Academic literature on the topic 'Data structures (Computer science)'
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Journal articles on the topic "Data structures (Computer science)"
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.
Full textChen, 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.
Full textTiwari, 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.
Full textMunro, 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.
Full textAlmanza-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.
Full textGiles, D. "Editorial - Data Structures." Computer Journal 34, no. 5 (May 1, 1991): 385. http://dx.doi.org/10.1093/comjnl/34.5.385.
Full textSmaragdakis, Yannis. "High-level data structures." Communications of the ACM 55, no. 12 (December 2012): 90. http://dx.doi.org/10.1145/2380656.2380676.
Full textLouchard, 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.
Full textPanangaden, 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.
Full textElmasry, 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.
Full textDissertations / Theses on the topic "Data structures (Computer science)"
Obiedat, Mohammad. "Incrementally Sorted Lattice Data Structures." Thesis, The George Washington University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3732474.
Full textData structures are vital entities that strongly impact the efficiency of several software applications. Compactness, predictable memory access patterns, and good temporal and spacial locality of the structure's operations are increasingly becoming essential factors in the selection of a data structure for a specific application. In general, the less data we store and move the better for efficiency and power consumption, especially in infrastructure software and applications for hand-held devices like smartphones. In this dissertation, we extensively study a data structure named lattice data structure (LDS) that is as compact and suitable for memory hierarchies as the array, yet with a rich structure that enables devising procedures with better time bounds.
To achieve performance similar to the performance of the optimal O(log(N)) time complexity of the searching operations of other structures, we provide a hybrid searching algorithm that can be implemented by searching the lattice using the basic searching algorithm when the degree of the sortedness of the lattice is less than or equal to 0.9h, and the jump searching algorithm when the degree of the sortedness of the lattice is greater than 0.9h. A sorting procedure that can be used, during the system idle time, to incrementally increase the degree of sortedness of the lattice is given. We also provide randomized and parallel searching algorithms that can be used instead of the usual jump-and-walk searching algorithms.
A lattice can be represented by a one-dimensional array, where each cell is represented by one array element. The worst case time complexity of the basic LDS operations and the average time complexity of some of the order-statistic operations are better than the corresponding time complexities of most of other data structures operations. This makes the LDS a good choice for memory-constrained systems, for systems where power consumption is a critical issue, and for real-time systems. A potential application of the LDS is to use it as an index structure for in-memory databases.
Kabiri, Chimeh Mozhgan. "Data structures for SIMD logic simulation." Thesis, University of Glasgow, 2016. http://theses.gla.ac.uk/7521/.
Full textEastep, Jonathan M. (Jonathan Michael). "Smart data structures : an online machine learning approach to multicore data structures." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65967.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 175-180).
As multicores become prevalent, the complexity of programming is skyrocketing. One major difficulty is eciently orchestrating collaboration among threads through shared data structures. Unfortunately, choosing and hand-tuning data structure algorithms to get good performance across a variety of machines and inputs is a herculean task to add to the fundamental difficulty of getting a parallel program correct. To help mitigate these complexities, this work develops a new class of parallel data structures called Smart Data Structures that leverage online machine learning to adapt themselves automatically. We prototype and evaluate an open source library of Smart Data Structures for common parallel programming needs and demonstrate signicant improvements over the best existing algorithms under a variety of conditions. Our results indicate that learning is a promising technique for balancing and adapting to complex, time-varying tradeoffs and achieving the best performance available.
by Jonathan M. Eastep.
Ph.D.
Butts, Robert O. "Heterogeneous construction of spatial data structures." Thesis, University of Colorado at Denver, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1588178.
Full textLinear spatial trees are typically constructed in two discrete, consecutive stages: calculating location codes, and sorting the spatial data according to the codes. Additionally, a GPU R-tree construction algorithm exists which likewise consists of sorting the spatial data and calculating nodes' bounding boxes. Current GPUs are approximately three orders of magnitude faster than CPUs for perfectly vectorizable problems. However, the best known GPU sorting algorithms only achieve 10-20 times speedup over sequential CPU algorithms. Both calculating location codes and bounding boxes are perfectly vectorizable problems. We thus investigate the construction of linear quadtrees, R-trees, and linear k-d trees using the GPU for location code and bounding box calculation, and parallel CPU algorithms for sorting. In this endeavor, we show how existing GPU linear quadtree and R-tree construction algorithms may be modified to be heterogeneous, and we develop a novel linear k-d tree construction algorithm which uses an existing parallel CPU quicksort partition algorithm. We implement these heterogeneous construction algorithms, and we show that heterogeneous construction of spatial data structures can approach the speeds of homogeneous GPU algorithms, while freeing the GPU to be used for better vectorizable problems.
Toussaint, Richard. "Data structures and operations for geographical information." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23945.
Full textFirst, we attempt to evaluate the efficiency of multipaging on static files and to suggest possible modifications to the standard algorithm to better serve spatial data.
