Schuler Robert, Czajkowski Karl, D'Arcy Mike, Tangmunarunkit Hongsuda, Kesselman Carl
USC Information Sciences Institute, Marina del Rey, California.
Sci Stat Database Manag. 2020 Jul;2020. doi: 10.1145/3400903.3400908. Epub 2020 Jul 30.
Database evolution is a notoriously difficult task, and it is exacerbated by the necessity to evolve database-dependent applications. As science becomes increasingly dependent on sophisticated data management, the need to evolve an array of database-driven systems will only intensify. In this paper, we present an architecture for data-centric ecosystems that allows the components to seamlessly co-evolve by centralizing the models and mappings at the data service and pushing model-adaptive interactions to the database clients. Boundary objects fill the gap where applications are unable to adapt and need a stable interface to interact with the components of the ecosystem. Finally, evolution of the ecosystem is enabled via integrated schema modification and model management operations. We present use cases from actual experiences that demonstrate the utility of our approach.
数据库演进是一项众所周知的艰巨任务，而由于需要对依赖数据库的应用程序进行演进，这一任务变得更加棘手。随着科学越来越依赖复杂的数据管理，演进一系列数据库驱动系统的需求只会日益增强。在本文中，我们提出了一种以数据为中心的生态系统架构，该架构通过在数据服务中集中模型和映射，并将模型自适应交互推向数据库客户端，使组件能够无缝地共同演进。边界对象填补了应用程序无法适应且需要稳定接口与生态系统组件进行交互的空白。最后，通过集成模式修改和模型管理操作实现生态系统的演进。我们展示了来自实际经验的用例，以证明我们方法的实用性。
