Carefully balancing load in distributed stream processing systems has a fundamental impact
on execution latency and throughput. Load balancing is challenging because real-world …

In a big data stream computing environment, the arrival rate of data streams usually
fluctuates over time, posing a great challenge to the elasticity of system. The performance of …

We address the joint optimization of multiple stream joins in a scale-out architecture by
tailoring prior work on multi-way stream joins to predicate-driven data partitioning schemes …

Streaming applications frequently encounter skewed workloads and execute on
heterogeneous clusters. Optimal resource utilization in such adverse conditions becomes a …

We propose MultiStream, a novel multi-way join operator that optimizes tuple-routing
schemes across materialized relations and intermediate results to compute the join results. It …

Stream Processing Engines (SPEs) have to support high data ingestion to ensure the quality
and efficiency for the end-user or a system administrator. The data flow processed by SPE …

The big data era is characterized by the emergence of live data with high volume and fast
arrival rate, it poses a new challenge to stream processing applications: how to process the …

In the last decade, real-time data processing has attracted much attention from both
academic community and industry, as the meaning of big data has evolved to incorporate as …

When processing data streams with highly skewed and nonstationary key distributions, we
often observe overloaded partitions when the hash partitioning fails to balance data …

In the last decade, most of the research and industry has been focused on processing big
and fast data as massive amount of data has been produced on numerous handheld …