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Journal articles on the topic 'Data structures (Computer science)'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 July 2024

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1

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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2

Chen, Yaozhang. "Analysis of the Development of Computer Science and its Future Trend." Applied and Computational Engineering 8, no. 1 (August 1, 2023): 341–45. http://dx.doi.org/10.54254/2755-2721/8/20230180.

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Computer science is one of the most influential innovations of the last century, including data structures, computer and network design, modeling data and information processes, and artificial intelligence. With the development of computer science, more and more people begin to pay attention to the importance of computers. This paper tells the history of computer science, and introduces some frontier technology of computer science. Computers have greatly improved people's work and lifestyle, developed modern society, and become an indispensable part of people's lives. Computers have entered the era of artificial intelligence, which has a major impact on the development of human society.

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3

Tiwari, Adarsh, Pradeep Kanyal, Himanshu Panchal, and Manjot Kaur Bhatia. "Computer Science and High Dimensional Data Modelling." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 517–20. http://dx.doi.org/10.22214/ijraset.2022.47922.

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Abstract: The need to grasp large database structures is a very important issue in biological and life science. This review paper is aimed toward quantitative medical researchers searching for guidance in modeling large numbers of variables in medical research, how this relates to straightforward linear models and therefore the geometry that underlies their analysis. Issues reviewed include LASSO-related approaches, principal-component based analysis, and problems with model stability and interpretation. Model misspecification issues associated with potential nonlinearities are examined, as is that the Bayesian perspective on these issues.

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4

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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5

Almanza-Cortés, Daniel Felipe, Manuel Felipe Del Toro-Salazar, Ricardo Andrés Urrego-Arias, Pedro Guillermo Feijóo-García, and Fernando De la Rosa-Rosero. "Scaffolded Block-based Instructional Tool for Linear Data Structures: A Constructivist Design to Ease Data Structures’ Understanding." International Journal of Emerging Technologies in Learning (iJET) 14, no. 10 (May 30, 2019): 161. http://dx.doi.org/10.3991/ijet.v14i10.10051.

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Data Structures courses commonly introduce topics involving high levels of abstraction and complexity, requiring significant effort from instructors and apprentices to achieve positive outcomes from the teaching-learning process. Despite the multiple studies that have occurred within the Computer Science Education (CSE) community to understand the experiences novice programmers may have when learning how to program, there is still a lack of exploration and research on understanding these experiences in scenarios different from first-year Computer Science (CS) courses. Looking further from CS introductory courses, this paper presents the results of a pilot study that evaluated the interaction of a group of CS Colombian students with DStBlocks, which is a scaffolded block-based instructional technology, designed and developed to ease linear data structures understanding. The findings and results of this pilot study are favorable, corresponding to tests centered on user experience and learning impact.

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6

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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7

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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8

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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9

Panangaden, Prakash, and Clark Verbrugge. "Generating irregular partitionable data structures." Theoretical Computer Science 238, no. 1-2 (May 2000): 31–80. http://dx.doi.org/10.1016/s0304-3975(98)00226-6.

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10

Elmasry, Amr, Meng He, J. Ian Munro, and Patrick K. Nicholson. "Dynamic range majority data structures." Theoretical Computer Science 647 (September 2016): 59–73. http://dx.doi.org/10.1016/j.tcs.2016.07.039.

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11

Gagie, Travis, Meng He, Gonzalo Navarro, and Carlos Ochoa. "Tree path majority data structures." Theoretical Computer Science 833 (September 2020): 107–19. http://dx.doi.org/10.1016/j.tcs.2020.05.039.

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12

Schalk, Andrea, and José Juan Palacios-Perez. "Concrete Data Structures as Games." Electronic Notes in Theoretical Computer Science 122 (March 2005): 193–210. http://dx.doi.org/10.1016/j.entcs.2004.06.058.

