Big Data has emerged in the past few years as a new paradigm providing abundant data
and opportunities to improve and/or enable research and decision-support applications with …

The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …

Big data has become a significant research area due to the birth of enormous data
generated from various sources like social media, internet of things and multimedia …

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 …

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 …

Big data has attracted much attention from academia and industry. But the discussion of big
data is disparate, fragmented and distributed among different outlets. This paper conducts a …

Event processing (EP) is a data processing technology that conducts online processing of
event information. In this survey, we summarize the latest cutting-edge work done on EP …

This paper addresses the problem of designing scaling strategies for elastic data stream
processing. Elasticity allows applications to rapidly change their configuration on-the-fly (eg …

Energy efficient workflow scheduling is the demand of the present time's computing
platforms such as an infrastructure-as-a-service (IaaS) cloud. An appreciable amount of …

Stream processing is an emerging paradigm to handle data streams upon arrival, powering
latency-critical application such as fraud detection, algorithmic trading, and health …