# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 3 Data Quality Taxonomy  
In our investigation of DQ-related research, we identified two issues among the existing DQ categories. The first issue is that the same specific data quality problem is assigned two or even several different names. The second issue is that the same specific data quality problem is classified into different categories. We aim to address these two minor issues within our proposed strategies. Based on 26,160 papers obtained from five databases, we performed a comprehensive statistical analysis of the types of DQ problems found in the literature and classified them into six categories: redundant data, missing data, noisy data, erroneous data, conflicting data, and sparse data. These six categories encompass a range of DQ problems that may be present in a dataset and serve as crucial guides for subsequent data quality improvements for downstream applications. Table 1 provides specific definitions for each category of data quality issues, along with the types of DQ problems identified in the literature and their corresponding references. This classification offers a framework for a thorough and detailed understanding of data quality issues, thereby enabling us to address them more effectively. All the data quality issues mentioned in the cited papers and their studies can be found in Table 1, and all of them can be mapped to the categories we have defined. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Dirty Data Category** & **Definition** & **Statistics on Dirty Data Types in Literature** & **Literature** \\ \hline
1 & Redundant data & Data in which the same content appears repeatedly, or there are similar duplicate records. & Duplicate data; duplicate records; duplicate values; duplicate data instances; duplicate data attributes; similar duplicate records & [1, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20] \\
2 & Missing data & Missing data in database tables; incomplete data collected. & Incomplete data; missing data; missing values; null values & [1, 7, 12, 14, 15, 21, 22, 23, 24, 25, 26, 27, 28] \\
3 & Noisy data & Attribute domain error; refers to data with large deviation. & Incorrect data; anomalous data; image anomalies; inaccurate data; illogical data; invalid data (data taking default values); irrelevant data; noisy data; outliers & [9, 10, 11, 15, 23] \\
4 & Erroneous data & Format-related errors, including misspelled attribute values. & Incorrect data; incorrect attributes; incorrect input; spelling errors; incorrect words; incorrect units; incorrect data format & [1, 7, 8, 17, 29] \\
5 & Conflicting data & Inconsistent field identification in stored data fields, syntactically or semantically heterogeneous data. & Data inconsistencies; conflicting data; conflicting information; structural errors; conflicting data structures from multiple sources; conflicting naming of data from multiple sources; value-field mismatch; single field holding multiple types of values & [1, 10, 11, 13, 15, 16, 17, 30, 31, 32] \\
6 & Sparse data & The data collected are not evenly distributed in the temporal or spatial dimension, making the data appear discontinuous and incomplete. & Unveven data distribution; discontinuous data; sparse trajectories & [13, 33, 34, 35, 36] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarizing and defining data quality issues. In fact, several pieces of literature have already proposed different categorization strategies for DQ problems. We identified two primary challenges during our investigation process. The aforementioned issues are common in current solutions. Firstly, we observed that the same specific data quality issue was assigned different names in different papers in the literature. For instance, as shown in the fourth category in Table 1, some studies describe data errors as \"incorrect data\", others as \"incorrect attributes\", and yet others use terms like \"incorrect input\", \"spelling errors\", \"incorrect words\", \"incorrect units\", \"incorrect date format\", etc.