# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 2 Related Work  
In our investigation of DQ-related research, we used Web of Science (WOS), IEEE Xplore (IEEE), the Association for Computing Machinery (ACM), Springer-Verlag (Springer), and China National Knowledge Internet (CNKI) as the data sources. On one hand, WOS, IEE, ACM, and Springer are four mainstream academic databases used worldwide, where most valuable research papers written in English are collected. On the other hand, CNKI is a widely used academic database in China, where the most valuable research papers written in Chinese are archived. Considering that our research datasets were collected from a major Chinese city, we also chose CNKI as a data source. In the search procedure, we employed the search terms \"data quality\" and \"data cleaning\", and specified the time range of 2000-2022 to retrieve literature from each database (retrieval date: 7 April 2023). After filtering literature that was clearly inconsistent with our research topic, a total of 26,160 papers were obtained as the original papers for this study.  
Figure 1 shows the distribution of the related literature from 2000 to 2022. We can clearly see that research on the topic of data quality has experienced explosive growth in recent years. It can be seen that there were relatively few academic studies on data quality in the 2000s, with a total of 134 published papers. A total of 756 papers was published in 2011, while a total of 3552 papers was published in 2022. This indicates that data quality has attracted extensive attention from both domestic and international scholars.  
For example, in ref. [7], the authors propose an intelligent preprocessing method for textual data that cleans data containing missing values, grammatical errors, and spelling mistakes. Similarly, ref. [8] explores the impact of noise issues such as misspellings and missing data on the task of detecting different records that refer to the same entity. In the smart grid domain, ref. [9] explores related applications and classifies dirty data into three categories: duplicate data, anomalous data, and incomplete data. Ref. [10] investigates DQ issues such as outliers, incomplete data, duplicate data, and conflicts and develops a tool called _Cleanix_, which is a prototype system used to clean up these issues. In addition, ref. [1]  
Figure 1: Distribution of data quality related literature from 2000–2022.  
summarizes data-cleaning techniques and defines four types of DQ problems: missing data, redundant data, data conflicts, and erroneous data. Ref. [11] describes DQ issues such as inconsistency, noise, incomplete, or duplicate values in real-world data.  
DQ problems in the public bus transportation system have also been addressed. For example, ref. [12] divided the scope of research on intelligent traffic prediction into four parts: spatiotemporal data, pre-processing, traffic prediction, and traffic applications, where problems such as data anomalies and missing data were analyzed in the data pre-processing stage. Ref. [13] examined issues with data sparsity in bus data, including inconsistencies between operators, GPS location errors, and sporadic GPS sampling. In a similar vein, ref. [14] categorized bus anomalous data into four groups based on the characteristics of bus big data: redundant data, range anomalous data, abnormal data, and missing data. Finally, ref. [15] analyzed four types of DQ problems, namely noisy data, missing values, inconsistency, and redundant data, in taxi track data.