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Integrating big data into the computing curricula

Published: 05 March 2014 Publication History

Abstract

An important recent technological development in computer science is the availability of highly distributed and scalable systems to process Big Data, i.e., datasets with high volume, velocity and variety. Given the extensive and effective use of systems incorporating Big Data in many application scenarios, these systems have become a key component in the broad landscape of database systems. This fact creates the need to integrate the study of Big Data Management Systems as part of the computing curricula. This paper presents well-structured guidelines to perform this integration by describing the important types of Big Data systems and demonstrating how each type of system can be integrated into the curriculum. A key contribution of this paper is the description of an array of course resources, e.g., virtual machines, sample projects, and in-class exercises, and how these resources support the learning outcomes and enable a hands-on experience with Big Data technologies.

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cover image ACM Conferences
SIGCSE '14: Proceedings of the 45th ACM technical symposium on Computer science education
March 2014
800 pages
ISBN:9781450326056
DOI:10.1145/2538862
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 05 March 2014

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  1. big data management systems
  2. databases curricula

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SIGCSE '14 Paper Acceptance Rate 108 of 274 submissions, 39%;
Overall Acceptance Rate 1,787 of 5,146 submissions, 35%

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  • (2023)Methodology of Modeling and Analyzing Disciplinary EvolutionJournal of Engineering Studies10.3724/SP.J.1224.2018.0016810:02(168-179)Online publication date: 8-Dec-2023
  • (2022)Big Data: A Transition from Conventional DatabasesInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-5682(318-326)Online publication date: 7-Jul-2022
  • (2022)Storing and structuring big data in histological research (vertebrates) using a relational database in SQLRegulatory Mechanisms in Biosystems10.15421/02222613:3(207-212)Online publication date: 18-Jul-2022
  • (2021)The Algorithm to Check the Correct of Practicing SQL DML2021 10th International Conference on Educational and Information Technology (ICEIT)10.1109/ICEIT51700.2021.9375594(253-257)Online publication date: 18-Jan-2021
  • (2020)Introducing Big Data to Undergraduate Students: A Novel Approach in a Distance UniversityIEEE Revista Iberoamericana de Tecnologias del Aprendizaje10.1109/RITA.2020.303322215:4(291-298)Online publication date: Nov-2020
  • (2019)Research on Teaching Evaluation Model Based on Weighted Naive Bayes2019 10th International Conference on Information Technology in Medicine and Education (ITME)10.1109/ITME.2019.00112(476-480)Online publication date: Aug-2019
  • (2019)Challenges of Big Data Technologies in Education Process in Business Information Systems2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA)10.1109/ICETA48886.2019.9040083(693-698)Online publication date: Nov-2019
  • (2018)A conceptual framework for designing a big data courseJournal of Computing Sciences in Colleges10.5555/3204979.320501733:5(192-198)Online publication date: 1-May-2018
  • (2018)A MODEL FOR AUTOMATED MATCHING BETWEEN JOB MARKET DEMAND AND UNIVERSITY CURRICULA OFFERSEEU Review10.1515/seeur-2017-002412:2(188-217)Online publication date: 11-May-2018
  • (2018)A Comprehensive Course on Big Data for Undergraduate Students2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2018.00067(353-360)Online publication date: May-2018
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