# Process-Oriented Stream Classification Pipeline: A Literature Review
## 4 Data Processing
### Data Segmentation  
Since the data stream is possibly unbounded in stream classification, it is often segmented into batches called windows when it comes to the actual processing, e.g., drift detection or updating. An overview of the most often described time window types in our literature base can be observed in Figure 5.  
Several works discuss the different types of time windows. Commonly, sliding, damped, and landmark time windows are distinguished [49; 88]. In [89; 107] damped time windows are denoted _fading time windows;_ however, they refer to the same concepts.  
In sliding time windows, the specific window size is fixed so that the algorithm discards the oldest observation from consideration for each new incoming observation. Thus, the same number of observations is always used to retrain the model. Similarly, the landmark time window keeps a steady number of observations, but the data is portioned into disjoint chunks. Whenever a landmark is reached, all observations previously considered are released. Landmarks can be defined differently, e.g., a threshold for the maximum number of observations or a certain amount of time passed. This approach should not be used when it is known a priori that the data stream contains drifts with long periods of stability in between [5]. In such cases, no window size can capture the distribution shift. An alternative is the damped time window. Unlike the approaches discussed, a function is used to decay older observations over time. In literature, usually exponential functions are used to weight observations and generate the decay [108].  
Additionally, refs. [89; 107; 109] present a tilted time window as a variant of the damped time window. It applies different levels of granularity to the data records according to their recency. Thus, old data points are summarized in larger, less important windows, while new observations are considered in smaller, heavier-weighted windows.  
Similarly, ref. [5] introduce the adapted time window as a variant of the landmark time window. Here, the window size is not fixed but can be adjusted dynamically. In prac  
Figure 5: Different ways to segment the input data. The more concentrated the blue, the more are the observations considered in retraining the model.  
tice, adapted time windows may lead to significant data chunk sizes and computational overhead. For example, the Adaptive Windowing algorithm (ADWIN) flushes the considered observations every time a concept drift is detected. Thus, it automatically selects the appropriate window size. The user specifies a sufficiently long time, within ADWIN automatically generates all possible windows and compares them by inspecting the mean value difference [110]. An optimal cut is found when the mean difference exceeds a predefined tolerance level that the user can set. ADWIN forms the basis for many automated update algorithms through its adaptive windows' capability [36].  
In the works of [88, 111], general advantages and disadvantages of windowing are discussed. Refs. [5, 88, 112] mention the processing of each incoming observation incrementally as it arrives as a possible way to process incoming data. Ref. [111] points out that by using the windows approach instead of incrementally adding new observations, the expiration of the old observations has to be controlled. Setting the correct window size is crucial for drift detection for all these approaches. However, some types of drifts cannot be detected by some window types. Using different input segmentation methods in parallel [111] is reasonable.  
Note here that the denomination of window types are those most commonly used in the literature. However, for different types of stream classification tasks, they can vary. For example, ref. [106] described window types for activity detection with sensors that incorporate the same approaches discussed here but are called differently. E.g., sliding time windows are called time-based windows, while landmark time windows are called sensor-based windows [106].