Context Stream processing is a popular paradigm to continuously process huge amounts of
data. Runtime adaptation plays a significant role in supporting the optimization of data …

With the continued deployment of the Internet of Things (IoT), increasing volumes of devices
are being deployed that emit massive spatially referenced data. Due in part to the dynamic …

LSM-Tree is widely adopted for storing time-series data in Internet of Things. According to
conventional policy (denoted by c), when writing, the data will first be buffered in MemTable …

The inherently large and varying volumes of information generated in large scale systems
demand near real-time processing of data streams. In this context, data streaming is …

Applications for adaptive (sometimes also called smart) Cyber-Physical Systems are
blossoming thanks to the large volumes of data, sensed in a continuous fashion, in large …

Streaming applications continuously process data to deliver streams of up-to-date results.
Their growing adoption for data analysis in many distributed systems is motivated by their …

Fine-grained data provenance in data streaming allows linking each result tuple back to the
source data that contributed to it, something beneficial for many applications (eg, to find the …

Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially
referenced data are being generated. Due to the dynamic, decentralized, and …

The growing interest in data analysis applications for Cyber–Physical Systems stems from
the large amounts of data such large distributed systems sense in a continuous fashion. A …

Abstract Stream Processing Engines (SPEs) process continuous streams of data and
produce results in a real-time fashion, typically through one-at-a-time tuple analysis. In Fog …