Machine Learning Based Mobile Big Data Analytics: State-of-the-art Applications, Taxonomy, Challenges and Future Research Directions
DOI:
https://doi.org/10.63746/njtd.v22i4.3385Keywords:
Mobile Big data, Machine learning, State-of-the-art applications, Taxonomy, Challenges, Future research directionsAbstract
Integrating machine learning with Mobile Big Data (MBD) offers unprecedented possibilities for deriving insights from the massive volumes of data produced by mobile devices, sensors, and services. Traditional data analytics methods struggle with scalability. This is due to the increasing volume, velocity, and variety of mobile data. Furthermore, unstructured data types, including text, images, and sensor readings present in mobile data, may introduce more challenges for traditional analytics techniques. Mobile Big Data analytics based on machine learning provide answers to these problems by utilizing sophisticated algorithms that can scale effectively to process enormous volumes of data in real time, handle unstructured data formats, and handle shifting patterns. This study presents a comprehensive review of state-of-the-art machine learning-based MBD analytics. This survey also considers the taxonomy of Mobile Big Data analytics based on various classification parameters, including data sources, data preprocessing and feature engineering, machine learning algorithms, evaluation metrics and methods, and model deployment and real-world applications. This review addresses the challenges faced by practitioners and researchers, sheds light on the state of machine learning-based MBD analytics today, and suggests future research avenues to further the subject. Researchers and practitioners will find this survey report to be a useful resource for analysing and developing more advanced and effective machine learning-based Mobile Big Data analytics.
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