Cloud-native database systems have started to gain broad support and popularity due to more and more applications and systems moving to the cloud. Various cloud-native databases have been emerging in recent years, but their developments are still in the primary stage. At this stage, database developers are generally confused about improving the performance of the database by applying AI technologies. The maturity model can help database developers formulate the measures and clarify the improvement path during development. However, the current maturity models are unsuitable for cloud-native databases since their architecture and resource management differ from traditional databases. Hence, we propose a maturity model for AI-empowered cloud-native databases from the perspective of resource management. We employ a systematic literature review and expert interviews to conduct the maturity model. Also, we develop an assessment tool based on the maturity model to help developers assess cloud-native databases. And we provide an assessment case to prove our maturity model. The assessment case results show that the database’s development direction conforms to the maturity model. It proves the effectiveness of the maturity model.

This paper reports an interesting case study on the Legacy Data Integration (LDI for short) for a Regional Cloud Arbitration Court. Due to the inconsistent structure and presentation, legacy arbitration cases can hardly integrate into the Cloud Court unless processed manually. In the case study, we aim to build an AI-enabled LDI method to replace the high-cost manual one and protect privacy during the process. Our method employs Optical Character Recognition (OCR), text classification, Named Entity Recognition (NER), and entity relation extraction to transform legacy data into system format. We train AI models to replace the tasks of the Court staff, such as reading and understanding legacy cases, removing privacy information, composing new records of cases to fit the Cloud Court, and inputting them through the system interfaces. With the applications of a Cloud Arbitration Court in Liaoning Provence, China, our intelligent LDI has similar effectiveness but greater efficiency than the manual LDI. Our method saves 90% of the workforce and achieves a 60%-70% information extraction rate of manual work. Our method achieves a comparable filtering effect for privacy while retaining the maximum amount of information. With the continuous development of informationalization and intelligentization in judgment and arbitration, many courts are building the court system using ABC technologies, namely Artificial intelligence, Big data, and Cloud computing. Our method could provide a practical reference when integrating legal data into the system.

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The role of feedback is often mentioned in the field of second language acquisition (SLA). It would affect the learning process and final proficiency level of the second language (L2) learners. This study investigated the influence of different types of feedback on L2 learners’ writing and learners’ acceptance of feedback. Results show that different types of feedback have different effects on diverse aspects of writing, and comprehensive feedback is the most effective method for the overall improvement of writing. In terms of learners’ acceptance of feedback, social, environmental, and personal factors are all influencing factors. These provide guidance for subsequent second language teaching, especially in L2 writing.