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13

Gupta, Ankur, Wing-Kai Hon, Rahul Shah, and Jeffrey Scott Vitter. "Compressed data structures: Dictionaries and data-aware measures." Theoretical Computer Science 387, no. 3 (November 2007): 313–31. http://dx.doi.org/10.1016/j.tcs.2007.07.042.

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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

Herlihy, Maurice. "Technical perspectiveHighly concurrent data structures." Communications of the ACM 52, no. 5 (May 2009): 99. http://dx.doi.org/10.1145/1506409.1506430.

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16

Hartline, Jason D., Edwin S. Hong, Alexander E. Mohr, William R. Pentney, and Emily C. Rocke. "Characterizing History Independent Data Structures." Algorithmica 42, no. 1 (February 9, 2005): 57–74. http://dx.doi.org/10.1007/s00453-004-1140-z.

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17

Stein, W., S. Hassfeld, and J. Muhling. "Tracing of Thin Tubular Structures in Computer Tomographic Data." Computer Aided Surgery 3, no. 2 (January 1998): 83–88. http://dx.doi.org/10.3109/10929089809148133.

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18

Taubenfeld, Gadi. "Contention-sensitive data structures and algorithms." Theoretical Computer Science 677 (May 2017): 41–55. http://dx.doi.org/10.1016/j.tcs.2017.03.017.

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19

J., Girish Raguvir, Manas Jyoti Kashyop, and N. S. Narayanaswamy. "Dynamic data structures for interval coloring." Theoretical Computer Science 838 (October 2020): 126–42. http://dx.doi.org/10.1016/j.tcs.2020.06.024.

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20

Colvin, Robert, Simon Doherty, and Lindsay Groves. "Verifying Concurrent Data Structures by Simulation." Electronic Notes in Theoretical Computer Science 137, no. 2 (July 2005): 93–110. http://dx.doi.org/10.1016/j.entcs.2005.04.026.

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21

Nash, John B., and Pauline A. Moroz. "An Examination of the Factor Structures of the Computer Attitude Scale." Journal of Educational Computing Research 17, no. 4 (December 1997): 341–56. http://dx.doi.org/10.2190/ngdu-h73e-xmr3-tg5j.

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Previous research regarding the popular Computer Attitude Scale (CAS) has indicated that the computer confidence and computer anxiety subscales measure the same trait. This study, utilizing data yielded from 208 educators, obtained estimates of the reliability of the four subscale version of the forty item CAS; provided detailed information regarding the factor patterns of the CAS subscales; and provided evidence about the differential validity of the CAS among four groups with differing intensity of computer usage. Correlations and exploratory factor analysis were used to analyze the data. The results confirm that the confidence and anxiety subscales are a continuum. A new, smaller, subscale was created to reflect this relationship. Further, a new factor, attitudes toward academic endeavors associated with computer training, was named. The CAS may now be interpreted as a thirty-four-item scale addressing computer liking, perceived usefulness of computers, computer confidence/anxiety, and attitudes toward academic endeavors associated with computer training.

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22

Esponda-Argüero, Margarita. "Techniques for Visualizing Data Structures in Algorithmic Animations." Information Visualization 9, no. 1 (January 29, 2009): 31–46. http://dx.doi.org/10.1057/ivs.2008.26.

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This paper deals with techniques for the design and production of appealing algorithmic animations and their use in computer science education. A good visual animation is both a technical artifact and a work of art that can greatly enhance the understanding of an algorithm's workings. In the first part of the paper, I show that awareness of the composition principles used by other animators and visual artists can help programmers to create better algorithmic animations. The second part shows how to incorporate those ideas in novel animation systems, which represent data structures in a visually intuitive manner. The animations described in this paper have been implemented and used in the classroom for courses at university level.

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23

Zhang, Qin. "Can data structures treat us fairly?" Communications of the ACM 65, no. 8 (August 2022): 82. http://dx.doi.org/10.1145/3543843.

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24

Goller, N. E. "Hybrid Data Structures Defined by Indirection." Computer Journal 28, no. 1 (January 1, 1985): 44–53. http://dx.doi.org/10.1093/comjnl/28.1.44.

