# Process-Oriented Stream Classification Pipeline: A Literature Review
## 3 Data Sources and Benchmarking
### Application Areas and Data Types  
The most relevant difference between data stream classification and batch processing is that data comes in a steady, endless stream of large quantities, which must be processed continuously and immediately. Many application areas require this kind of processing, as observed in Figure 2, where we give an overview of the topics covered in this section. Stream classification applications can be found in almost all areas where data streams are generated, but they differ depending on the respective conditions and problems, such as the underlying data types. Typical stream data types are numerical or categorical data of sensor environments, video or image data, and text data, for example, from social media sources. In the following, we give an overview of the largest application areas found during our literature search. Additionally, an overview of common stream classification application areas is given in the works of [7, 8]. A frequently mentioned field where stream classification algorithms are applied is the IoT sector. IoT applications rely on sensor sets or intelligent devices, which constantly gather data and monitor the surroundings [9]; an exemplary use case is smart vehicles [10]. Intelligent decisions must be made in real time by data stream algorithms based on sensor data input for automated driving maneuvers. In addition to driver support, vehicle data can also be used to assist public transport, e.g., to detect traffic jams or delays [11]. In the field of activity recognition, video data can be analyzed with the help of stream classification algorithms [12, 13, 14]. Typical application scenarios are, e.g., human motion capturing for the automatic recognition of sign language or accident detection in home surveillance applications for older adults, or vehicle detection [2, 15, 16]. In addition to surveillance applications in public and private spaces, stream classification algorithms are also used in the entertainment sector. For instance, human gesture recognition is utilized in video game development [13]. With the rise of social media, a new application area for stream-based algorithms was born. Participatory online platforms such as Facebook or Twitter invite users to publish their content in texts, photos, and videos. Over time, several algorithms have been developed to analyze the development of discussions within these platforms. Tasks and problems here are, for example, the automated detection of hate speech or cyber-bullying [17]. There  
Figure 2: Typical application areas and data types, as well as a categorization of benchmarking datasets and frameworks, stand at the beginning of a stream classification pipeline. are also efforts to detect texts that indicate health problems such as depression or even emergency event detection [18; 19; 20]. Social media researchers are dealing with concise texts compared to other applications on (stream) text data, such as spam email detection. Therefore, an algorithm has less contextual information to learn the true meaning of texts for classifying, e.g., sentiments [21; 22]. Another application area for stream algorithms is the analysis of telescope data. Detecting stellar behavior or other astronomical events requires algorithms that can deal with large amounts of image data [3; 23; 24; 25]. In contrast, ref. [26] uses satellite data to monitor forest development, and ref. [27] uses sensor and satellite data to detect abnormal events (fires, animal movement) in forest areas. Another application of image data is the health industry, in which medical image data is used to detect abnormalities or signs of illness [28]. Recently, images related to COVID-19 patients were also in the focus of research, i.e., facial mask detection and ultrasound images [29; 30]. Stream algorithms are also used in the financial sector to detect financial distress periods or abnormal developments and intrusion detection [31].