Qu, WeipingDessloch, Stefan2018-01-102018-01-1020142014https://dl.gi.de/handle/20.500.12116/11715Next-generation business intelligence (BI) enables enterprises to quickly react in changing business environments. Increasingly, data integration pipelines need to be merged with query pipelines for real-time analytics from operational data. Newly emerging hybrid analytic flows have been becoming attractive which consist of a set of extract-transform-load (ETL) jobs together with analytic jobs running over multiple platforms with different functionality.In traditional databases, materialized views are used to optimize query performance. In cross-platform, large-scale data transformation environments, similar challenges (e.g. view selection) arise when using materialized views. In this work, we propose an approach that generates materialized views in hybrid flows and maintains these views in a query-driven, incremental manner. To accelerate data integration processes, the location of a materialization point in a transformation flow varies dynamically based on metrics like source update rates and maintenance cost in terms of flow operations. Besides, by picking up the most suitable platform for accommodating views, for example, materializing and maintaining intermediate results of Hadoop jobs in relational databases, better performance has been shown.Hybrid analytic flowsIncremental view maintenanceReal-time data managementText/Journal Article1610-1995