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Interactive Data Exploration of Distributed Raw Files: A Systematic Mapping Study

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URI: http://hdl.handle.net/10498/21347

DOI: 10.1109/ACCESS.2018.2882244

ISSN: 2169-3536

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Author/s
Álvarez Ayllón, Alejandro; Palomo Duarte, ManuelAuthority UCA; Dodero Beardo, Juan ManuelAuthority UCA
Date
2019
Department
Ingeniería Informática
Source
IEEE Access - 2019 - Volume: 7 p. 10691 - 10717
Abstract
When exploring big amounts of data without a clear target, providing an interactive experience becomes really dif cult, since this tentative inspection usually defeats any early decision on data structures or indexing strategies. This is also true in the physics domain, speci cally in high-energy physics, where the huge volume of data generated by the detectors are normally explored via C++ code using batch processing, which introduces a considerable latency. An interactive tool, when integrated into the existing data management systems, can add a great value to the usability of these platforms. Here, we intend to review the current state-of-the-art of interactive data exploration, aiming at satisfying three requirements: access to raw data les, stored in a distributed environment, and with a reasonably low latency. This paper follows the guidelines for systematic mapping studies, which is well suited for gathering and classifying available studies.We summarize the results after classifying the 242 papers that passed our inclusion criteria. While there are many proposed solutions that tackle the problem in different manners, there is little evidence available about their implementation in practice. Almost all of the solutions found by this paper cover a subset of our requirements, with only one partially satisfying the three. The solutions for data exploration abound. It is an active research area and, considering the continuous growth of data volume and variety, is only to become harder. There is a niche for research on a solution that covers our requirements, and the required building blocks are there.
Subjects
Big data applications; data analysis; data engineering; data exploration; database systems; interactive systems; systematic mapping study
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
This work is under a Creative Commons License Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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