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Editorial

Special Issue on Innovative Applications of Big Data and Cloud Computing

1
Department of Computer Science, Tunghai University, Taichung City 407224, Taiwan
2
Research Center for Smart Sustainable Circular Economy, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Taichung City 407224, Taiwan
3
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan
4
Division of Computer Science, The University of Aizu, Aizu-Wakamatsu City 965-8580, Japan
5
Department of Computer Science & Engineering, Sant Longowal Institute of Engineering & Technology, (SLIET), Longowal 148106, India
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2022, 12(19), 9648; https://doi.org/10.3390/app12199648
Submission received: 19 September 2022 / Accepted: 21 September 2022 / Published: 26 September 2022
(This article belongs to the Special Issue Innovative Applications of Big Data and Cloud Computing)

1. Introduction

Big Data and Cloud Computing are two major information technologies for processing data to translate data to knowledge. In recent years, Big Data and Cloud Computing have been combined for various applications, i.e., core service design that combines Big Data and Cloud Computing, Big Data visualization in cloud environments, and cloud-based system implementation for processing Big Data. This Special Issue focuses on collecting innovative applications that utilize Big Data and Cloud Computing as their fundamental tools.

2. Innovative Applications of Big Data and Cloud Computing

This Special Issue presents a total of ten papers that include manufacturing proposals, bread-and-butter issues, information technologies, and scheduling approaches. Hsu et al. [1] present an information-extraction system using a computer vision technique to read the panel information in smart factories. Miao et al. [2] constructed a monitoring system for outdoor campuses to automatically detect their air and water quality. Lee et al. [3] discuss the relationship between the health and the intake of different types of nutrients and micronutrients. Kochovski et al. [4] report on QoS guarantees by considering an edge/fog computing approach and service-level agreements for IoT devices in manufacturing processes. Pogiatzis and Samakovitis [5] present a pipeline architecture with event-driven extract, transform, and load processes to increase system performance in processing data. Chung et al. [6] propose a resource-allocation algorithm for heterogeneous resources to reduce resource wastage and improve system utilization. Sánchez-Carballido et al. [7] present an annotation-as-a-service design which provides annotations for autonomous driving videos. Park et al. [8] designed an approach for rapidly migrating the flash-cache in cloud computing to enhance the I/O workload. Jeon et al. [9] present an effective approach which monitors the resources of idle virtual machines and subsequently reassigns the machines to new jobs to increase resource utilization. Akhtar et al. [10] present a classification system which can identify whether a given fetus is large-for-gestational-age or not; this approach can improve the health of newborns.

3. Future Action Recognition

Although the application window for this Special Issue has been closed, more innovative applications applying Big Data and Cloud Computing are continuously being applied to various issues, such as smart manufacturing, autonomous driving, pattern recognition, scheduling, and decision making. This Special Issue marks the beginning of collections presenting current innovative applications using Big Data and Cloud Computing. We hope that more novel ideas will be inspired by the articles in this Special Issue.

Author Contributions

C.-T.Y., C.-K.T., N.Y.Y. and V.K.V. analyze and write all. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Science and Technology Council (NSTC), Taiwan (R.O.C.), under grants number 111-2622-E-029-003, 111-2621-M-029-004, 110-2221-E-029-020-MY3, and MOST 109-2221-E-167 -030 -MY3.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hsu, T.C.; Tsai, Y.H.; Chang, D.M. The Vision-Based Data Reader in IoT System for Smart Factory. Appl. Sci. 2022, 12, 6586. [Google Scholar] [CrossRef]
  2. Miao, H.Y.; Yang, C.T.; Kristiani, E.; Fathoni, H.; Lin, Y.S.; Chen, C.Y. On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN. Appl. Sci. 2022, 12, 5018. [Google Scholar] [CrossRef]
  3. Lee, H.-A.; Huang, T.-T.; Yen, L.-H.; Wu, P.-H.; Chen, K.-W.; Kung, H.-H.; Lui, C.-Y.; Hsu, C.-Y. Precision Nutrient Management Using Artificial Intelligence Based on Digital Data Collection Framework. Appl. Sci. 2022, 12, 4167. [Google Scholar] [CrossRef]
  4. Kochovski, P.; Paščinski, U.; Stankovski, V.; Ciglarič, M. Pareto-Optimised Fog Storage Services with Novel Service-Level Agreement Specification. Appl. Sci. 2022, 12, 3308. [Google Scholar] [CrossRef]
  5. Pogiatzis, A.; Samakovitis, G. An event-driven serverless ETL pipeline on AWS. Appl. Sci. 2020, 11, 191. [Google Scholar] [CrossRef]
  6. Chung, W.C.; Wu, T.L.; Lee, Y.H.; Huang, K.C.; Hsiao, H.C.; Lai, K.C. Minimizing resource waste in heterogeneous resource allocation for data stream processing on clouds. Appl. Sci. 2020, 11, 149. [Google Scholar] [CrossRef]
  7. Sánchez-Carballido, S.; Senderos, O.; Nieto, M.; Otaegui, O. Semi-Automatic Cloud-Native Video Annotation for Autonomous Driving. Appl. Sci. 2020, 10, 4301. [Google Scholar] [CrossRef]
  8. Park, H.; Lee, M.; Hong, C.H. FirepanIF: High Performance Host-Side Flash Cache Warm-Up Method in Cloud Computing. Appl. Sci. 2020, 10, 1014. [Google Scholar] [CrossRef]
  9. Jeon, J.; Park, J.H.; Jeong, Y.S. Resource Utilization Scheme of Idle Virtual Machines for Multiple Large-Scale Jobs Based on OpenStack. Appl. Sci. 2019, 9, 4327. [Google Scholar] [CrossRef]
  10. Akhtar, F.; Li, J.; Pei, Y.; Imran, A.; Rajput, A.; Azeem, M.; Wang, Q. Diagnosis and prediction of large-for-gestational-age fetus using the stacked generalization method. Appl. Sci. 2019, 9, 4317. [Google Scholar] [CrossRef] [Green Version]
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MDPI and ACS Style

Yang, C.-T.; Tsung, C.-K.; Yen, N.Y.; Verma, V.K. Special Issue on Innovative Applications of Big Data and Cloud Computing. Appl. Sci. 2022, 12, 9648. https://doi.org/10.3390/app12199648

AMA Style

Yang C-T, Tsung C-K, Yen NY, Verma VK. Special Issue on Innovative Applications of Big Data and Cloud Computing. Applied Sciences. 2022; 12(19):9648. https://doi.org/10.3390/app12199648

Chicago/Turabian Style

Yang, Chao-Tung, Chen-Kun Tsung, Neil Yuwen Yen, and Vinod Kumar Verma. 2022. "Special Issue on Innovative Applications of Big Data and Cloud Computing" Applied Sciences 12, no. 19: 9648. https://doi.org/10.3390/app12199648

APA Style

Yang, C. -T., Tsung, C. -K., Yen, N. Y., & Verma, V. K. (2022). Special Issue on Innovative Applications of Big Data and Cloud Computing. Applied Sciences, 12(19), 9648. https://doi.org/10.3390/app12199648

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