Literatura científica selecionada sobre o tema "Data structures (Computer science)"
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Artigos de revistas sobre o assunto "Data structures (Computer science)"
Manjula, V. "Graph Applications to Data Structures". Advanced Materials Research 433-440 (janeiro de 2012): 3297–301. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3297.
Texto completo da fonteChen, Yaozhang. "Analysis of the Development of Computer Science and its Future Trend". Applied and Computational Engineering 8, n.º 1 (1 de agosto de 2023): 341–45. http://dx.doi.org/10.54254/2755-2721/8/20230180.
Texto completo da fonteTiwari, Adarsh, Pradeep Kanyal, Himanshu Panchal e Manjot Kaur Bhatia. "Computer Science and High Dimensional Data Modelling". International Journal for Research in Applied Science and Engineering Technology 10, n.º 12 (31 de dezembro de 2022): 517–20. http://dx.doi.org/10.22214/ijraset.2022.47922.
Texto completo da fonteMunro, Ian. "Succinct Data Structures". Electronic Notes in Theoretical Computer Science 91 (fevereiro de 2004): 3. http://dx.doi.org/10.1016/j.entcs.2003.12.002.
Texto completo da fonteAlmanza-Cortés, Daniel Felipe, Manuel Felipe Del Toro-Salazar, Ricardo Andrés Urrego-Arias, Pedro Guillermo Feijóo-García e 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, n.º 10 (30 de maio de 2019): 161. http://dx.doi.org/10.3991/ijet.v14i10.10051.
Texto completo da fonteGiles, D. "Editorial - Data Structures". Computer Journal 34, n.º 5 (1 de maio de 1991): 385. http://dx.doi.org/10.1093/comjnl/34.5.385.
Texto completo da fonteSmaragdakis, Yannis. "High-level data structures". Communications of the ACM 55, n.º 12 (dezembro de 2012): 90. http://dx.doi.org/10.1145/2380656.2380676.
Texto completo da fonteLouchard, G., Claire Kenyon e 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.
Texto completo da fontePanangaden, Prakash, e Clark Verbrugge. "Generating irregular partitionable data structures". Theoretical Computer Science 238, n.º 1-2 (maio de 2000): 31–80. http://dx.doi.org/10.1016/s0304-3975(98)00226-6.
Texto completo da fonteElmasry, Amr, Meng He, J. Ian Munro e Patrick K. Nicholson. "Dynamic range majority data structures". Theoretical Computer Science 647 (setembro de 2016): 59–73. http://dx.doi.org/10.1016/j.tcs.2016.07.039.
Texto completo da fonteTeses / dissertações sobre o assunto "Data structures (Computer science)"
Obiedat, Mohammad. "Incrementally Sorted Lattice Data Structures". Thesis, The George Washington University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3732474.
Texto completo da fonteData 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/.
Texto completo da fonteEastep, 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.
Texto completo da fonteThis 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.
Texto completo da fonteLinear 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.
Texto completo da fonteFirst, 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.
Texto completo da fonteZhu, 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.
Texto completo da fonteIn 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.
Texto completo da fonteJain, 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.
Texto completo da fontePǎtraşcu, Mihai. "Lower bound techniques for data structures". Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45866.
Texto completo da fonteIncludes 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.
Livros sobre o assunto "Data structures (Computer science)"
Keogh, James Edward. Data structures demystified. New York: McGraw-Hill/Osborne, 2004.
Encontre o texto completo da fonteYedidyah, Langsam, e Augenstein Moshe J, eds. Data structures usingC. Englewood Cliffs, N.J: Prentice Hall, 1990.
Encontre o texto completo da fonteC, Walsh Brian, ed. Computer users' data book. Oxford [Oxfordshire]: Blackwell Scientific Publications, 1986.
Encontre o texto completo da fonteLewis, Harry R. Data structures & their algorithms. New York, NY: HarperCollins Publishers, 1991.
Encontre o texto completo da fonteFeldman, Michael B. Data structures with Ada. Reading, Mass: Addison-Wesley Pub. Co., 1993.
