Data integration over multiple heterogeneous data sources has become increasingly important for modern applications. The integrated data is usually stored as materialized views to allow better access, performance, and high availability. In loosely coupled environments, such as the data grid, the data sources are autonomous. Hence, tie source updates can be concurrent and cause erroneous results during view maintenance. State-of-the-art maintenance strategies apply compensating queries to correct such errors, making the restricting assumption that all source schemata remain static over time. However, in such dynamic environments, the data sources may change not only their data but also their schema. Consequently, either the maintenance queres or the compensating queries may fail. In this paper, we propose a novel framework called DyDa that overcomes these limitations and handles both source data updates and schema changes. We identify three types of maintenance anomalies, caused by either source data updates, data-preserving schema changes, or non-data-preserving schema changes. We propose a compensation algorithm to solve the first two types of anomalies. We show that the third type of anomaly is caused by the violation of dependencies between maintenance processes. Then, we propose dependency detection and correction algorithms to identify and resolve the violations. Put together, DyDa extends prior maintenance solutions to solve all types of view maintenance anomalies. The experimental results show that DyDa imposes a minimal overhead on data update processing while allowing for the extended functionality to handle concurrent schema changes.

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 ongoing view maintenance fail. 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.

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Some of the most recently proposed algorithms for the incremental maintenance of materialized data warehouses (DW), such as SWEEP and PSWEEP, offer several significant advantages over previous solutions, such as high-performance, no potential for infinite waits and reduced remote queries and thus reduced network and information source (IS) loads. However, similar to many other algorithms, they still have the restricting assumption that each IS can be composed of just one single relation. This is unrealistic in practice. In this paper, we hence propose a solution to overcome this restriction. The Multi-Relation Encapsulation (MRE) Wrapper supports multiple relations in ISs in a manner transparent to the rest of the environment. The MRE Wrapper treats one IS composed of multiple relations as if it were a single relation from the DW point of view; thus any existing incremental view maintenance algorithms can now be applied even to such complex ISs without any changes. Hence, our method maintains all advantages offered by existing algorithms in particular SWEEP and PSWEEP, while also achieving the additional desired features of being non-intrusive, efficient, flexible and well-behaved.

Summary form only given. Data warehouses (DW) are built by gathering information from several information sources (IS) and integrating it into one repository customized to users' needs. ISs are typically owned by different information providers and hence are independent and autonomous. This implies they will update their data and schemas independently and without any concern for how this may affect the DW defined upon them. Hence, solutions for data warehouse maintenance are needed that can handle such IS updates. We have developed a first comprehensive solution that successfully tackles this problem (X. Zhang et al., 1998). In particular, to guarantee the correctness of concurrent view definition evolution and view extent maintenance of a data warehouse, we introduce a framework called the SDCC (Schema change and Data update Concurrency Control) system. SDCC integrates various algorithms designed to address different individual view maintenance subproblems into one system, such as algorithms for view extent maintenance after IS data updates, for view definition evolution after IS schema changes, and for view extent adaptation after view definition changes.

Recently proposed view maintenance algorithms tackle the problem of concurrent data updates happening at different autonomous information systems (ISs), whereas the EVE (Evolvable View Environment) system addresses the maintenance of a data warehouse (DW) after IS schema changes. The concurrency of schema changes and data updates still remains an unexplored problem, however. This paper provides a solution that guarantees concurrent view definition evolution and view extent maintenance of a DW defined over distributed ISs. For this problem, we introduce a framework called SDCC (Schema change and Data update Concurrency Control) system. SDCC integrates existing algorithms designed to address view maintenance subproblems (such as view extent maintenance after IS data updates, view definition evolution after IS schema changes, and view extent adaptation after view definition changes) into one system by providing protocols that enable them to correctly co-exist and collaborate. SDCC tracks any potential faulty updates of the DW caused by conflicting concurrent IS changes, and compensates for such conflicting updates by a local correction strategy called local compensation.

Supporting independent ISs and integrating them in distributed data warehouses (materialized views) is becoming more important with the growth of the WWW. However, views defined over autonomous ISs are susceptible to schema changes. In the EVE project we are developing techniques to support the maintenance of data warehouses defined over distributed dynamic ISs [5, 6, 7]. The EVE system is the first to allow views to survive schema changes of their underlying ISs while also adapting to changing data in those sources. EVE achieves this is two steps: applying view query rewriting algorithms that exploit information about alternative ISs and the information they contain, and incrementally adapting the view extent to the view definition changes. Those processes are referred to as view synchronization and view adaption, respectively. They increase the survivability of materialized views in changing environments and reduce the necessity of human interaction in system maintenance.