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Data Engineering for Affective Understanding Systems

Faculty of Information Technology, University of Petra, Amman 11196, Jordan
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Received: 8 March 2019 / Revised: 3 April 2019 / Accepted: 16 April 2019 / Published: 18 April 2019
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Abstract

Affective understanding is an area of affective computing which is concerned with advancing the ability of a computer to understand the affective state of its user. This area continues to receive attention in order to improve the human-computer interactions of automated systems and services. Systems within this area typically deal with big data from different sources, which require the attention of data engineers to collect, process, integrate and store. Although many studies are reported in this area, few look at the issues that should be considered when designing the data pipeline for a new system or study. By reviewing the literature of affective understanding systems one can deduct important issues to consider during this design process. This paper presents a design model that works as a guideline to assist data engineers when designing data pipelines for affective understanding systems, in order to avoid implementation faults that may increase cost and time. We illustrate the feasibility of this model by presenting its utilization to develop a stress detection application for drivers as a case study. This case study shows that failure to consider issues in the model causes major errors during implementation leading to highly expensive solutions and the wasting of resources. Some of these issues are emergent such as performance, thus implementing prototypes is recommended before finalizing the data pipeline design. View Full-Text
Keywords: affective understanding; data engineering; data pipeline; design model; stress detection system affective understanding; data engineering; data pipeline; design model; stress detection system
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

El-Khalili, N.; Alnashashibi, M.; Hadi, W.; Banna, A.A.; Issa, G. Data Engineering for Affective Understanding Systems. Data 2019, 4, 52.

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