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 …

Stream Processing (SP) has evolved as the leading paradigm to process and gain value
from the high volume of streaming data produced, eg, in the domain of the Internet of Things …

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 …

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 …

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 …

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 …

The contribution of cloud computing and mobile computing technologies lead to the newly
emerging mobile cloud computing paradigm. Three major approaches have been proposed …

This paper presents the design of a read-optimized relational DBMS that contrasts sharply
with most current systems, which are write-optimized. Among the many differences in its …

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

Traditional databases store sets of relatively static records with no pre-defined notion of time,
unless timestamp attributes are explicitly added. While this model adequately represents …