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Статті в журналах з теми "Data structures (Computer science)":

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.
2

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.
3

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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4

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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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.
6

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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7

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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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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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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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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Дисертації з теми "Data structures (Computer science)":

1

Obiedat, Mohammad. "Incrementally Sorted Lattice Data Structures." Thesis, The George Washington University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3732474.

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Data 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.

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Kabiri, Chimeh Mozhgan. "Data structures for SIMD logic simulation." Thesis, University of Glasgow, 2016. http://theses.gla.ac.uk/7521/.

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Due to the growth of design size and complexity, design verification is an important aspect of the Logic Circuit development process. The purpose of verification is to validate that the design meets the system requirements and specification. This is done by either functional or formal verification. The most popular approach to functional verification is the use of simulation based techniques. Using models to replicate the behaviour of an actual system is called simulation. In this thesis, a software/data structure architecture without explicit locks is proposed to accelerate logic gate circuit simulation. We call thus system ZSIM. The ZSIM software architecture simulator targets low cost SIMD multi-core machines. Its performance is evaluated on the Intel Xeon Phi and 2 other machines (Intel Xeon and AMD Opteron). The aim of these experiments is to: • Verify that the data structure used allows SIMD acceleration, particularly on machines with gather instructions ( section 5.3.1). • Verify that, on sufficiently large circuits, substantial gains could be made from multicore parallelism ( section 5.3.2 ). • Show that a simulator using this approach out-performs an existing commercial simulator on a standard workstation ( section 5.3.3 ). • Show that the performance on a cheap Xeon Phi card is competitive with results reported elsewhere on much more expensive super-computers ( section 5.3.5 ). To evaluate the ZSIM, two types of test circuits were used: 1. Circuits from the IWLS benchmark suit [1] which allow direct comparison with other published studies of parallel simulators.2. Circuits generated by a parametrised circuit synthesizer. The synthesizer used an algorithm that has been shown to generate circuits that are statistically representative of real logic circuits. The synthesizer allowed testing of a range of very large circuits, larger than the ones for which it was possible to obtain open source files. The experimental results show that with SIMD acceleration and multicore, ZSIM gained a peak parallelisation factor of 300 on Intel Xeon Phi and 11 on Intel Xeon. With only SIMD enabled, ZSIM achieved a maximum parallelistion gain of 10 on Intel Xeon Phi and 4 on Intel Xeon. Furthermore, it was shown that this software architecture simulator running on a SIMD machine is much faster than, and can handle much bigger circuits than a widely used commercial simulator (Xilinx) running on a workstation. The performance achieved by ZSIM was also compared with similar pre-existing work on logic simulation targeting GPUs and supercomputers. It was shown that ZSIM simulator running on a Xeon Phi machine gives comparable simulation performance to the IBM Blue Gene supercomputer at very much lower cost. The experimental results have shown that the Xeon Phi is competitive with simulation on GPUs and allows the handling of much larger circuits than have been reported for GPU simulation. When targeting Xeon Phi architecture, the automatic cache management of the Xeon Phi, handles and manages the on-chip local store without any explicit mention of the local store being made in the architecture of the simulator itself. However, targeting GPUs, explicit cache management in program increases the complexity of the software architecture. Furthermore, one of the strongest points of the ZSIM simulator is its portability. Note that the same code was tested on both AMD and Xeon Phi machines. The same architecture that efficiently performs on Xeon Phi, was ported into a 64 core NUMA AMD Opteron. To conclude, the two main achievements are restated as following: The primary achievement of this work was proving that the ZSIM architecture was faster than previously published logic simulators on low cost platforms. The secondary achievement was the development of a synthetic testing suite that went beyond the scale range that was previously publicly available, based on prior work that showed the synthesis technique is valid.
3

Eastep, 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.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
This 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.
4

Butts, Robert O. "Heterogeneous construction of spatial data structures." Thesis, University of Colorado at Denver, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1588178.

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Linear 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.

5

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.

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The topic of this thesis revolves around the organization of geographical information in permanent memory. Our premise is that a recognized and fully documented direct access storage technique called Multidimensional Paging or Multipaging would provide a well balanced storing structure for this category of data. Since multipaging uses a multidimensional perspective on the information to allocate it to pages in secondary storage then spatial data, which is fundamentally multidimensional by nature, would surely offer a suitable profile.
First, 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.)
6

Eid, Ashraf. "Efficient associative data structures for bitemporal databases." Thesis, University of Ottawa (Canada), 2002. http://hdl.handle.net/10393/6226.

