Cloud-Centric Data Engineering: AI-Driven Mechanisms for Enhanced Data Quality Assurance
In the era of digital transformation, organizations are increasingly reliant on cloud-centric data engineering frameworks to manage vast amounts of data efficiently. The exponential growth of data, coupled with its critical role in driving business intelligence and AI/ML applications, underscores the necessity of robust data quality assurance (DQA). However, traditional approaches to DQA are often inadequate for addressing the scale, complexity, and dynamic nature of cloud-based data environments. This paper explores the integration of artificial intelligence (AI) mechanisms in cloud-centric data engineering to enhance data quality assurance processes.
Through detailed case studies in healthcare, e-commerce, and finance, the paper highlights practical applications of AI-driven DQA, showcasing their impact on operational efficiency and decision-making. Furthermore, it evaluates key technologies and tools, including cloud-native services like AWS Glue, Google Cloud Data Quality, and Microsoft Azure Data Factory, alongside open-source AI platforms. Challenges such as algorithmic biases, ethical considerations, and cost implications are also addressed, providing a balanced perspective on the adoption of AI for DQA.
Finally, the paper outlines future directions, predicting advancements in autonomous systems, federated learning, and edge computing that will shape the next generation of cloud-centric data engineering. By leveraging AI to enhance data quality assurance, organizations can unlock the full potential of their data assets, driving innovation and maintaining a competitive edge in the evolving digital landscape
Wang, F., Hu, L., Hu, J., Zhou, J., & Zhao, K. (2017). Recent advances in the internet of things: Multiple perspectives. IETE Technical Review, 34(2), 122-132.
Zheng, Z., Zhu, J., & Lyu, M. R. (2013, June). Service-generated big data and big data-as-a-service: an overview. In 2013 IEEE international congress on Big Data (pp. 403-410). IEEE.
Chen, X., Lu, C. D., & Pattabiraman, K. (2014, November). Failure prediction of jobs in compute clouds: A google cluster case study. In 2014 IEEE International Symposium on Software Reliability Engineering Workshops (pp. 341-346). IEEE.
Malhotra, I., Gopinath, S., Janga, K. C., Greenberg, S., Sharma, S. K., & Tarkovsky, R.
(2014). Unpredictable nature of tolvaptan in treatment of hypervolemic hyponatremia:
case review on role of vaptans. Case reports in endocrinology, 2014(1), 807054.
Karakolias, S., Kastanioti, C., Theodorou, M., & Polyzos, N. (2017). Primary care
doctors’ assessment of and preferences on their remuneration: Evidence from Greek
public sector. INQUIRY: The Journal of Health Care Organization, Provision, and
Financing, 54, 0046958017692274.
Singh, V. K., Mishra, A., Gupta, K. K., Misra, R., & Patel, M. L. (2015). Reduction of
microalbuminuria in type-2 diabetes mellitus with angiotensin-converting enzyme
inhibitor alone and with cilnidipine. Indian Journal of Nephrology, 25(6), 334-339.
Karakolias, S. E., & Polyzos, N. M. (2014). The newly established unified healthcare
fund (EOPYY): current situation and proposed structural changes, towards an upgraded
model of primary health care, in Greece. Health, 2014.
Shilpa, Lalitha, Prakash, A., & Rao, S. (2009). BFHI in a tertiary care hospital: Does
being Baby friendly affect lactation success?. The Indian Journal of Pediatrics, 76, 655-
657.
Polyzos, N. (2015). Current and future insight into human resources for health in Greece.
Open Journal of Social Sciences, 3(05), 5.
Gopinath, S., Janga, K. C., Greenberg, S., & Sharma, S. K. (2013). Tolvaptan in the
treatment of acute hyponatremia associated with acute kidney injury. Case reports in
nephrology, 2013(1), 801575.
Gopinath, S., Giambarberi, L., Patil, S., & Chamberlain, R. S. (2016). Characteristics and
survival of patients with eccrine carcinoma: a cohort study. Journal of the American
Academy of Dermatology, 75(1), 215-217.
Shakibaie-M, B. (2013). Comparison of the effectiveness of two different bone substitute
materials for socket preservation after tooth extraction: a controlled clinical study.
International Journal of Periodontics & Restorative Dentistry, 33(2).
