Various research communities have independently arrived at stream processing as a
programming model for efficient and parallel computing. These communities include digital …

Data stream processing (DSP) has emerged over the years as the reference paradigm for
the analysis of continuous and fast information flows, which often have to be processed with …

Borealis is a second-generation distributed stream processing engine that is being
developed at Brandeis University, Brown University, and MIT. Borealis inherits core stream …

Many applications in several domains such as telecommunications, network security, large-
scale sensor networks, require online processing of continuous data flows. They produce …

Today we are witnessing a dramatic shift toward a data-driven economy, where the ability to
efficiently and timely analyze huge amounts of data marks the difference between industrial …

TimeStream is a distributed system designed specifically for low-latency continuous
processing of big streaming data on a large cluster of commodity machines. The unique …

We study the problem of load balancing in distributed stream processing engines, which is
exacerbated in the presence of skew. We introduce Partial Key Grouping (PKG), a new …

The nature of data in enterprises and on the Internet is changing. Data used to be stored in a
database first and queried later. Today timely processing of new data, represented as …

The need for real-time processing of" big data" has led to the development of frameworks for
distributed stream processing in clusters. It is important for such frameworks to be robust …

Typical use cases like financial trading or monitoring of manufacturing equipment pose huge
challenges regarding end to end latency as well as throughput towards existing data stream …