Modern computing tasks such as real-time analytics require refresh of query results under
high update rates. Incremental View Maintenance (IVM) approaches this problem by …

The concept of state and its applications vary widely across big data processing systems.
This is evident in both the research literature and existing systems, such as Apache Flink …

We introduce partially-stateful data-flow, a new streaming data-flow model that supports
eviction and reconstruction of data-flow state on demand. By avoiding state explosion and …

Stream processing has become the dominant processing model for monitoring and real-time
analytics. Modern Parallel Stream Processing Engines (pSPEs) have made it feasible to …

We introduce F-IVM, a unified incremental view maintenance (IVM) approach for a variety of
tasks, including gradient computation for learning linear regression models over joins, matrix …

Views materialization is a powerful technique for optimization of query evaluation. However,
when data sources are updated, these materialized views (MVs) should be maintained in …

We revisit the classical change propagation framework for query evaluation under updates.
The standard framework takes a query plan and materializes the intermediate views, which …

This article describes F-IVM, a unified approach for maintaining analytics over changing
relational data. We exemplify its versatility in four disciplines: processing queries with group …

We investigate the query evaluation problem for fixed queries over fully dynamic databases
where tuples can be inserted or deleted. The task is to design a dynamic data structure that …

Current systems for data-parallel, incremental processing and view maintenance over high-
rate streams isolate the execution of independent queries. This creates unwanted …