This work is licensed under the Creative Commons Attribution 4.0 International License.
Cao, L. (2017). Data science: a comprehensive overview. ACM Computing Surveys (CSUR), 50(3), 1-42.Search in Google Scholar
Thudumu, S., Branch, P., Jin, J., & Singh, J. (2020). A comprehensive survey of anomaly detection techniques for high dimensional big data. Journal of Big Data, 7, 1-30.Search in Google Scholar
Galeano, P., & Peña, D. (2019). Data science, big data and statistics. Test, 28(2), 289-329.Search in Google Scholar
Zenati, H., Romain, M., Foo, C. S., Lecouat, B., & Chandrasekhar, V. (2018, November). Adversarially learned anomaly detection. In 2018 IEEE International conference on data mining (ICDM) (pp. 727-736). IEEE.Search in Google Scholar
Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020). Big-data science in porous materials: materials genomics and machine learning. Chemical reviews, 120(16), 8066-8129.Search in Google Scholar
Hamada, T., Keum, N., Nishihara, R., & Ogino, S. (2017). Molecular pathological epidemiology: new developing frontiers of big data science to study etiologies and pathogenesis. Journal of gastroenterology, 52, 265-275.Search in Google Scholar
Rousseeuw, P. J., & Hubert, M. (2018). Anomaly detection by robust statistics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(2), e1236.Search in Google Scholar
Leung, C. K., Chen, Y., Shang, S., & Deng, D. (2020, December). Big data science on COVID-19 data. In 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE) (pp. 14-21). IEEE.Search in Google Scholar
Pang, G., Cao, L., Chen, L., & Liu, H. (2018, July). Learning representations of ultrahigh-dimensional data for random distance-based outlier detection. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2041-2050).Search in Google Scholar
Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (Eds.). (2020). Big data and social science: Data science methods and tools for research and practice. CRC Press.Search in Google Scholar
Blokhin, E., & Villars, P. (2020). The PAULING FILE project and materials platform for data science: From big data toward materials genome. Handbook of materials modeling: methods: theory and modeling, 1837-1861.Search in Google Scholar
Stephenson, D. (2018). Big Data Demystified: How to use big data, data science and AI to make better business decisions and gain competitive advantage. Pearson UK.Search in Google Scholar
Lu, S., Wei, X., Li, Y., & Wang, L. (2018, August). Detecting anomaly in big data system logs using convolutional neural network. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/ CyberSciTech) (pp. 151-158). IEEE.Search in Google Scholar
Madduri, R., Chard, K., D’Arcy, M., Jung, S. C., Rodriguez, A., Sulakhe, D., ... & Foster, I. (2019). Reproducible big data science: A case study in continuous FAIRness. PloS one, 14(4), e0213013.Search in Google Scholar
Lee, J. H., Kang, J., Shim, W., Chung, H. S., & Sung, T. E. (2020). Pattern detection model using a deep learning algorithm for power data analysis in abnormal conditions. Electronics, 9(7), 1140.Search in Google Scholar
Fernandes, E., Moro, S., & Cortez, P. (2023). Data science, machine learning and big data in digital journalism: A survey of state-of-the-art, challenges and opportunities. Expert Systems with Applications, 221, 119795.Search in Google Scholar
Kotu, V., & Deshpande, B. (2018). Data science: concepts and practice. Morgan Kaufmann.Search in Google Scholar
Martinez, I., Viles, E., & Olaizola, I. G. (2021). Data science methodologies: Current challenges and future approaches. Big Data Research, 24, 100183.Search in Google Scholar
Marir, N., Wang, H., Feng, G., Li, B., & Jia, M. (2018). Distributed abnormal behavior detection approach based on deep belief network and ensemble SVM using spark. IEEE Access, 6, 59657-59671.Search in Google Scholar
Santesteban, C., & Longpre, S. (2020). How big data confers market power to big tech: Leveraging the perspective of data science. The Antitrust Bulletin, 65(3), 459-485.Search in Google Scholar
Kieu, T., Yang, B., & Jensen, C. S. (2018, June). Outlier detection for multidimensional time series using deep neural networks. In 2018 19th IEEE international conference on mobile data management (MDM) (pp. 125-134). IEEE.Search in Google Scholar
Halwani, M. A., Amirkiaee, S. Y., Evangelopoulos, N., & Prybutok, V. (2022). Job qualifications study for data science and big data professions. Information Technology & People, 35(2), 510-525.Search in Google Scholar
Ahmad, S., Lavin, A., Purdy, S., & Agha, Z. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, 134-147.Search in Google Scholar
Schembera, B., & Duran, J. M. (2020). Dark data as the new challenge for big data science and the introduction of the scientific data officer. Philosophy & Technology, 33, 93-115.Search in Google Scholar
Li, W., Xiang, D., Tsung, F., & Pu, X. (2020). A diagnostic procedure for high-dimensional data streams via missed discovery rate control. Technometrics, 62(1), 84-100.Search in Google Scholar
Uygun, Y., Oguz, R. F., Olmezogullari, E., & Aktas, M. S. (2020, December). On the large-scale graph data processing for user interface testing in big data science projects. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 2049-2056). IEEE.Search in Google Scholar
Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., & Chen, H. (2018, February). Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International conference on learning representations.Search in Google Scholar
Syed, L., Jabeen, S., Manimala, S., & Elsayed, H. A. (2019). Data science algorithms and techniques for smart healthcare using IoT and big data analytics. Smart techniques for a smarter planet: towards smarter algorithms, 211-241.Search in Google Scholar
Zhou, P., Hu, X., Li, P., & Wu, X. (2017). Online feature selection for high-dimensional class-imbalanced data. Knowledge-Based Systems, 136, 187-199.Search in Google Scholar
Leung, C. K. (2021). Data science for big data applications and services: data lake management, data analytics and visualization. In Big Data Analyses, Services, and Smart Data 6 (pp. 28-44). Springer Singapore.Search in Google Scholar
Song, H., Jiang, Z., Men, A., & Yang, B. (2017). A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data. Computational intelligence and neuroscience, 2017(1), 8501683.Search in Google Scholar
Muniswamaiah, M., Agerwala, T., & Tappert, C. C. (2019, December). Federated query processing for big data in data science. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 6145-6147). IEEE.Search in Google Scholar
Nachman, B., & Shih, D. (2020). Anomaly detection with density estimation. Physical Review D, 101(7), 075042.Search in Google Scholar
Carlos, R. C., Kahn, C. E., & Halabi, S. (2018). Data science: big data, machine learning, and artificial intelligence. Journal of the American College of Radiology, 15(3), 497-498.Search in Google Scholar
Buck Lena,Schmidt Tobias,Feist Maren,Schwarzfischer Philipp,Kube Dieter,Oefner Peter J... & Spang Rainer. (2023). Anomaly detection in mixed high dimensional molecular data. Bioinformatics (Oxford, England)(8).Search in Google Scholar
Zhang Xin,Wei Pingping & Wang Qingling. (2023). A hybrid anomaly detection method for high dimensional data. PeerJ. Computer sciencee1199-e1199.Search in Google Scholar
Amgad Muneer,12,Shakirah Mohd Taib,12,Suliman Mohamed Fati,Abdullateef O. Balogun. & 12. (2022). A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data. Computers, Materials & Continua(3),5363-5381.Search in Google Scholar