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Most applications require storing multiple versions of data and involve a lot of temporal semantics in their schema. This requires maintenance and querying of temporal relations. A Bitemporal DBMS will simplify the development and maintenance of such applications by moving temporal support from the application into the DBMS engine. The success of such Bitemporal DBMSs relies mainly on the availability of high performance indices that handle update and search operations efficiently. A successful associative data structure (index) is the one that can efficiently partition the space of the attributes that are used within the keys. Temporal attributes have unique characteristics and should support now-relative intervals. These intervals grow as time grows and thus we need an index that can handle attributes with variable values. The proposed bitemporal index partitions the bitemporal space into four subspaces according to the end value of the temporal intervals. This results in separating those keys that have variable intervals from those that have fixed interval(s). In this thesis we have used on-the-shelf index that successfully indexes spatial attributes. But instead of representing the two temporal dimensions as a rectangle, we have represented them as 4 dimensional points. This results in better partitioning of each subtree space and in better search performance.
7

Zhu, 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.

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Distributed memory parallel architectures support a memory model where some memory accesses are local, and thus inexpensive, while other memory accesses are remote, and potentially quite expensive. In order to achieve efficiency on such architectures, we need to reduce remote accesses. This is particularly challenging for applications that use dynamic data structures.
In 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.
8

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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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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Pǎtraşcu, Mihai. "Lower bound techniques for data structures." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45866.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes 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.

Книги з теми "Data structures (Computer science)":

1

Decker, Rick. Data structures. Englewood Cliffs, N.J: Prentice Hall, 1989.

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2

Martin, Johannes J. Data types and data structures. Englewood Cliffs, N.J: Prentice-Hall International, 1986.

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3

Keogh, James Edward. Data structures demystified. New York: McGraw-Hill/Osborne, 2004.

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4

C, Walsh Brian, ed. Computer users' data book. Oxford [Oxfordshire]: Blackwell Scientific Publications, 1986.

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5

Langdon, W. B. Genetic programming and data structures: Genetic programming + data structures = automatic programming! Boston: Kluwer Academic Publishers, 1998.

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6

Brass, Peter. Advanced data structures. New York, NY: Cambrige University Press, 2008.

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7

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

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8

Tenenbaum, Aaron M. Data structures usingC. Englewood Cliffs, N.J: Prentice Hall, 1990.

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9

Rosenstein, Milton. Data structures for programmers. New York: Wiley, 1988.

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10

Feldman, Michael B. Data structures with Ada. Reston, Va: Reston Pub. Co., 1985.

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Частини книг з теми "Data structures (Computer science)":

1

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.

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Skiena, 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.

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Skiena, 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.

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4

Grillmeyer, 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.

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Laaksonen, 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.

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Laaksonen, 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.

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Cormode, 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.

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Raman, 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.

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Carlsson, 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.

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Nielsen, 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.

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Тези доповідей конференцій з теми "Data structures (Computer science)":

1

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.

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Narman, 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.

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Patrascu, 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.

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4

Hubbard, 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.

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Weiss, 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.

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6

Kortsarts, 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.

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7

Coffey, 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.

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8

McVey, 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.

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9

Haiming 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.

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10

VanDeGrift, 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.

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Звіти організацій з теми "Data structures (Computer science)":

1

Fateman, 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.

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2

Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, July 2022. http://dx.doi.org/10.32370/iaj.2710.

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Artificial Intelligence (AI) is one of the oldest fields of computer science used in building structures that look like human beings in terms of thinking, learning, solving problems, and decision making (Jovanovic et al., 2021). AI technologies and techniques have been in application in various aspects to aid in solving problems and performing tasks more reliably, efficiently, and effectively than what would happen without their use. These technologies have also been reshaping the health sector's field, particularly digital tools and medical robotics (Dantas & Nogaroli, 2021). The new reality has been feasible since there has been exponential growth in the patient health data collected globally. The different technological approaches are revolutionizing medical sciences into dataintensive sciences (Dantas & Nogaroli, 2021). Notably, with digitizing medical records supported the increasing cloud storage, the health sector created a vast and potentially immeasurable volume of biomedical data necessary for implementing robotics and AI. Despite the notable use of AI in healthcare sectors such as dermatology and radiology, its use in psychological healthcare has neem models. Considering the increased mortality and morbidity levels among patients with psychiatric illnesses and the debilitating shortage of psychological healthcare workers, there is a vital requirement for AI and robotics to help in identifying high-risk persons and providing measures that avert and treat mental disorders (Lee et al., 2021). This discussion is focused on understanding how AI and robotics could be employed in improving mental health in the human community. The continued success of this technology in other healthcare fields demonstrates that it could also be used in redefining mental sicknesses objectively, identifying them at a prodromal phase, personalizing the treatments, and empowering patients in their care programs.
3