Swarnagowri, B. N., & Gopinath, S. (2013). Ambiguity in diagnosing
esthesioneuroblastoma--a case report. Journal of Evolution of Medical and Dental
Sciences, 2(43), 8251-8255.
Gopinath, S., Janga, K. C., Greenberg, S., & Sharma, S. K. (2013). Tolvaptan in the
treatment of acute hyponatremia associated with acute kidney injury. Case reports in
nephrology, 2013(1), 801575.
Swarnagowri, B. N., & Gopinath, S. (2013). Pelvic Actinomycosis Mimicking
Malignancy: A Case Report. tuberculosis, 14, 15.
Gopinath, S., Giambarberi, L., Patil, S., & Chamberlain, R. S. (2016). Characteristics and
survival of patients with eccrine carcinoma: a cohort study. Journal of the American
Academy of Dermatology, 75(1), 215-217.
Swarnagowri, B. N., & Gopinath, S. (2013). Ambiguity in diagnosing
esthesioneuroblastoma--a case report. Journal of Evolution of Medical and Dental
Sciences, 2(43), 8251-8255.
Malhotra, I., Gopinath, S., Janga, K. C., Greenberg, S., Sharma, S. K., & Tarkovsky, R.
(2014). Unpredictable nature of tolvaptan in treatment of hypervolemic hyponatremia:
case review on role of vaptans. Case reports in endocrinology, 2014(1), 807054.
Swarnagowri, B. N., & Gopinath, S. (2013). Pelvic Actinomycosis Mimicking
Malignancy: A Case Report. tuberculosis, 14, 15.
Papakonstantinidis, S., Poulis, A., & Theodoridis, P. (2016). RU# SoLoMo ready?:
Consumers and brands in the digital era. Business Expert Press.
Poulis, A., Panigyrakis, G., & Panos Panopoulos, A. (2013). Antecedents and
consequents of brand managers’ role. Marketing Intelligence & Planning, 31(6), 654-673.
Poulis, A., & Wisker, Z. (2016). Modeling employee-based brand equity (EBBE) and
perceived environmental uncertainty (PEU) on a firm’s performance. Journal of Product
& Brand Management, 25(5), 490-503.
Mulakhudair, A. R., Hanotu, J., & Zimmerman, W. (2017). Exploiting ozonolysis-
microbe synergy for biomass processing: Application in lignocellulosic biomass
pretreatment. Biomass and bioenergy, 105, 147-154.
Abbas, Z., & Hussain, N. (2017). Enterprise Integration in Modern Cloud Ecosystems: Patterns, Strategies, and Tools.
Kommera, A. R. (2015). Future of enterprise integrations and iPaaS (Integration Platform as a Service) adoption. Neuroquantology, 13(1), 176-186.
Gudimetla, S. R. (2015). Beyond the barrier: Advanced strategies for firewall implementation and management. NeuroQuantology, 13(4), 558-565..
Sparks, E. R., Talwalkar, A., Haas, D., Franklin, M. J., Jordan, M. I., & Kraska, T. (2015, August). Automating model search for large scale machine learning. In Proceedings of the Sixth ACM Symposium on Cloud Computing (pp. 368-380).
Silva, B. N., Khan, M., & Han, K. (2018). Internet of things: A comprehensive review of enabling technologies, architecture, and challenges. IETE Technical review, 35(2), 205-220.
Behera, R. K., Reddy, K. H. K., & Sinha Roy, D. (2019). Modeling and assessing reliability of service-oriented internet of things. International Journal of Computers and Applications, 41(3), 195-206.
Kaur, H., & Sood, S. K. (2019). Adaptive neuro fuzzy inference system (ANFIS) based wildfire risk assessment. Journal of Experimental & Theoretical Artificial Intelligence, 31(4), 599-619.
Butler, K., & Merati, N. (2016). Analysis patterns for cloud-centric atmospheric and ocean research. In Cloud Computing in Ocean and Atmospheric Sciences (pp. 15-34). Academic Press.
Srinivas, J., Das, A. K., Kumar, N., & Rodrigues, J. J. (2018). Cloud centric authentication for wearable healthcare monitoring system. IEEE Transactions on Dependable and Secure Computing, 17(5), 942-956.