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25

Shavit, Nir. "Data structures in the multicore age." Communications of the ACM 54, no. 3 (March 2011): 76–84. http://dx.doi.org/10.1145/1897852.1897873.

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26

Patterson, Evan, Owen Lynch, and James Fairbanks. "Categorical Data Structures for Technical Computing." Compositionality 4 (December 28, 2022): 5. http://dx.doi.org/10.32408/compositionality-4-5.

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Many mathematical objects can be represented as functors from finitely-presented categories C to Set. For instance, graphs are functors to Set from the category with two parallel arrows. Such functors are known informally as C-sets. In this paper, we describe and implement an extension of C-sets having data attributes with fixed types, such as graphs with labeled vertices or real-valued edge weights. We call such structures acsets, short for attributed C-sets. Derived from previous work on algebraic databases, acsets are a joint generalization of graphs and data frames. They also encompass more elaborate graph-like objects such as wiring diagrams and Petri nets with rate constants. We develop the mathematical theory of acsets and then describe a generic implementation in the Julia programming language, which uses advanced language features to achieve performance comparable with specialized data structures.

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27

Gorshkov, P. V. "Rational data structures and their applications." Cybernetics 25, no. 6 (1990): 760–65. http://dx.doi.org/10.1007/bf01069776.

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28

Andon, F. I., V. A. Reznichenko, and A. E. Yashunin. "A calculus for hierarchical data structures." Cybernetics 20, no. 6 (1985): 785–90. http://dx.doi.org/10.1007/bf01072163.

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29

Guessarian, Irène. "Some Fixpoint Techniques in Algebraic Structures and Applications to Computer Science." Fundamenta Informaticae 10, no. 4 (October 1, 1987): 387–413. http://dx.doi.org/10.3233/fi-1987-10405.

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This paper recalls some fixpoint theorems in ordered algebraic structures and surveys some ways in which these theorems are applied in computer science. We describe via examples three main types of applications: in semantics and proof theory, in logic programming and in deductive data bases.

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30

Shand, Mark A. "Algorithms for corner stitched data-structures." Algorithmica 2, no. 1-4 (November 1987): 61–80. http://dx.doi.org/10.1007/bf01840349.

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31

Gostev, Yu G. "Generating power of atomic grammars on data structures. Encoding of data structures by strings of symbols." Cybernetics 24, no. 5 (September 1988): 575–82. http://dx.doi.org/10.1007/bf01255669.

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32

ETIENNE, F. "The Impact of Modern Graphics Tools on Science, and their Limitations." International Journal of Modern Physics C 02, no. 01 (March 1991): 58–65. http://dx.doi.org/10.1142/s012918319100007x.

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Within the last few years the range of scientific applications for which computer graphics is used has become extremely large. However, not all scientists require the same level of computing power. Until recently the software interface to graphics display systems has been provided by the manufacturers of the hardware. This generated interest in the possibility of using graphics standards. Another important issue is related to the deluge of data generated by super-computers and high-volume data sources which make it impossible for users to have an overall knowledge of either the data structures or the application programs. Partial solutions can be found in emerging products providing an interactive computational environment for scientific visualization. Some of the characteristics required for graphics hardware are presented. From a hardware perspective, graphics computing involves the use of a graphical computer system with sufficient power and functionality that the user can manipulate and interact with displayed objects. To achieve such a level of performance computers are usually designed as networked workstations with access to local graphics capabilities. Finally, it is made clear that the main computer graphics applications are scientific activities. From high energy physics experiments with wireframe event displays up to medical imaging with interactive volume rendering, scientific visualization is not simply displaying data from data intensive sources. Fields of computer graphics like image processing, computer aided design, signal processing and user interfaces provide tools helping researchers to understand and steer scientific computation.

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33

Ferragina, Paolo, Fabrizio Lillo, and Giorgio Vinciguerra. "On the performance of learned data structures." Theoretical Computer Science 871 (June 2021): 107–20. http://dx.doi.org/10.1016/j.tcs.2021.04.015.