Nechaev, 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.

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One of the well-known results of the theory of management is the fact, that multi-stage hierarchical organization of management is unstable. Hence, the ideas expressed in a number of works by Don Tapscott on advantages of network organization of businesses over vertically integrated ones is clear. While studying the basic tendencies of business organization in the conditions of globalization, computerization and internetization of the society and the results of the financial activities of the well-known companies, the authors arrive at the conclusion, that such companies, as IBM, Boeing, Mercedes-Benz and some others companies have not been engaged in their traditional business for a long time. Their partner networks performs this function instead of them. The companies themselves perform the function of system integrators. The Tapscott’s idea finds its confirmation within the framework of a new powerful direction of the development of the modern interdisciplinary science – the theory of the complex networks (CN) [2]. CN-s are multifractal objects, the loss of multifractality being the indicator of the system transition from more complex state into more simple state. We tested the multifractal properties of the data using the wavelet transform modulus maxima approach in order to analyze scaling properties of our company. Comparative analysis of the singularity spectrumf(®), namely, the difference between maximum and minimum values of ® (∆ = ®max ¡ ®min) shows that IBM company is considerably more fractal in comparison with Apple Computer. Really, for it the value of ∆ is equal to 0.3, while for the vertically integrated company Apple it only makes 0.06 – 5 times less. The comparison of other companies shows that this dependence is of general character. Taking into consideration the fact that network organization of business has become dominant in the last 5-10 years, we carried out research for the selected companies in the earliest possible period of time which was determined by the availability of data in the Internet, or by historically later beginning of stock trade of computer companies. A singularity spectrum of the first group of companies turned out to be considerably narrower, or shifted toward the smaller values of ® in the pre-network period. The latter means that dynamic series were antipersistant. That is, these companies‘ management was rigidly controlled while the impact of market mechanisms was minimized. In the second group of companies if even the situation did changed it did not change for the better. In addition, we discuss applications to the construction of portfolios of stock that have a stable ratio of risk to return.
4

Oleksiuk, 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.

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The article analyzes the phenomenon of augmented reality (AR) in education. AR is a new technology that complements the real world with the help of computer data. Such content is tied to specific locations or activities. Over the last few years, AR applications have become available on mobile devices. AR becomes available in the media (news, entertainment, sports). It is starting to enter other areas of life (such as e-commerce, travel, marketing). But education has the biggest impact on AR. Based on the analysis of scientific publications, the authors explored the possibilities of using augmented reality in education. They identified means of augmented reality for teaching computer science at school. Such programs and services allow students to observe the operation of computer systems when changing their parameters. Students can also modify computer hardware for augmented reality objects and visualize algorithms and data processes. The article describes the content of author training for practicing teachers. At this event, some applications for training in AR technology were considered. The possibilities of working with augmented reality objects in computer science training are singled out. It is shown that the use of augmented reality provides an opportunity to increase the realism of research; provides emotional and cognitive experience. This all contributes to engaging students in systematic learning; creates new opportunities for collaborative learning, develops new representations of real objects.
5

Wachen, 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.

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In this technical report, we examine the implementation of a coaching model for teachers of the Exploring Computer Science course in Chicago Public Schools over a period of four academic years (from 2016-2017 to 2019-2020). We first provide a description of the coaching model and how it evolved over time. Next, we present findings from a descriptive analysis of data collected through logs of coaching interactions and surveys of ECS teacher coaches during the 2019-2020 school year. Coaching logs and survey data were also collected during the 2018-2019 school year and, where appropriate, we compare results across years. We then discuss the products that were produced by the coaching team to support the implementation of the model. Finally, we provide an overview of next steps for the coaching team in the 2020-2021 school year and beyond.
6

Wachen, 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.