Encontre o texto completo da fonteTenenbaum, Aaron M. Data structures using PASCAL. 2a ed. Englewood Cliffs,NJ: Prentice-Hall International, 1986.
Encontre o texto completo da fonteR, Hubbard J. Data structures with Java. Upper Saddle River, N.J: Pearson Prentice Hall, 2004.
Encontre o texto completo da fonteDale, Nell B. C++ plus data structures. 2a ed. Sudbury, Mass: Jones and Bartlett Publishers, 2001.
Encontre o texto completo da fonteSingh, Bhagat. Introduction to data structures. St. Paul: West Pub. Co., 1985.
Encontre o texto completo da fonte1967-, Zachmann Gabriel, ed. Geometric data structures for computer graphics. Wellesley, MA: A K Peters, 2005.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Data structures (Computer science)"
Dawe, M. S., e 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.
Texto completo da fonteSkiena, 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.
Texto completo da fonteSkiena, 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.
Texto completo da fonteGrillmeyer, 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.
Texto completo da fonteLaaksonen, 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.
Texto completo da fonteLaaksonen, 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.
Texto completo da fonteCormode, 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.
Texto completo da fonteRaman, Rajeev, Venkatesh Raman e 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.
Texto completo da fonteCarlsson, Svante, e 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.
Texto completo da fonteNielsen, 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Data structures (Computer science)"
Beckwith, Brandon, e 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.
Texto completo da fonteNarman, Husnu S., Cameron Berry, Alex Canfield, Logan Carpenter, Jeremy Giese, Neil Loftus e 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.
Texto completo da fontePatrascu, 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.
Texto completo da fonteHubbard, 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.
Texto completo da fonteWeiss, 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.
Texto completo da fonteKortsarts, 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.
Texto completo da fonteCoffey, 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.
Texto completo da fonteMcVey, 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.
Texto completo da fonteHaiming Lai, Ming Xu, Jian Xu, Yizhi Ren e 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.
Texto completo da fonteVanDeGrift, 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "Data structures (Computer science)"
Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, julho de 2022. http://dx.doi.org/10.32370/iaj.2710.
Texto completo da fonteFateman, Richard J., e Carl G. Ponder. Speed and Data Structures in Computer Algebra Systems. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1987. http://dx.doi.org/10.21236/ada197131.
Texto completo da fonteNechaev, V., Володимир Миколайович Соловйов e A. Nagibas. Complex economic systems structural organization modelling. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1118.
Texto completo da fonteOleksiuk, Vasyl P., e Olesia R. Oleksiuk. Exploring the potential of augmented reality for teaching school computer science. [б. в.], novembro de 2020. http://dx.doi.org/10.31812/123456789/4404.
Texto completo da fonteWachen, John, Steven McGee, Don Yanek e Valerie Curry. Coaching Teachers of Exploring Computer Science: A Report on Four Years of Implementation. The Learning Partnership, janeiro de 2021. http://dx.doi.org/10.51420/report.2021.1.
Texto completo da fonteWachen, John, Mark Johnson, Steven McGee, Faythe Brannon e Dennis Brylow. Computer Science Teachers as Change Agents for Broadening Participation: Exploring Perceptions of Equity. The Learning Partnership, abril de 2021. http://dx.doi.org/10.51420/conf.2021.2.
Texto completo da fonteGoncharenko, Tatiana, Nataliia Yermakova-Cherchenko e Yelyzaveta Anedchenko. Experience in the Use of Mobile Technologies as a Physics Learning Method. [б. в.], novembro de 2020. http://dx.doi.org/10.31812/123456789/4468.
Texto completo da fonteJohnson, Mark, John Wachen e Steven McGee. Entrepreneurship, Federalism, and Chicago: Setting the Computer Science Agenda at the Local and National Levels. The Learning Partnership, abril de 2020. http://dx.doi.org/10.51420/conf.2020.1.
Texto completo da fonteChamberlain, C. A., e 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.
Texto completo da fonteTucker Blackmon, Angelicque. Formative External Evaluation and Data Analysis Report Year Three: Building Opportunities for STEM Success. Innovative Learning Center, LLC, agosto de 2020. http://dx.doi.org/10.52012/mlfk2041.
Texto completo da fonte