# A Review of the Development and Future Challenges of Case-Based Reasoning
## 3 Development of CBR Key Technologies
### Case Base Maintenance (CBM)  
As CBR systems were developed and deployed for real-world application scenarios, the potential pitfalls of long-term case learning became apparent, especially in relation to the impact of case-based growth on retrieval costs [15]. In the process of case studies, cases are continuously saved to the case base (CB). On the one hand, the increase in the number of cases helps to improve the problem-solving ability of CBR systems. However, with the increase in the CB scale, redundant, repetitive, or even wrong cases increase the time complexity of the systems and reduce the overall performance of the CBR system. Especially in the current era of information flooding, many case bases are already very large premises. Case base maintenance (CBM) has also begun to become more important, and related research is gradually increasing.  
CBM ensures the performance of CBR systems by deleting, adding, updating, and modifying cases and related data in the case base. [PERSON] and [PERSON] [85] defined CBM as the method used in CBR to operate and organize the content of its case base in order to improve or maintain the ability of CBR technology. We summarize the general CBM maintenance process in Figure 7. The basic requirement of CBM is that CBR systems need to maintain the existing case base without affecting its problem-solving capability [86]. [PERSON] et al. [87] first proposed dividing CBM into the following two strategies: case base division and case base optimization. In recent years, [PERSON] et al. [88] proposed dividing CBM into the following steps:  
1. Direct models [89; 90; 91]: They do not consider the relationship between cases and do not otherwise retrieve information from individual cases.
2. Hybrid model [87; 92]: They describe an association between cases by integrating different AI methods.  
3. Case property model [93; 94]: They integrate additional information into the case to better illustrate the features of the case in the case base to achieve the maintenance function.  
Although the direct model is simple, the effect is relatively poor. The case attribute model is maintained by adding additional information, but the selection and addition of additional information are limited by the case base itself, which needs to be analyzed on a case-by-case basis. Therefore, the hybrid model is currently the most popular research method. For example, [PERSON] et al. [88] proposed a CBM model based on reputation values, calculating a case attribute called reputation for each member of the case base, whose value reflects the capability of the relevant case. Based on this case attribute, several removal strategies and maintenance methods are designed. Each strategy focuses on different aspects of case-based maintenance. [PERSON] et al. [95] proposed a CBM strategy based on active semi-supervised maintenance (ASSM), using ML technology to solve the problem of scarcity of marked cases. [PERSON] et al. [96] provided a new alternating technique to correctly detect noise and redundancy in cases or features. It is a method of dynamically maintaining the case base and can maintain the vocabulary knowledge carrier of CB and CBR systems at the same time.