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34

Hains, Gaétan, Frédéric Loulergue, and John Mullins. "Concrete data structures and functional parallel programming." Theoretical Computer Science 258, no. 1-2 (May 2001): 233–67. http://dx.doi.org/10.1016/s0304-3975(00)00010-4.

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35

Ono, Hirotaka, Kazuhisa Makino, and Toshihide Ibaraki. "Logical analysis of data with decomposable structures." Theoretical Computer Science 289, no. 2 (October 2002): 977–95. http://dx.doi.org/10.1016/s0304-3975(01)00413-3.

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36

Delgado-Friedrichs, Olaf. "Data structures and algorithms for tilings I." Theoretical Computer Science 303, no. 2-3 (July 2003): 431–45. http://dx.doi.org/10.1016/s0304-3975(02)00500-5.

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37

Andy, Gill. "Debugging Haskell by Observing Intermediate Data Structures." Electronic Notes in Theoretical Computer Science 41, no. 1 (August 2001): 1. http://dx.doi.org/10.1016/s1571-0661(05)80538-9.

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38

Fariña, Antonio, Susana Ladra, Oscar Pedreira, and Ángeles S. Places. "Rank and Select for Succinct Data Structures." Electronic Notes in Theoretical Computer Science 236 (April 2009): 131–45. http://dx.doi.org/10.1016/j.entcs.2009.03.019.

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39

Tasiran, Serdar, and Shaz Qadeer. "Runtime Refinement Checking of Concurrent Data Structures." Electronic Notes in Theoretical Computer Science 113 (January 2005): 163–79. http://dx.doi.org/10.1016/j.entcs.2004.01.028.

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40

Ábrahám, Erika, Marc Herbstritt, Bernd Becker, and Martin Steffen. "Bounded Model Checking with Parametric Data Structures." Electronic Notes in Theoretical Computer Science 174, no. 3 (May 2007): 3–16. http://dx.doi.org/10.1016/j.entcs.2006.12.019.

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41

Rakesh, Palepu Narasimha. "A Data Science Approach to Bioinformatics." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3860–69. http://dx.doi.org/10.22214/ijraset.2021.37221.

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Computer aided drug design (CADD) which uses the computational advance towards to develop, discover and scrutinize and examine drugs and alike biologically agile molecules. CADD is a specialized stream which uses the computational techniques to mimic drug-receptor interactions. CADD procedures are so much dependent on the tools of bioinformatics, databases & applications. There are so many advantages of computer aided drug discovery; it saves lot of time which is one of the main advantages followed by low cost and more accuracy. CADD required less manpower to work. There are different types of CADD such as ligand and structure based design. Objectives of the Computer aided drug design are to boost up the screening process, to test the rational of drug design, to efficiently screen and to remove hopeless ones as early as possible. In Drug designing the selected molecule should be organic small molecule, complementary in shape to the target and oppositely charged to the biomolecular target. The molecule will interacts and binds with the target which activates or inhibits the function of a biomolecule such as a protein or lipid. The main basic goal in the drug design is to forecast whether a given molecule will bind to target and if thus how strongly. Molecular mechanics techniques also used to provide the semi quantitative prediction of the binding affinity. These techniques use machine learning, linear regression, neural nets or other statistical methods to derive predictive binding affinity equations. Preferably, the computational technique will be able to forecast the affinity prior to a compound is synthesized, saving huge time and cost. Computational techniques have quickened the discovery by decreasing the number of iterations required and have often produced the novel structures.

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42

Gautam, Ganesh, Himanshu Arora, Jayendra choudhary, and Aryan Raj. "Data Privacy and Ethical Concerns in AI and Computer Science." Industrial Engineering Journal 51, no. 08 (2022): 25–31. http://dx.doi.org/10.36893/iej.2022.v51i8.025-031.

