# Process-Oriented Stream Classification Pipeline: A Literature Review
## 1 Introduction  
The analysis of stream data has become increasingly important in recent years. For instance, applications based on real-time sensor data or images have become indispensable in the Internet of Things (IoT). The characteristic of stream data is that it comes in large quantities and at a high speed. Moreover, this kind of data often is of relatively large dimensionality, i.e., it contains numerous features and observations [1]. Manual data processing is infeasible; thus, a broad field of data stream mining methods has emerged over the years. As in the static environment, the core tasks include unsupervised and supervised learning. In this work, we address research questions and challenges of supervised learning methods that are exposed to data streams--concretely, we focus on stream classification. Data stream classification finds several application areas. These include, for example, the detection of specific gestures or elements in video data, the interpretation of a device's sensor data, or the analysis of telescope data [2, 3]. Regardless of the specific application, the process of data stream classification consists of several steps and modification options. Next to typical classification steps, such as data preprocessing and training and testing the classifier model, other essential tasks open up in the streaming scenario. While the initial training can occur in an offline mode, the validation needs to be performed during the whole lifetime of an online process. Changes in the data, such as the shift of feature distributions, or the emergence of new classes, can weaken the algorithm's quality over time. If a change in the data is detected and the performance of classification results decreases substantially, the model needs to be updated immediately. For good model quality, update procedures must guarantee continuous adaptation to the data stream without neglecting the actual classification task. During the entire usage cycle, fast processing of new incoming data points must be ensured. Due to these numerous challenges, a broad field of work on stream classification has emerged over the years. Methodologically, the field is vast. On the one hand, there is work on developing and further refining (classification) algorithms. On the other hand, researchers examine closely related topics such as drift detection and adaption techniques. Diverse research fields produced problem-tailored solution approaches as well as terminology and taxonomies. Thus, the field also grew in summary papers and comparative work. Benchmark studies, for example, deal with the comparison of different algorithms concerning efficiency and speed. Survey papers structure the field of data stream mining and bring the notations and wordings of the different disciplines to a common understanding. Due to the size of the research field, it is striking that the works often deal only with a partial aspect of the stream classification process. For example, they focus on change detection or algorithm structure [4; 5]. Newcomers or interested researchers thus have to navigate through various papers to gain a holistic overview. Our main contribution is to provide a road map in stream classification literature. Thus, for better comprehensibility, we structure the research field from a process-centered point of view along a stream classification pipeline (see Figure 1). For each process step, we refer to relevant, comparative, and summary works and future research directions. Our underlying literature base spans approximately 320 papers from the stream classification discipline between 1984 and 2021. We specifically searched for summary papers and recent publications in the field. The remainder of this work is structured as follows: In Section 2, we provide an overview of the underlying problem description, the literature base as well as the stream classification pipeline, which is the underlying structure of this work.