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.

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.

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.

Although, there is a number of studies exploring emerging edge computing technology, the state-of-the-art still lacks contributions in efficient data management strategies for near real-time edge analytics.

While cloud computing offers dynamic and scalable resources, edge computing pushes data processing to the edge of the network, in proximity of data sources. However, many requirements and limitations must be considered for reliable data analytics.