Our solution to this problem consists in compressing the pages that overflow. Because geographical information is often a representation of occurences of Nature, we hypothesize that Fractal Geometry, which serves to formalize a mathematical description of such elements, could provide the theoretical background to derive an efficient fractal-based compression algorithm. An appreciable improvement is obtained by compressing the pages of the multipaged administrative regions data that exceed their capacity: $ alpha=0.7272$ and $ pi=1.0$.
The outcome of these experiments led us to elaborate a mixed system based on two relatively different approaches: multipaging and fractal-based data compression. The first part consisted in the implementation of the standard static multipaging algorithm using a relational database management system named Relix. The other approach was developed using the C programming language to accommodate some particularities of the multipaged spatial data. The preliminary results were encouraging and allowed us to establish the parameters for a more formal implementation. Also, it brought out the limits of the compression method in view of the intended usage of the data. (Abstract shortened by UMI.)
Eid, Ashraf. "Efficient associative data structures for bitemporal databases." Thesis, University of Ottawa (Canada), 2002. http://hdl.handle.net/10393/6226.
Full textZhu, Yingchun 1968. "Optimizing parallel programs with dynamic data structures." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=36745.
Full textIn this thesis, I present two compiler techniques to reduce the overhead of remote memory accesses for dynamic data structure based applications: locality techniques and communication optimizations. Locality techniques include a static locality analysis, which statically estimates when an indirect reference via a pointer can be safely assumed to be a local access, and dynamic locality checks, which consists of runtime tests to identify local accesses. Communication techniques include: (1) code movement to issue remote reads earlier and writes later; (2) code transformations to replace repeated/redundant remote accesses with one access; and (3) transformations to block or pipeline a group of remote requests together. Both locality and communication techniques have been implemented and incorporated into our EARTH-McCAT compiler framework, and a series of experiments have been conducted to evaluate these techniques. The experimental results show that we are able to achieve up to 26% performance improvement with each technique alone, and up to 29% performance improvement when both techniques are applied together.
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.
Full textJain, 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.
Full textPǎtraşcu, Mihai. "Lower bound techniques for data structures." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45866.
Full textIncludes bibliographical references (p. 135-143).
We describe new techniques for proving lower bounds on data-structure problems, with the following broad consequences: * the first [omega](lg n) lower bound for any dynamic problem, improving on a bound that had been standing since 1989; * for static data structures, the first separation between linear and polynomial space. Specifically, for some problems that have constant query time when polynomial space is allowed, we can show [omega](lg n/ lg lg n) bounds when the space is O(n - polylog n). Using these techniques, we analyze a variety of central data-structure problems, and obtain improved lower bounds for the following: * the partial-sums problem (a fundamental application of augmented binary search trees); * the predecessor problem (which is equivalent to IP lookup in Internet routers); * dynamic trees and dynamic connectivity; * orthogonal range stabbing. * orthogonal range counting, and orthogonal range reporting; * the partial match problem (searching with wild-cards); * (1 + [epsilon])-approximate near neighbor on the hypercube; * approximate nearest neighbor in the l[infinity] metric. Our new techniques lead to surprisingly non-technical proofs. For several problems, we obtain simpler proofs for bounds that were already known.
by Mihai Pǎtraşcu.
Ph.D.
Books on the topic "Data structures (Computer science)"
Keogh, James Edward. Data structures demystified. New York: McGraw-Hill/Osborne, 2004.
Find full textYedidyah, Langsam, and Augenstein Moshe J, eds. Data structures usingC. Englewood Cliffs, N.J: Prentice Hall, 1990.
Find full textC, Walsh Brian, ed. Computer users' data book. Oxford [Oxfordshire]: Blackwell Scientific Publications, 1986.
Find full textLewis, Harry R. Data structures & their algorithms. New York, NY: HarperCollins Publishers, 1991.
Find full textFeldman, Michael B. Data structures with Ada. Reading, Mass: Addison-Wesley Pub. Co., 1993.
Find full textTenenbaum, Aaron M. Data structures using PASCAL. 2nd ed. Englewood Cliffs,NJ: Prentice-Hall International, 1986.
Find full textR, Hubbard J. Data structures with Java. Upper Saddle River, N.J: Pearson Prentice Hall, 2004.
Find full textDale, Nell B. C++ plus data structures. 2nd ed. Sudbury, Mass: Jones and Bartlett Publishers, 2001.
Find full textSingh, Bhagat. Introduction to data structures. St. Paul: West Pub. Co., 1985.
Find full text1967-, Zachmann Gabriel, ed. Geometric data structures for computer graphics. Wellesley, MA: A K Peters, 2005.
Find full textBook chapters on the topic "Data structures (Computer science)"
Dawe, M. S., and C. M. Dawe. "Data Structures." In PROLOG for Computer Science, 81–115. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-2031-5_8.
Full textSkiena, Steven S. "Data Structures." In Texts in Computer Science, 439–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54256-6_15.
Full textSkiena, Steven S. "Data Structures." In Texts in Computer Science, 69–108. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54256-6_3.
Full textGrillmeyer, Oliver. "Data Structures." In Exploring Computer Science with Scheme, 169–97. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2937-5_7.
