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Internet of Things
time series forecasting
Projekte & Rollen
Data Management Strategies for Near Real-Time Edge Analytics
Engineering Principles for Edge Data Services
A way of combining inputs, such as application information and data workload characteristics, must enable dynamic, software-defined components for the overall edge storage management.
Investigating Elasticity for Edge Storage Services
To achieve optimized design of elastic edge storage services, requirements coming from edge data and system characterization, application-specific analytics context, and edging system operations, should be considered.
Need for Elastic Edge Data Services
In the IoT, a massive number of smart devices produce a variety of data at unprecedented scale. The next generation edge storage service must make sure that the edge analytics is always served with relevant and suitable data.
Towards Self-adaptive Technique Selection for Edge Data Recovery
One of the solutions for efficient and automatic edge data recovery of different gaps might include pre-calculated projection recovery maps (PRMs) that recommend techniques and needed ranges of data for detected gaps.
From raw sensor data to smart actuator decisions
To timely manage accurate and automatic decisions, future management of IoT systems must deal with incomplete data, the big data volume and limited capacity of storage resources, imposing a solution for efficient data management, called EDMFrame.
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