There are a couple ways mitigate this drawback, both of which are outside the scope of this article. One is “garbage collection”: pruning tombstones from CRDTs, which prevents you from merging states with any changes made before the tombstones were removed. Another is creating an efficient format to encode the data. You can also combine these methods. Research suggests that this can result in as little as 50% overhead compared to the “plain” data CRDTs: The Hard Parts A talk on the latest research on CRDTs, originally given at the Hydra distributed computing conference on 6 July 2020.References: https://martin.kleppmann.co... youtu.be/x7drE24geUw?t=3587 . If you’d like to skip ahead and see some of this optimization in action, check out the final part in this series: Making CRDTs 98% More Efficient Making CRDTs 98% More Efficient | jakelazaroff.com State-based CRDTs grow monotonically, but that doesn't mean they can't be efficient. We'll learn how to compress the pixel editor state by 98%. jakelazaroff.com/words/making-crdts-98-percent-more-efficient/ . ↩
强化数据链整合，构建“招培就”全周期数字画像体系。构建全周期数字画像体系，能够打通高校人才培养过程中招生、培养、就业3个关键环节的数据壁垒，实现数据的有效流通与共享，精准反馈人才培养各环节效果。通过深度分析“招培就”全周期数据，高校可及时调整招生策略、优化培养方案、改进就业服务，提高人才培养与社会需求的契合度。通过结合学生个人情况和职业规划意向，对学生学习过程数据如学习进度、作业完成情况、课堂表现、实践表现等进行分析，有助于为每名学生打造专属的学习路径。推动数字赋能高校人才培养质量提升，应鼓励高校充分利用大数据、云计算、人工智能等技术，建设集招生、教学、就业于一体，数据衔接贯通并可实时交互的综合性数字化平台，对“招培就”数据进行存储、分析和挖掘；加强对教师和管理人员的数据素养培训，提高其运用数据进行决策和教学的能力。
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