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In this paper, the authors share findings from a qualitative analysis of computer science teachers’ perspectives about equity within the context of an equity-focused professional development program. Drawing upon a framework emphasizing educator belief systems in perpetuating inequities in computer science education and the importance of equity-focused teacher professional development, we explored how computer science teachers understand the issue of equity in the classroom. We analyzed survey data from a sample of participants in a computer science professional development program, which revealed that teachers have distinct ways of framing their perceptions of equity and also different perspectives about what types of strategies help to create equitable, inclusive classrooms reflective of student identity and voice.
7

Goncharenko, 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.

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Swift changes in society, related to sciences technicians’ development, technologies, by the increase of general volume of information, pull out new requirements for maintenance, structure, and quality of education. It requires teachers to diversify a tool in the direction of the increase in possibilities of the use of mobile technologies and computer systems. Lately in the world, more attention spared to the use of mobile learning, which in obedience to «Recommendations of UNESCO on the questions of a policy in the area of mobile learning» foresees the use of mobile technology, both separate and together with other by informational computer technologies. [1]. Mobile learning allows using the open informational systems, global educational networks, unique digital resources which belong to different educational establishments and co-operate with each other. The use of existent educational resources and creation of own, based on the academic resources from informative space, allows to promote the interest of students to the study of physics, to take into account the individual features, and also features of region and framework of society of the country. During the last years in Ukraine competency-based approach to the organization of studies certainly one of basic. The new Education Act addresses the key competencies that every modern person needs for a successful life, including mathematical competence; competence in natural sciences, engineering, and technology; innovation; information and communication competence [2]. This further emphasizes the importance of providing students with quality physical education and the problems associated with it. Using mobile technology in professional teaching work, the teacher has the opportunity to implement the basic principles of the competence approach in teaching physics. An analysis of the data provided in the official reports of the Ukrainian Center for Educational Quality Assessment showed that the number of students making an external independent assessment in physics and choosing a future profession related to physics has decreased significantly. This is due to the loss of students' interest in physics and the complexity of the content of the subject, as well as the increase in the amount of information that students need to absorb. In this article, we explore the possibilities of mobile technology as a means of teaching physics students and give our own experience of using mobile technology in the process of teaching physics (for example, the optics section in primary school).
8

Johnson, 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.

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From 2012-13 to 2018-19, the number of Chicago Public Schools (CPS) high school students taking an introductory computer science course rose from three thousand per year to twelve thousand per year. Our analysis examines the policy entrepreneurship that helped drive the rapid expansion of computer science education in CPS, within the broader context of the development of computer science at the national level. We describe how actions at the national level (e.g., federal policy action and advocacy work by national organizations) created opportunities in Chicago and, likewise, how actions at the local level (e.g., district policy action and advocacy by local educators and stakeholders) influenced agenda setting at the national level. Data from interviews with prominent computer science advocates are used to document and explain the multidirectional (vertical and horizontal) flow of advocacy efforts and how these efforts influenced policy decisions in the area of computer science. These interviews with subsystem actors––which include district leaders, National Science Foundation program officers, academic researchers, and leaders from advocacy organizations––provide an insider’s perspective on the unfolding of events and highlight how advocates from various organizations worked to achieve their policy objectives.
9

Chamberlain, 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.

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The Geodetic Survey Division of the Canada Centre for Surveying is replacing the National Geodetic Data Base (NGDB) with the National Geodetic Information System (NGIS). For the NGIS to be successful, it was recognized that a sound, well engineered data mode was essential. The methodology chosen to design the data mode! was Nijssen's Information Analysis Methodology (NIAM), a binary modeling technique that is supported by a Computer Aided Software Engineering (CASE) tool, PC-IAST. An NGIS prototype has also been developed using Digital Equipment of Canada's Relational Database (Rdb) management system and COGNOS Corporations POWERHOUSE 4th generation language. This paper addresses the need for, and the advantages of using a strong engineering approach to data modeling and describes the use of the NIAM methodology in NGIS development. The paper identifies the relationship between the data mode!, data structures, the design and development of a database and the use of automated tools for systems development. In conclusion, critical success factors for the continuation of the N.G.I.S. developments are identified and the benefits that will accrue are enumerated.
10

Tucker 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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