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 …

Many important “big data” applications need to process data arriving in real time. However,
current programming models for distributed stream processing are relatively low-level, often …

In this position paper, we motivate the need for streaming data integration in three main
forms including across multiple streaming data sources, over multiple stream processing …

We present JetStream, a system that allows real-time analysis of large, widely-distributed
changing data sets. Traditional approaches to distributed analytics require users to specify …

The Internet of Things (IoT) has become widespread around the world. Since a large
number of diverse devices, such as vehicles, household electrical appliances, smart …

The past few years have seen a major change in computing systems, as growing data
volumes and stalling processor speeds require more and more applications to scale out to …

A method and system for processing a data stream are described. The method executes,
until the occurrence of a cut condition, a map function from a set of query processing steps to …

" Big Data" in map-reduce (MR) clusters is often fundamentally temporal in nature, as are
many analytics tasks over such data. For instance, display advertising uses Behavioral …

Continuous analytics systems that enable query processing over steams of data have
emerged as key solutions for dealing with massive data volumes and demands for low …

Log analytics are a bedrock component of running many of today's Internet sites. Application
and click logs form the basis for tracking and analyzing customer behaviors and …