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    An Overview of Big Data Concepts, Methods, and Analytics: Challenges, Issues, and Opportunities
    (IEEE, 2023) Mahmoudian, Mahshad; Zanjani, S. Mohammadali; Shahinzadeh, Hossein; Kabalci, Yasin; Kabalci, Ersan; Ebrahimi, Farshad
    In recent years, data generation is increasing on a large scale and fast pace, and the development of Internet applications, mobile applications, and network-connected sensors has also increased widely. These applications and extensive internet connections continuously produce a large volume of data, with a wide diversity and different structures, which is called big data. At the same time, technologies related to big data are also developing. The rapid growth of cloud computing and the Internet of Things (IoT) is accelerating the dramatic growth of data generation. Sensors around the world are collecting and transmitting data that will be stored and processed in the cloud, and the era of big data is coming. In this article, first, an overview of big data and the definitions of its features are explained, and then the applications of big data in different fields are examined and the challenges facing it are discussed. Finally, technologies related to big data in the field of big data analysis, data storage technologies, and visualization tools are proposed and cloud computing, IoT, and data center are examined as new technologies that are closely related to big data. The main goal of this article is to provide a comprehensive overview of big data and examine and explain various aspects of its applications and implementation.
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    The Intelligent Mechanism for Data Collection and Data Mining in the Vehicular Ad-Hoc Networks (VANETs) Based on Big-Data-Driven
    (IEEE, 2023) Mahmoudian, Mahshad; Zanjani, S. Mohammadali; Shahinzadeh, Hossein; Kabalci, Yasin; Kabalci, Ersan; Ebrahimi, Farshad
    Big data technology has attracted the main attention of researchers in almost all sciences. The Vehicular Ad-Hoc Network (VANET) enables information exchange between vehicles, other devices, and public networks, playing a key role in road safety and intelligent transportation systems. With the proliferation of connected vehicles and the development of novel mobile apps and technologies, VANETs will generate vast quantities of data that need to be transmitted quickly and reliably. Furthermore, analyzing a wide range of data types can enhance VANET's performance. By utilizing big data technologies, the Ad-Hoc Vehicular Network can extract valuable insights from a large amount of operational data, thus improving traffic management processes, including planning, engineering, and operations. VANETs have access to big data during real-time operations. This paper presents VANET features as big data features in the literature, followed by a discussion of methods for utilizing big data to study VANET features. Combining automotive ad networks and big data facilitates the easy management of a large number of driving factors, as the data mining process in big data enables quick decision-making based on statistical analysis or graphical representations of data.