Integrating Big Data into the computing curricula

Yasin Silva, Suzanne Dietrich, Jason M. Reed, Lisa M. Tsosie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSIGCSE 2014 - Proceedings of the 45th ACM Technical Symposium on Computer Science Education
PublisherAssociation for Computing Machinery
Pages139-144
Number of pages6
DOIs
StatePublished - 2014
Event45th ACM Technical Symposium on Computer Science Education, SIGCSE 2014 - Atlanta, GA, United States
Duration: Mar 5 2014Mar 8 2014

Other

Other45th ACM Technical Symposium on Computer Science Education, SIGCSE 2014
CountryUnited States
CityAtlanta, GA
Period3/5/143/8/14

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Keywords

  • Big data management systems
  • Databases curricula

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

Cite this

Silva, Y., Dietrich, S., Reed, J. M., & Tsosie, L. M. (2014). Integrating Big Data into the computing curricula. In SIGCSE 2014 - Proceedings of the 45th ACM Technical Symposium on Computer Science Education (pp. 139-144). Association for Computing Machinery. https://doi.org/10.1145/2538862.2538877