A large number of distributed applications requires continuous and timely processing of
information as it flows from the periphery to the center of the system. Examples include …

The traditional databases are not capable of handling unstructured data and high volumes
of real-time datasets. Diverse datasets are unstructured lead to big data, and it is laborious …

Organizations deploy a hierarchy of clusters-cameras, private clusters, public clouds-for
analyzing live video feeds from their cameras. Video analytics queries have many …

Video cameras are pervasively deployed for security and smart city scenarios, with millions
of them in large cities worldwide. Achieving the potential of these cameras requires …

Managing and securing networks requires collecting and analyzing network traffic data in
real time. Existing telemetry systems do not allow operators to express the range of queries …

MillWheel is a framework for building low-latency data-processing applications that is widely
used at Google. Users specify a directed computation graph and application code for …

In the data stream scenario, input arrives very rapidly and there is limited memory to store
the input. Algorithms have to work with one or few passes over the data, space less than …

MapReduce is a popular framework for data-intensive distributed computing of batch jobs.
To simplify fault tolerance, many implementations of MapReduce materialize the entire …

This paper describes the basic processing model and architecture of Aurora, a new system
to manage data streams for monitoring applications. Monitoring applications differ …

As users of" big data" applications expect fresh results, we witness a new breed of stream
processing systems (SPS) that are designed to scale to large numbers of cloud-hosted …