SIAM Journal on Computing 42, no. 1 (January 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, no. 11 (November 1987): 79–86. http://dx.doi.org/10.1145/39305.39313.

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18

Mäkinen, Erkki, and Markku Siermala. "Restricted universe data structures." ACM SIGCSE Bulletin 32, no. 2 (June 2000): 22–24. http://dx.doi.org/10.1145/355354.355365.

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19

Solar-Lezama, Armando, Christopher Grant Jones, and Rastislav Bodik. "Sketching concurrent data structures." ACM SIGPLAN Notices 43, no. 6 (May 30, 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, no. 12 (December 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, no. 1 (April 1999): 75–77. http://dx.doi.org/10.1016/s0167-6423(99)00002-7.

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22

Aspnes, James, David Eisenstat, and Yitong Yin. "Low-contention data structures." Journal of Parallel and Distributed Computing 72, no. 5 (May 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, no. 2 (July 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 (March 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, no. 10 (November 15, 2012): 1017–34. http://dx.doi.org/10.1145/2398857.2384690.

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26

Driscoll, James R., Neil Sarnak, Daniel D. Sleator, and Robert E. Tarjan. "Making data structures persistent." Journal of Computer and System Sciences 38, no. 1 (February 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, and Elaine Shi. "Authenticated data structures, generically." ACM SIGPLAN Notices 49, no. 1 (January 13, 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, no. 1 (March 4, 2009): 143–47. http://dx.doi.org/10.1145/1539024.1508920.

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29

Kienel, C., and S. Kimeswenger. "Covered data structures I." Astronomy and Astrophysics Supplement Series 127, no. 2 (January 1998): 319–25. http://dx.doi.org/10.1051/aas:1998370.

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30

Taylor, David J., and James P. Black. "Experimenting with data structures." Software: Practice and Experience 16, no. 5 (May 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, no. 1 (January 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, no. 8 (August 5, 2023): 880–85. http://dx.doi.org/10.21275/sr23807001651.

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33

Gog, Simon, and Matthias Petri. "Optimized succinct data structures for massive data." Software: Practice and Experience 44, no. 11 (May 23, 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, and Alexander Schlemmer. "Mapping Hierarchical File Structures to Semantic Data Models for Efficient Data Integration into Research Data Management Systems." Data 9, no. 2 (January 26, 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, and B. G. M. Vandeginste. "Data structures and data transformations for clustering chemical data." TrAC Trends in Analytical Chemistry 20, no. 1 (January 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, and Dr Jayant Shekhar. "Data Representation in Big data via Succinct Data Structures." International Journal of Engineering Science and Technology 10, no. 1 (January 31, 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, and R. J. Winder. "Data structures, coding and classification." Technology and Health Care 18, no. 1 (February 21, 2010): 71–87. http://dx.doi.org/10.3233/thc-2010-0568.

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39

Janićijević, Stefana, and Vojkan Nikolić. "Graph structures for data visualizations." Serbian Journal of Engineering Management 6, no. 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 (January 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, and Gelin Zhou. "Data Structures for Path Queries." ACM Transactions on Algorithms 12, no. 4 (September 2, 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, no. 3 (July 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, no. 2 (June 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, no. 1 (March 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, no. 6094 (November 1986): 208. http://dx.doi.org/10.1038/324208a0.

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46

Bukhovets, A. G., and E. A. Bukhovets. "Modeling of fractal data structures." Automation and Remote Control 73, no. 2 (February 2012): 381–85. http://dx.doi.org/10.1134/s0005117912020154.

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47

Pape, Tobias, Vasily Kirilichev, Carl Friedrich Bolz, and Robert Hirschfeld. "Record data structures in racket." ACM SIGAPP Applied Computing Review 16, no. 4 (January 13, 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, and Renato F. Werneck. "Data structures for mergeable trees." ACM Transactions on Algorithms 7, no. 2 (March 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, no. 2 (April 2000): 226–27. http://dx.doi.org/10.1037/004738.

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50

Ernst, Daniel J., Daniel E. Stevenson, and Paul J. Wagner. "Hybrid and custom data structures." ACM SIGCSE Bulletin 41, no. 3 (August 25, 2009): 213–17. http://dx.doi.org/10.1145/1595496.1562945.

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