Verma, P., & Sood, S. K. (2018). Cloud-centric IoT based disease diagnosis healthcare framework. Journal of Parallel and Distributed Computing, 116, 27-38.
Almobaideen, W., Allan, M., & Saadeh, M. (2016). Smart archaeological tourism: Contention, convenience and accessibility in the context of cloud-centric IoT. Mediterranean Archaeology and Archaeometry, 16(1), 227-227.
Hasan, M. M., & Mouftah, H. T. (2017). Cloud-centric collaborative security service placement for advanced metering infrastructures. IEEE Transactions on Smart Grid, 10(2), 1339-1348.
Ali, S., Wang, G., Bhuiyan, M. Z. A., & Jiang, H. (2018, October). Secure data provenance in cloud-centric internet of things via blockchain smart contracts. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 991-998). IEEE.
Mladenow, A., Kryvinska, N., & Strauss, C. (2012). Towards cloud-centric service environments. Journal of Service Science Research, 4, 213-234.
Gupta, R., & Garg, R. (2015, May). Mobile Applications modelling and security handling in Cloud-centric Internet of Things. In 2015 Second International Conference on Advances in Computing and Communication Engineering (pp. 285-290). IEEE.
Zhao, W., Liu, J., Guo, H., & Hara, T. (2018). ETC-IoT: Edge-node-assisted transmitting for the cloud-centric internet of things. IEEE Network, 32(3), 101-107.
Gupta, P. K., Maharaj, B. T., & Malekian, R. (2017). A novel and secure IoT based cloud centric architecture to perform predictive analysis of users activities in sustainable health centres. Multimedia Tools and Applications, 76, 18489-18512.
Pouryazdan, M., Fiandrino, C., Kantarci, B., Kliazovich, D., Soyata, T., & Bouvry, P. (2016, December). Game-theoretic recruitment of sensing service providers for trustworthy cloud-centric Internet-of-Things (IoT) applications. In 2016 IEEE Globecom Workshops (GC Wkshps) (pp. 1-6). IEEE.
Butun, I., Kantarci, B., & Erol-Kantarci, M. (2015, June). Anomaly detection and privacy preservation in cloud-centric internet of things. In 2015 IEEE International Conference on Communication Workshop (ICCW) (pp. 2610-2615). Ieee.
Raj, P., Venkatesh, V., & Amirtharajan, R. (2013). Envisioning the cloud-induced transformations in the software engineering discipline. Software Engineering Frameworks for the Cloud Computing Paradigm, 25-53.
Jin, Y., Wen, Y., Shi, G., Wang, G., & Vasilakos, A. V. (2012, January). CoDaaS: An experimental cloud-centric content delivery platform for user-generated contents. In 2012 International Conference on Computing, Networking and Communications (ICNC) (pp. 934-938). IEEE.
Erder, M., & Pureur, P. (2015). Continuous architecture: sustainable architecture in an agile and cloud-centric world. Morgan Kaufmann.
Butt, S. M. (2014). Cloud centric real time mobile learning system for computer science. GRIN Verlag.
Skourletopoulos, G., Mavromoustakis, C. X., Mastorakis, G., Sahalos, J. N., Batalla, J. M., & Dobre, C. (2017, May). Cost-benefit analysis game for efficient storage allocation in cloud-centric internet of things systems: a game theoretic perspective. In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) (pp. 1149-1154). IEEE.
Kesavulu, M., Helfert, M., & Bezbradica, M. (2016). Towards Refactoring in Cloud-Centric Internet of Things for Smart Cities.
Raveendar, B., & Marikannu, P. (2015). ENHANCING FAST RETRANSMISSION AND FAST RECOVERY IN CLOUD MOBILE MEDIA.
Shams, F. (2017). CLOUD-CENTRIC SOFTWARE ARCHITECTURE FOR INDUSTRIAL PRODUCT-SERVICE SYSTEMS.
Oteafy, S. M., & Hassanein, H. S. (2014, June). Cloud-centric Sensor Networks-Deflating the hype. In 2014 IEEE Symposium on Computers and Communications (ISCC) (pp. 1-5). IEEE.
Copyright (c) 2025 International Journal of Engineering and Computer Science
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.