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As Artificial Intelligence and Computer Science continue to grow and turn out to be a part of our day by day lives, the ethical issues and issues about information privateness have turn out to be extra essential. This evaluation paper thoroughly seems at how statistics privateness and ethics join in AI and CS. It explores the demanding situations and possibilities that arise when AI and CS technology acquire, system, and observe quite a few data. The paper talks about the ethical problems due to AI algorithms and self-running structures. It looks into troubles like bias, transparency, accountability, and fairness. Additionally, it talks about the converting guidelines about facts privateness and how they affect AI and CS, in particular in phrases of records protection, consent, and the proper to be forgotten. The paper also discusses the ethical frameworks and guidelines created to cope with those complicated issues. It uses numerous resources like research articles, case research, and coverage files to offer an updated and multidisciplinary view of the subject. It ends with the aid of citing the brand new developments and future directions in this subject, inclusive of the importance of different professionals working collectively to address these challenges. In quick, this thorough assessment paper is a beneficial resource for researchers, policymakers, and practitioners who need to recognize and address the complicated issues of facts privateness and ethics in AI and pc technological know-how.

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43

Li, Xinlin, Yiming Wang, Xiaoyu Bi, Yalu Xu, Haojiang Ying, and Yiyang Chen. "Multi-Dimensional Data Analysis Platform (MuDAP): A Cognitive Science Data Toolbox." Symmetry 16, no. 4 (April 22, 2024): 503. http://dx.doi.org/10.3390/sym16040503.

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Researchers in cognitive science have long been interested in modeling human perception using statistical methods. This requires maneuvers because these multiple dimensional data are always intertwined with complex inner structures. The previous studies in cognitive sciences commonly applied principal component analysis (PCA) to truncate data dimensions when dealing with data with multiple dimensions. This is not necessarily because of its merit in terms of mathematical algorithm, but partly because it is easy to conduct with commonly accessible statistical software. On the other hand, dimension reduction might not be the best analysis when modeling data with no more than 20 dimensions. Using state-of-the-art techniques, researchers in various research disciplines (e.g., computer vision) classified data with more than hundreds of dimensions with neural networks and revealed the inner structure of the data. Therefore, it might be more sophisticated to process human perception data directly with neural networks. In this paper, we introduce the multi-dimensional data analysis platform (MuDAP), a powerful toolbox for data analysis in cognitive science. It utilizes artificial intelligence as well as network analysis, an analysis method that takes advantage of data symmetry. With the graphic user interface, a researcher, with or without previous experience, could analyze multiple dimensional data with great ease.

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44

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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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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45

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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46

Jagannathan, Suresh. "TS/Scheme: Distributed data structures in Lisp." LISP and Symbolic Computation 7, no. 4 (1994): 291–314. http://dx.doi.org/10.1007/bf01018613.

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47

Athanassoulis, Manos, Stratos Idreos, and Dennis Shasha. "Data Structures for Data-Intensive Applications: Tradeoffs and Design Guidelines." Foundations and Trends® in Databases 13, no. 1-2 (2023): 1–168. http://dx.doi.org/10.1561/1900000059.

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48

Prokop, Yu V., O. H. Trofymenko, and O. V. Dykyi. "RESEARCH OF APPROACHES TO TEACHING THE COURSE “ALGORITHMS AND DATA STRUCTURES” FOR COMPUTER SCIENCE STUDENTS." Scientific notes of Taurida National V.I. Vernadsky University. Series: Technical Sciences 1, no. 2 (2021): 216–20. http://dx.doi.org/10.32838/2663-5941/2021.2-1/34.

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49

Hambrusch, Susanne E., and Chuan-Ming Liu. "Data replication in static tree structures." Information Processing Letters 86, no. 4 (May 2003): 197–202. http://dx.doi.org/10.1016/s0020-0190(02)00503-3.

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50

Chanchary, Farah, and Anil Maheshwari. "Time Windowed Data Structures for Graphs." Journal of Graph Algorithms and Applications 23, no. 2 (2019): 191–226. http://dx.doi.org/10.7155/jgaa.00489.

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