Full textLaaksonen, Antti. "Data Structures." In Undergraduate Topics in Computer Science, 51–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72547-5_5.
Full textLaaksonen, Antti. "Data Structures." In Undergraduate Topics in Computer Science, 57–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39357-1_5.
Full textCormode, Graham. "Summary Data Structures for Massive Data." In Lecture Notes in Computer Science, 78–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39053-1_9.
Full textRaman, Rajeev, Venkatesh Raman, and S. Srinivasa Rao. "Succinct Dynamic Data Structures." In Lecture Notes in Computer Science, 426–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44634-6_39.
Full textCarlsson, Svante, and Jingsen Chen. "Searching rigid data structures." In Lecture Notes in Computer Science, 446–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0030864.
Full textNielsen, Frank. "Object-Oriented Data-Structures." In Undergraduate Topics in Computer Science, 1–22. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-339-6_8.
Full textConference papers on the topic "Data structures (Computer science)"
Beckwith, Brandon, and Dewan Ahmed. "Gamification of Undergraduate Computer Science Data Structures." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00129.
Full textNarman, Husnu S., Cameron Berry, Alex Canfield, Logan Carpenter, Jeremy Giese, Neil Loftus, and Isabella Schrader. "Augmented Reality for Teaching Data Structures in Computer Science." In 2020 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, 2020. http://dx.doi.org/10.1109/ghtc46280.2020.9342932.
Full textPatrascu, Mihai. "(Data) STRUCTURES." In 2008 IEEE 49th Annual IEEE Symposium on Foundations of Computer Science (FOCS). IEEE, 2008. http://dx.doi.org/10.1109/focs.2008.69.
Full textHubbard, Aleata. "Linear Data Structures." In SIGCSE '19: The 50th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3287324.3293796.
Full textWeiss, Mark Allen. "Data Structures Courses." In SIGCSE '15: The 46th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2676723.2694801.
Full textKortsarts, Yana. "Session details: Algorithms and data structures." In SIGCSE05: Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/3259446.
Full textCoffey, John W. "Integrating theoretical and empirical computer science in a data structures course." In Proceeding of the 44th ACM technical symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2445196.2445211.
Full textMcVey, Bonita. "Session details: Algorithms and data structures." In SIGCSE04: Technical Symposium on Computer Science Education 2004. New York, NY, USA: ACM, 2004. http://dx.doi.org/10.1145/3244203.
Full textHaiming Lai, Ming Xu, Jian Xu, Yizhi Ren, and Ning Zheng. "Evaluating data storage structures of MapReduce." In 2013 8th International Conference on Computer Science & Education (ICCSE). IEEE, 2013. http://dx.doi.org/10.1109/iccse.2013.6554067.
Full textVanDeGrift, Tammy. "POGIL Activities in Data Structures." In SIGCSE '17: The 48th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3017680.3017697.
Full textReports on the topic "Data structures (Computer science)"
Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, July 2022. http://dx.doi.org/10.32370/iaj.2710.
Full textFateman, Richard J., and Carl G. Ponder. Speed and Data Structures in Computer Algebra Systems. Fort Belvoir, VA: Defense Technical Information Center, August 1987. http://dx.doi.org/10.21236/ada197131.
Full textNechaev, V., Володимир Миколайович Соловйов, and A. Nagibas. Complex economic systems structural organization modelling. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1118.
Full textOleksiuk, Vasyl P., and Olesia R. Oleksiuk. Exploring the potential of augmented reality for teaching school computer science. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4404.
Full textWachen, John, Steven McGee, Don Yanek, and Valerie Curry. Coaching Teachers of Exploring Computer Science: A Report on Four Years of Implementation. The Learning Partnership, January 2021. http://dx.doi.org/10.51420/report.2021.1.
Full textWachen, John, Mark Johnson, Steven McGee, Faythe Brannon, and Dennis Brylow. Computer Science Teachers as Change Agents for Broadening Participation: Exploring Perceptions of Equity. The Learning Partnership, April 2021. http://dx.doi.org/10.51420/conf.2021.2.
Full textGoncharenko, Tatiana, Nataliia Yermakova-Cherchenko, and Yelyzaveta Anedchenko. Experience in the Use of Mobile Technologies as a Physics Learning Method. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4468.
Full textJohnson, Mark, John Wachen, and Steven McGee. Entrepreneurship, Federalism, and Chicago: Setting the Computer Science Agenda at the Local and National Levels. The Learning Partnership, April 2020. http://dx.doi.org/10.51420/conf.2020.1.
Full textChamberlain, C. A., and K. Lochhead. Data modeling as applied to surveying and mapping data. Natural Resources Canada/CMSS/Information Management, 1988. http://dx.doi.org/10.4095/331263.
Full textTucker Blackmon, Angelicque. Formative External Evaluation and Data Analysis Report Year Three: Building Opportunities for STEM Success. Innovative Learning Center, LLC, August 2020. http://dx.doi.org/10.52012/mlfk2041.
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