## 3 Related Work
### Data Administration  
Data administration comprises all data science tasks in the context of data refinement, i.e., all steps necessary to gain knowledge from raw data [29]. We divide them into the core tasks of a data refinement process, namely selection and extraction of raw data (i.e., data acquisition), data cleansing and data transformation (i.e., data preparation), data storage and data curation (i.e., data management), and distribute refined data (i.e., data provisioning) [30]. Research approaches to assist with these tasks are discussed next. Data Acquisition.In the context of this work, data acquisition refers to the process of selecting relevant data from a wide variety of sources and then gathering them in a central data management architecture [31]. This represents the first step in the big data value chain and thus enables data to become an asset in the first place [32]. Due to the prevailing heterogeneity among data sources and schemas in which the raw data are available, a systematic approach is required for data acquisition. The so-called ETL process (ETL stands for extraction, transformation, loading) represents a well-established three-step process in which adapter technologies are used to first gather the selected data from the sources, then sanitize them, and finally store them in a structured form [33]. However, this process assumes that there is a central data management architecture with a uniform data schema and that the data in the sources are at rest, i.e., can be retrieved at any point in time [34]. However, due to the IoT, such a conception is outdated. Here, data have a high variety of features even within a single source. Therefore, a rigid target schema is not practicable, since information would be lost in the merger [35]. Moreover, the data are in motion, i.e., they are sent out as a data stream immediately after they are captured by the source, and they accrue in a large volume and at a high velocity, which requires adjustments to the ETL process [36]. Thus, modern-day data acquisition approaches must offer a combination of near real-time processing for streaming data (e.g., via Kafka [37]) and traditional batch-based processing for data at rest [38]. To store the collected raw data, modern data management systems such as Apache Hive (see [[https://hive.apache.org/](https://hive.apache.org/)]([https://hive.apache.org/](https://hive.apache.org/)); accessed on 6 February 2023) are suitable, as they are not only able to store large volumes of data efficiently, but also cope with heterogeneity within the data [39]. This way, an acquisition infrastructure for big data  
Figure 1: Hierarchical Classification of Research in the Context of Data Handling. can be implemented based on the lambda architecture [40]. In the lambda architecture principle, a batch processing layer and a stream processing layer acquire and preprocess data independently of each other and then make them available via a merging layer [41]. A fundamental problem with this architecture is that two separate implementations of the same preprocessing logic need to be maintained for the batch processing layer and the stream processing layer, respectively. In the kappa architecture, all data are therefore gathered and preprocessed as micro-batches by a single stream processing system and made available in a schemaless mass storage system [42]. However, the composition of the micro-batches results in either latency (if the batches are too big which results in long waiting times until sufficient data are available) or a high overhead (if the batches are too small and therefore a lot of batches have to be processed). So, there are sacrifices to be made with both approaches. Therefore, approaches such as the delta architecture aim to combine these two architectures to achieve real-time processing with the ability to handle bulk data efficiently [43]. Nevertheless, more comprehensive preprocessing operations should be performed detached from data acquisition as part of subsequent data preparation [44].