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Relevant bibliographies by topics / Computer algorithms. Data structures (Computer science)

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Academic literature on the topic 'Computer algorithms. Data structures (Computer science)'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Computer algorithms. Data structures (Computer science)"

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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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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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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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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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Laube, Ulrich, and Markus E. Nebel. "Maximum likelihood analysis of algorithms and data structures." Theoretical Computer Science 411, no. 1 (January 2010): 188–212. http://dx.doi.org/10.1016/j.tcs.2009.09.025.

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Vitter, Jeffrey Scott. "Algorithms and Data Structures for External Memory." Foundations and Trends® in Theoretical Computer Science 2, no. 4 (2006): 305–474. http://dx.doi.org/10.1561/0400000014.

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Franaszek, P. A., P. Heidelberger, D. E. Poff, and J. T. Robinson. "Algorithms and data structures for compressed-memory machines." IBM Journal of Research and Development 45, no. 2 (March 2001): 245–58. http://dx.doi.org/10.1147/rd.452.0245.

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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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Knop, A., S. Lovett, S. McGuire, and W. Yuan. "Guest Column." ACM SIGACT News 52, no. 2 (June 14, 2021): 46–70. http://dx.doi.org/10.1145/3471469.3471479.

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Communication complexity studies the amount of communication necessary to compute a function whose value depends on information distributed among several entities. Yao [Yao79] initiated the study of communication complexity more than 40 years ago, and it has since become a central eld in theoretical computer science with many applications in diverse areas such as data structures, streaming algorithms, property testing, approximation algorithms, coding theory, and machine learning. The textbooks [KN06,RY20] provide excellent overviews of the theory and its applications.

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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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Dissertations / Theses on the topic "Computer algorithms. Data structures (Computer science)"

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Costa, Andre. "Analytic modelling of agent-based network routing algorithms." Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09PH/09phc8373.pdf.

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Karras, Panagiotis. "Data structures and algorithms for data representation in constrained environments." Thesis, Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38897647.

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宋永健 and Wing-kin Sung. "Fast labeled tree comparison via better matching algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31239316.

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Sung, Wing-kin. "Fast labeled tree comparison via better matching algorithms /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20229999.

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Jain, Jhilmil Cross James H. "User experience design and experimental evaluation of extensible and dynamic viewers for data structures." Auburn, Ala., 2007. http://repo.lib.auburn.edu/2006%20Fall/Dissertations/JAIN_JHILMIL_3.pdf.

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黎少斌 and Shiao-bun Lai. "Trading off time for space for the string matching problem." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31214216.

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Lai, Shiao-bun. "Trading off time for space for the string matching problem /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18061795.

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Benjamin, Jim Isaac. "Quadtree algorithms for image processing /." Online version of thesis, 1991. http://hdl.handle.net/1850/11078.

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Mak, Vivian. "Algorithms for proximity problems in the presence of obstacles /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B21414944.

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Bae, Sung Eun. "Sequential and Parallel Algorithms for the Generalized Maximum Subarray Problem." Thesis, University of Canterbury. Computer Science and Software Engineering, 2007. http://hdl.handle.net/10092/1202.

