update usecases (#137)

Author: taoshengshi

README.md

@@ -35,8 +35,8 @@ JiaoziFS's versatility shines across different industries – making it the mult
 
 * **Enterprise DataHub & Data Collaboration**: Depending on your operating scale, you may even be managing multiple team members, who may be spread across different locations. JiaoziFS enable Collaborative Datasets Version Management at Scale,Share & collaborate easily: Instantly share insights and co-edit with your team.
 * **DataOps & Data Products & Data Mesh**: Augmenting Enterprise Data Development and Operations,JiaoziFS ensures Responsible DataOps/AIOps/MLOps by improving Data Versioning, Provenance, and Reproducibility. JiaoziFS makes a fusion of data science and product development and allows data to be containerized into shareable, tradeable, and trackable assets(data products or data NFTs). Versioning data products in a maturing Data Mesh environment via standard processes, data consumers can be informed about both breaking and non-breaking changes in a data product, as well as retirement of data products.
-* **Digital Twins for Manufacturing**: Developing digital twins for manufacturing involves managing tons of large files and multiple iterations of a project. All of the data collected and created in the digital twin process (and there is a lot of it) needs to be managed carefully. JiaoziFS allows you to manage changes to files over time and store these modifications in a database.
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+* **Industrial Digital Twin**: Developing digital twins for manufacturing involves managing tons of large files and multiple iterations of a project. All of the data collected and created in the digital twin process (and there is a lot of it) needs to be managed carefully. JiaoziFS allows you to manage changes to files over time and store these modifications in a database.
+* **Data Lake Management**: Data lakes are dynamic.   New files and new versions of ex- isting files enter the lake at the ingestion stage.   Additionally, extractors can evolve over time and generate new versions of raw data.   As a result, data lake versioning is a cross-cutting concern across all stages of a data lake.   Of course vanilla dis- tributed file systems are not adequate for versioning-related operations.   For example, simply storing all versions may be too costly for large datasets, and without a good version manager, just using filenames to track versions can be error-prone.   In a data lake, for which there are usually many users, it is even more important to clearly maintain correct versions being used and evolving across different users.   Furthermore, as the number of versions increases, efficiently and cost-effectively providing storage and retrieval of versions is going to be an important feature of a successful data lake system.
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 ### Spec
 [JiaoziFS Specification](https://github.com/jiaozifs/Spec)