Data integration over multiple heterogeneous data sources has become increasingly important for modern applications. The integrated data is usually stored in materialized views for high availability and better performance. Such views must be maintained after the data sources change. In a loosely-coupled and dynamic environment, such as the Data Grid, the sources may autonomously change not only their data but also their schema, query capabilities or semantics, which may consequently cause the on-going view maintenance fail. In this paper, first, we analyze the maintenance errors and classify them into different classes of dependencies. We then propose several dependency detection and correction algorithms to handle these new classes of concurrency. Our techniques are not tied to specific maintenance algorithms nor to a particular data model. To our knowledge, this is the first complete solution to the view maintenance concurrency problems for both data and schema changes. We have implemented the proposed solutions and experimentally evaluated the impact of anomalies on maintenance performance and trade-offs between different dependency detection algorithms.