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The maximum subarray problem (MSP) involves selection of a segment of consecutive array elements that has the largest possible sum over all other segments in a given array. The efficient algorithms for the MSP and related problems are expected to contribute to various applications in genomic sequence analysis, data mining or in computer vision etc. The MSP is a conceptually simple problem, and several linear time optimal algorithms for 1D version of the problem are already known. For 2D version, the currently known upper bounds are cubic or near-cubic time. For the wider applications, it would be interesting if multiple maximum subarrays are computed instead of just one, which motivates the work in the first half of the thesis. The generalized problem of K-maximum subarray involves finding K segments of the largest sum in sorted order. Two subcategories of the problem can be defined, which are K-overlapping maximum subarray problem (K-OMSP), and K-disjoint maximum subarray problem (K-DMSP). Studies on the K-OMSP have not been undertaken previously, hence the thesis explores various techniques to speed up the computation, and several new algorithms. The first algorithm for the 1D problem is of O(Kn) time, and increasingly efficient algorithms of O(K² + n logK) time, O((n+K) logK) time and O(n+K logmin(K, n)) time are presented. Considerations on extending these results to higher dimensions are made, which contributes to establishing O(n³) time for 2D version of the problem where K is bounded by a certain range. Ruzzo and Tompa studied the problem of all maximal scoring subsequences, whose definition is almost identical to that of the K-DMSP with a few subtle differences. Despite slight differences, their linear time algorithm is readily capable of computing the 1D K-DMSP, but it is not easily extended to higher dimensions. This observation motivates a new algorithm based on the tournament data structure, which is of O(n+K logmin(K, n)) worst-case time. The extended version of the new algorithm is capable of processing a 2D problem in O(n³ + min(K, n) · n² logmin(K, n)) time, that is O(n³) for K ≤ n/log n For the 2D MSP, the cubic time sequential computation is still expensive for practical purposes considering potential applications in computer vision and data mining. The second half of the thesis investigates a speed-up option through parallel computation. Previous parallel algorithms for the 2D MSP have huge demand for hardware resources, or their target parallel computation models are in the realm of pure theoretics. A nice compromise between speed and cost can be realized through utilizing a mesh topology. Two mesh algorithms for the 2D MSP with O(n) running time that require a network of size O(n²) are designed and analyzed, and various techniques are considered to maximize the practicality to their full potential.

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Books on the topic "Computer algorithms. Data structures (Computer science)"

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Lewis, Harry R. Data structures & their algorithms. New York, NY: HarperCollins Publishers, 1991.

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Algorithms and data structures. Englewood Cliffs, N.J: Prentice-Hall, 1986.

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Algorithms 2: Data structures and search algorithms. Chichester: Wiley, 1990.

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Robert, Lafore, ed. Data structures & algorithms in Java. Corte Madera, CA: Waite Group Press, 1998.

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Robert, Lafore, ed. Data structures & algorithms in Java. 2nd ed. Indianapolis, Ind: Sams, 2003.

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Wood, Derick. Data structures, algorithms and performance. Reading, Mass: Addison-Wesley, 1992.

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Data structures, algorithms, and performance. Reading, Mass: Addison-Wesley, 1993.

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Antoni, Kreczmar, and Rytter Wojciech, eds. Analysis of algorithms and data structures. Wokingham, England: Addison-Wesley, 1991.

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Goodrich, Michael T. Data structures and algorithms in C++. 2nd ed. Hoboken, N.J: Wiley, 2011.

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1960-, Tamassia Roberto, and Mount David M, eds. Data structures and algorithms in C++. 2nd ed. Hoboken, N.J: Wiley, 2011.

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Book chapters on the topic "Computer algorithms. Data structures (Computer science)"

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Italiano, Giuseppe F. "Resilient Algorithms and Data Structures." In Lecture Notes in Computer Science, 13–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13073-1_3.

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Erwig, Martin. "Graph algorithms = iteration + data structures?" In Graph-Theoretic Concepts in Computer Science, 277–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56402-0_54.

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Taubenfeld, Gadi. "Contention-Sensitive Data Structures and Algorithms." In Lecture Notes in Computer Science, 157–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04355-0_17.

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Hains, Gaétan, and John Mullins. "Array structures and data-parallel algorithms." In Lecture Notes in Computer Science, 409–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0024730.

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Devroye, Luc. "Probabilistic analysis of algorithms and data structures." In Lecture Notes in Computer Science, 230. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51542-9_20.

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Bieri, Hanspeter. "Teaching Algorithms and Data Structures: 10 Personal Observations." In Lecture Notes in Computer Science, 39–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36477-3_4.

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Nymeyer, Albert, and Kairong Qian. "Heuristic Search Algorithms Based on Symbolic Data Structures." In Lecture Notes in Computer Science, 966–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24581-0_83.

