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Analysis of the Join Performance in Vertically Distributed Cloud Databases

Analysis of the Join Performance in Vertically Distributed Cloud Databases

Jens Kohler, Kiril Simov, Thomas Specht
Copyright: © 2015 |Volume: 6 |Issue: 2 |Pages: 23
ISSN: 1947-9220|EISSN: 1947-9239|EISBN13: 9781466678484|DOI: 10.4018/IJARAS.2015070104
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MLA

Kohler, Jens, et al. "Analysis of the Join Performance in Vertically Distributed Cloud Databases." IJARAS vol.6, no.2 2015: pp.65-87. http://doi.org/10.4018/IJARAS.2015070104

APA

Kohler, J., Simov, K., & Specht, T. (2015). Analysis of the Join Performance in Vertically Distributed Cloud Databases. International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), 6(2), 65-87. http://doi.org/10.4018/IJARAS.2015070104

Chicago

Kohler, Jens, Kiril Simov, and Thomas Specht. "Analysis of the Join Performance in Vertically Distributed Cloud Databases," International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS) 6, no.2: 65-87. http://doi.org/10.4018/IJARAS.2015070104

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Abstract

Cloud Computing becomes interesting for enterprises across all branches. Renting computing capabilities from external providers avoids initial investments, as only those resources have to be paid that were used eventually. Especially in the context of “Big Data” this pay-as-you-go accounting model is particularly important. The dynamically scalable resources from the Cloud enable enterprises to store or analyze these huge amounts of unstructured data without using their own hardware infrastructure. However, Cloud Computing is currently facing severe data security and protection issues. These challenges require new ways to store and analyze data, especially when huge data volumes with sensitive data are stored at external locations. The presented approach separates data on database table level into independent chunks and distributes them across several clouds. Hence, this work is a contribution to a more secure and resilient cloud architecture as multiple public and private cloud providers can be used independently to store data without losing data security and privacy constraints.

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