Recently, big data streams have become ubiquitous due to the fact that a number of
applications generate a huge amount of data at a great velocity. This made it difficult for …

Big data processing systems are evolving to be more stream oriented where each data
record is processed as it arrives by distributed and low-latency computational frameworks on …

Recent advances in computer vision-in the form of deep neural networks-have made it
possible to query increasing volumes of video data with high accuracy. However, neural …

Unbounded, unordered, global-scale datasets are increasingly common in day-to-day
business (eg Web logs, mobile usage statistics, and sensor networks). At the same time …

Large volumes of videos are continuously recorded from cameras deployed for traffic control
and surveillance with the goal of answering “after the fact” queries: identify video frames with …

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 …

Many" big data" applications must act on data in real time. Running these applications at
ever-larger scales requires parallel platforms that automatically handle faults and stragglers …

The emerging class of wide-area streaming analytics faces the challenge of scarce and
variable WAN bandwidth. Non-adaptive applications built with TCP or UDP suffer from …

Stream processors are emerging in industry as an apparatus that drives analytical but also
mission critical services handling the core of persistent application logic. Thus, apart from …

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 …