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Brookes, Stephen, and Shai Geva. "Continuous functions and parallel algorithms on concrete data structures." In Lecture Notes in Computer Science, 326–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55511-0_17.

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Chen, Danny Z., and Haitao Wang. "Improved Points Approximation Algorithms Based on Simplicial Thickness Data Structures." In Lecture Notes in Computer Science, 363–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19222-7_36.

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Fürlinger, Karl, Colin Glass, Jose Gracia, Andreas Knüpfer, Jie Tao, Denis Hünich, Kamran Idrees, Matthias Maiterth, Yousri Mhedheb, and Huan Zhou. "DASH: Data Structures and Algorithms with Support for Hierarchical Locality." In Lecture Notes in Computer Science, 542–52. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14313-2_46.

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Conference papers on the topic "Computer algorithms. Data structures (Computer science)"

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Martinez, Cristian A., Carlos Nocera, Diego A. Rodriguez, Ismael Orozco, and Eduardo Xamena. "Teaching practice in algorithms and data structures." In 2017 36th International Conference of the Chilean Computer Science Society (SCCC). IEEE, 2017. http://dx.doi.org/10.1109/sccc.2017.8405106.

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Silva, Davi Bernardo, Rafael de Lima Aguiar, Diogo Steinke Dvconlo, and Carlos N. Silla. "Recent Studies About Teaching Algorithms (CS1) and Data Structures (CS2) for Computer Science Students." In 2019 IEEE Frontiers in Education Conference (FIE). IEEE, 2019. http://dx.doi.org/10.1109/fie43999.2019.9028702.

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King, Jason. "Combining Theory and Practice in Data Structures & Algorithms Course Projects." In SIGCSE '21: The 52nd ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3408877.3432476.

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Lucas, Joan M. "Illustrating the Interaction of Algorithms and Data Structures Using the Matching Problem." In SIGCSE '15: The 46th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2676723.2677212.

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Su, Simon, Edward Zhang, Paul Denny, and Nasser Giacaman. "A Game-Based Approach for Teaching Algorithms and Data Structures using Visualizations." In SIGCSE '21: The 52nd ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3408877.3432520.

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Zhong, Fay. "Designing Adaptive Learning Objects for Enhanced Student Engagement in Data Structures and Algorithms." In SIGCSE '18: The 49th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3159450.3162323.

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Färnqvist, Tommy, and Fredrik Heintz. "Competition and Feedback through Automated Assessment in a Data Structures and Algorithms Course." In ITiCSE '16: Innovation and Technology in Computer Science Education Conference 2016. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2899415.2899454.

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Kapoor, Amanpreet, and Christina Gardner-Mccune. "Introducing a Technical Interview Preparation Activity in a Data Structures and Algorithms Course." In ITiCSE 2021: 26th ACM Conference on Innovation and Technology in Computer Science Education. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3456565.3460033.

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Poulsen, Seth. "Using Spatio-Algorithmic Problem Solving Strategies to Increase Access to Data Structures." In ITiCSE '20: Innovation and Technology in Computer Science Education. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3341525.3394004.

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R.M. Debenham, Evan, and Roberto Solis-Oba. "New Algorithms for Computing Field of Vision over 2D Grids." In 6th International Conference on Computer Science, Engineering And Applications (CSEA 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101801.

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The aim of this paper is to propose new algorithms for Field of Vision (FOV) computation which improve on existing work at high resolutions. FOV refers to the set of locations that are visible from a specific position in a scene of a computer game. We summarize existing algorithms for FOV computation, describe their limitations, and present new algorithms which aim to address these limitations. We first present an algorithm which makes use of spatial data structures in a way which is new for FOV calculation. We then present a novel technique which updates a previously calculated FOV, rather than recalculating an FOV from scratch. We compare our algorithms to existing FOV algorithms and show they provide substantial improvements to running time. Our algorithms provide the largest improvement over existing FOV algorithms at large grid sizes, thus allowing the possibility of the design of high resolution FOV-based video games.

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