Edge HVAC Analytics

A Knowledge Graph Driven Approach for Edge Analytics

Distributed Scheduling of Event Analytics across Edge and Cloud

Internet of Things (IoT) domains generate large volumes of high velocity event streams from sensors, which need to be analyzed with low latency to drive decisions. Complex Event Processing (CEP) is a Big Data technique to enable such analytics, and is traditionally performed on Cloud Virtual Machines (VM). Leveraging captive IoT edge resources in combination with Cloud VMs can offer better perf...

Firework: Data Processing and Sharing for Hybrid Cloud-Edge Analytics

Now we are entering the era of the Internet of Everything (IoE) and billions of sensors and actuators are connected to the network. As one of the most sophisticated IoE applications, real-time video analytics is promising to significantly improve public safety, business intelligence, and healthcare & life science, among others. However, cloudcentric video analytics requires that all video data ...

Quality-aware aggregation & predictive analytics at the edge

We investigate the quality of aggregation and predictive analytics in edge computing environments. Edge analytics require pushing processing and inference to the edge of a network of sensing & actuator nodes, which enables huge amount of contextual data to be processed in real time that would be prohibitively complex and costly to transfer on centralized locations. We propose a quality-aware, t...

Correction to: Predictive intelligence to the edge: impact on edge analytics

The article (Predictive intelligence to the edge: impact on edge analytics), written by (Christos Anagnostopoulos), was originally published electronically on the publisher’s internet portal (https://link.springer.com/journal/12530) on (8/28/2017 3:34:38 PM) without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on [11/17/2017] to © The Aut...