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Enhancing the Interactive Visualisation of a Data Preparation Tool from in-Memory Fitting to Big Data Sets

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Business Information Systems Workshops (BIS 2020)

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Abstract

In order to derive reliable insights or make evidence-based decisions, the starting point is to assess and meet a minimum quality of data, either by those that publish the data (preferably) or alternatively by those that prepare data for analysis and develop specific analytics. Much of the (open) data shared by governments and different institutions, or crowdsourced, is in tabular format, and the amount and size of it is increasing rapidly. This paper presents the challenges faced and the solutions adopted while evolving the web-based graphical user interface (GUI) of a tabular data preparation tool from in-memory fitting to Big Data sets. Traditional standalone processing and rendering solutions are no longer usable in a Big Data context. We report on the approach adopted to asynchronously pre-compute the visualisations required for the tool, in addition to the applied visualisation aggregation strategies. The implementation of this approach has allowed us to overcome web-browsers’ client-side data handling limitations and to avoid information overload when using granular information charts from our existing in-memory data preparation tool with Big Data sets. The developed solution provides the user with an acceptable GUI interaction time.

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Acknowledgments

This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 727721 (MIDAS).

This work was supported by the Gipuzkoan Science, Technology and Innovation Network Programme funding of the HIDRA project.

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Correspondence to Gorka Epelde .

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Epelde, G., Álvarez, R., Beristain, A., Arrúe, M., Arangoa, I., Rankin, D. (2020). Enhancing the Interactive Visualisation of a Data Preparation Tool from in-Memory Fitting to Big Data Sets. In: Abramowicz, W., Klein, G. (eds) Business Information Systems Workshops. BIS 2020. Lecture Notes in Business Information Processing, vol 394. Springer, Cham. https://doi.org/10.1007/978-3-030-61146-0_22

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  • DOI: https://doi.org/10.1007/978-3-030-61146-0_22

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