## 4 Quality Analysis of References Data in Wikipedia and CRIS  
In organizational database systems, there are usually very many data sources available. As an example, we will consider the reference data in Wikipedia as a data source. From the importance of data quality in organizational database systems, arises the question of how the quality of the reference data can be analyzed before it integrates into the information system and leads to a good decision. For that, it is necessary to examine the notion of data quality and introduce methods using the DataCleaner ([[https://datacleaner.org/](https://datacleaner.org/)]([https://datacleaner.org/](https://datacleaner.org/))) to analyze the data quality of reference data in Wikipedia. We understand the concept of data quality both from the point of view of the provider of a data source and from the point of view of the user of the data source. The definition of data quality states that reference data in Wikipedia should be suitable for the purpose for which it was collected and generated. Data quality is multi-dimensional and context-dependent [36]. It can not be defined by a single criterion, but from four different quality criteria (such as correctness, completeness, consistency and timeliness) together [3]. Only if these are considered together can the quality of the reference data be meaningfully described. According to [27] the quality of data must often be defined as the suitability of the data to be used for certain required usage goals, which must be error-free, complete, correct, up-to-date and inconsistent so that users can get better results. High data quality can simplify integration on the one hand. On the other hand, quality issues become apparent in the course of data integration when comparing multiple data sources [37]. Data quality problems (such as duplicates, incomplete information, incorrect information, null values, etc.) during integration can be controlled by the process of data analysis and this is referred to as data profiling [5]. Data profiling is responsible for collecting as much information as possible about the data, making it easier to identify potential sources of error. In addition, data profiling analyzes the attributes at the instance level and captures as many metadata as possible. the attribute name, data type, value ranges, unique key, patterns, and domains [5]. Based on our practical example of reference data in Wikipedia with a subset of randomly selected 12,334 records, we performed data profiling using the DataCleaner tool to analyze and improve the quality problems of the reference data. With a tool like DataCleaner improves the data quality of existing data in a sustainable way and reduces the duplicates and redundancies within individual databases but also across an entire database. Furthermore, DataCleaner reduces the effort and cost of editing the data. The process of DataCleaner consists of three steps:  
1. check data and identify errors
2. validate data
3. correct mistakes  
Our example includes three columns \(citation\_id\), \(parameter\_name\) and \(parameter\_value\) and for that they have analyzed their data structure and data contents and used this analyzer as follows (see Figure 4):  
* Completeness analyzer: Reference data can be checked with completeness analyzer, if all required fields are completely filled out. * Unique Key: Unique key analysis is the finding of null values or duplicates of reference data. * Character set distribution: This analysis checks and maps the text characterization of reference data to the corresponding affinity, e.g., Arabic, Latin, etc. * Pattner finder: Possible patterns of reference data can be detected and resolved using pattern analysis and this could e.g., date format, e-mail addresses, etc. [5]. * Value Distribution: The value distribution can be used to identify all the values of a specific column and to examine which rows belong to specific values. The results of our quality analysis for the reference data in Wikipedia with this analyzer are clearly shown in Figures 5 and 6.