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A workload‑driven approach for view selection in large dimensional datasets

Leandro Ordonez Ante (UGent) , Gregory Van Seghbroeck (UGent) , Tim Wauters (UGent) , Bruno Volckaert (UGent) and Filip De Turck (UGent)
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Abstract
The information explosion the world has witnessed in the last two decades has forced businesses to adopt a data-driven culture for them to be competitive. These data-driven businesses have access to countless sources of information, and face the challenge of making sense of overwhelming amounts of data in a efficient and reliable manner, which implies the execution of read-intensive operations. In the context of this challenge, a framework for the dynamic read-optimization of large dimensional datasets has been designed, and on top of it a workload-driven mechanism for automatic materialized view selection and creation has been developed. This paper presents an extensive description of this mechanism, along with a proof-of-concept implementation of it and its corresponding performance evaluation. Results show that the proposed mechanism is able to derive a limited but comprehensive set of views leading to a drop in query latency ranging from 80% to 99.99% at the expense of 13% of the disk space used by the base dataset. This way, the devised mechanism enables speeding up query execution by building materialized views that match the actual demand of query workloads.
Keywords
Data models, Information models, Data mining and (big) data analysis, Dimensional data modeling, MATERIALIZED VIEW

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MLA
Ordonez Ante, Leandro, et al. “A Workload‑driven Approach for View Selection in Large Dimensional Datasets.” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, vol. 28, 2020, pp. 1161–86, doi:10.1007/s10922-020-09526-z.
APA
Ordonez Ante, L., Van Seghbroeck, G., Wauters, T., Volckaert, B., & De Turck, F. (2020). A workload‑driven approach for view selection in large dimensional datasets. JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, 28, 1161–1186. https://doi.org/10.1007/s10922-020-09526-z
Chicago author-date
Ordonez Ante, Leandro, Gregory Van Seghbroeck, Tim Wauters, Bruno Volckaert, and Filip De Turck. 2020. “A Workload‑driven Approach for View Selection in Large Dimensional Datasets.” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 28: 1161–86. https://doi.org/10.1007/s10922-020-09526-z.
Chicago author-date (all authors)
Ordonez Ante, Leandro, Gregory Van Seghbroeck, Tim Wauters, Bruno Volckaert, and Filip De Turck. 2020. “A Workload‑driven Approach for View Selection in Large Dimensional Datasets.” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 28: 1161–1186. doi:10.1007/s10922-020-09526-z.
Vancouver
1.
Ordonez Ante L, Van Seghbroeck G, Wauters T, Volckaert B, De Turck F. A workload‑driven approach for view selection in large dimensional datasets. JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT. 2020;28:1161–86.
IEEE
[1]
L. Ordonez Ante, G. Van Seghbroeck, T. Wauters, B. Volckaert, and F. De Turck, “A workload‑driven approach for view selection in large dimensional datasets,” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, vol. 28, pp. 1161–1186, 2020.
@article{8659473,
  abstract     = {The information explosion the world has witnessed in the last two decades has forced businesses to adopt a data-driven culture for them to be competitive. These data-driven businesses have access to countless sources of information, and face the challenge of making sense of overwhelming amounts of data in a efficient and reliable manner, which implies the execution of read-intensive operations. In the context of this challenge, a framework for the dynamic read-optimization of large dimensional datasets has been designed, and on top of it a workload-driven mechanism for automatic materialized view selection and creation has been developed. This paper presents an extensive description of this mechanism, along with a proof-of-concept implementation of it and its corresponding performance evaluation. Results show that the proposed mechanism is able to derive a limited but comprehensive set of views leading to a drop in query latency ranging from 80% to 99.99% at the expense of 13% of the disk space used by the base dataset. This way, the devised mechanism enables speeding up query execution by building materialized views that match the actual demand of query workloads.},
  author       = {Ordonez Ante, Leandro and Van Seghbroeck, Gregory and Wauters, Tim and Volckaert, Bruno and De Turck, Filip},
  issn         = {1064-7570},
  journal      = {JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT},
  keywords     = {Data models,Information models,Data mining and (big) data analysis,Dimensional data modeling,MATERIALIZED VIEW},
  language     = {eng},
  pages        = {1161--1186},
  title        = {A workload‑driven approach for view selection in large dimensional datasets},
  url          = {http://dx.doi.org/10.1007/s10922-020-09526-z},
  volume       = {28},
  year         = {2020},
}

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