## 3 Related Work
### Data Administration
Data Preparation.Once data acquisition has been completed, the collected raw data must be converted into a machine-processable form via data cleansing, transforming, adding metadata, and harmonizing the schemas. These activities are collectively referred to as data preparation [45]. To carry out data preparation effectively, both data knowledge and domain knowledge are urgently needed [46]. The term 'human in the loop' encompasses approaches that empower experts without IT knowledge to actively participate in the data preparation process and contribute their expertise [47]. Research approaches therefore aim to cluster thematically related data sources and thereby represent the sources as a knowledge network so that users can easily identify further relevant sources [48]. Alternative approaches aim to group sources based on the features of their data, as they may need similar data preparation steps [49] or to suggest which data transforming operations are appropriate for such data [50]. Furthermore, the knowledge of the experts can be persisted in the form of a knowledge base that users can leverage in data preparation [51]. Sampling approaches aim directly at facilitating the work of experts by presenting them with only a representative sample of the complete base data. On this manageable sample, the expert defines cleansing and transforming steps, which are subsequently applied to the entire dataset [52]. Here, efficiency and effectiveness can be significantly increased if the data are initially divided into semantically related blocks, which are then taken into account for sampling [53]. Such defined cleansing and transforming steps can be converted into data preparation rules, which can be applied semi-automatically also to new datasets in the future [54]. These rules describe how to obtain processable data from raw data. For reasons of transparency, however, the backward direction must also be provided in order to be able to disclose later on which base data a result was obtained [55]. Why- and how-provenance can be used for this purpose [56]. In addition to human-in-the-loop approaches, however, there is also the countertrend, namely AI-assisted fully automated data preparation [57]. However, this results in a chicken-and-egg problem, since good and reliable training data are required to train the AI--this training data, however, also requires sound data preparation [58]. Data Management.For the management of the processed data, data warehouses were state-of-the-art technology for a long time. Here, the data from multiple data sources are organized in a unified structure that is optimized for tailorable but predefined analysis purposes [59]. However, due to the IoT, the heterogeneity of data sources as well as the amount of semistructured or outright unstructured data increased drastically. Moreover, as data became an essential asset, there is a need for comprehensive and flexible data analysis. The rigid structure of data warehouses is not designed for either [60]. Although there are approaches to describe the semantics of inherently unstructured data to make them processable in a data warehouse [61], they are always limited to specific types of data. Since a rigid data structure is an inherent property of a data warehouse, such approaches do not solve the fundamental issues when dealing with IoT data. Data lakes are intended to overcome these challenges. The basic idea is that all raw data are stored (almost) untouched and data preparation takes place dynamically depending on the respective use case [62]. Thus, a data lake pursues a schema-on-read philosophy, i.e., only when data are processed, a schema that is appropriate for the data and the intended usage is defined and applied [63]. To reduce the resulting overhead that occurs with every data access and to facilitate data governance in general, a zone architecture for the data lake is highly recommended. Each zone provides data at a certain processing stage [64]. However, data lakes are rather concepts than clearly specified architectures [65].