## 2 Definitions and Background
### Data Quality  
Data quality is described in a variety of dimensions [11], e.g., accuracy, completeness or consistency. Each of these dimensions require a different set of tools, metrics and visualizations to assess the data quality [12]. However, a clear specification of a set of distinct quality dimensions and indicators does not exist [13]. Not only the assessment but also the awareness of the challenges is prevented without the specification of data quality dimensions and attributes [14]. A deficiency in data quality is the mismatch between the real system and its data-driven representation [11]. A mismatch is not only induced by incorrect data of the representation but also by incomplete data. Much research has already classified the different data quality dimensions to construct a framework for data quality assessment [15]. However, defining the dimensions in detail and identifying appropriate metrics to assess data quality still is challenging [16] due to the variety of data and use cases. Several researchers proposed a process for data quality assessment. On the highest level of abstraction, the process is divided into the two phases: Assessment and Improvement [17], while the first phase is divided into the three steps Define, Measure and Analyze [18] (cf. Figure 2). In the first step, the relevant quality dimensions and attributes/elements as well as indicators/metrics are defined in a so-called quality model [19]. Thereby, each dimension can be described by a variety of attributes and the attributes again can be represented by a variety of indicators. Particular attention has to be given to the fact, that the dimensions interact [20]. Interdependencies have to be identified and taken into account in the data quality assessment process. Furthermore, the targets are defined in order to provide a basis for comparison. This step is use case and domain specific and requires respective expert knowledge. In Measure, the defined quality model is applied on the data. In order to do so, the data is collected from the different data sources, is cleaned and transformed and finally stored into a database. Each data object of the database can be investigated separately or in combination with several other data objects [21]. To measure the structure and characteristics of the data, several methods/techniques including statistical analysis or machine learning algorithms are available. Correlation analysis for example can be used to model the dependencies between sensor signals and thus to identify data value inconsistencies. A report summarizes the results of this step, giving an overview of the data and their structure based on the indicators. In Analyze, the results are interpreted in order to assess the quality of the data and identify discrepancies to the targets. For this, visualizations should be used to support the decision-making by the experts. In order to plan appropriate actions to increase the data quality, the causes of errors have to be found. Based on this, the improvement of data quality is achieved. Appropriate actions have to be taken, which follow two different strategies. On the one hand, the collected data itself can be corrected or enriched (data-driven strategy). Missing data for example can be replaced. Different approaches are proposed, e.g., a framework for a semantic reasoning engine [22], to correct and enrich data automatically. On the other hand, the mechanisms of the data processing leading to deficiencies in data quality can be eliminated (process-driven strategy). Using different sensors or changing the communication protocol could improve the data gathering process and therefore increase data value consistency. In this paper, the step Define is focused. The related work in Section 3 discuss the dimension completeness and the attributes and metrics which are introduced by the literature. It is shown that the attribute of data set completeness is not yet covered and that a metric is missing.