  The advent of the Internet of Things (IoT) gives the opportunity to numerous
devices to interact with their environment, collect and process data. Data are
transferred, in an upwards mode, to the Cloud through the Edge Computing (EC)
infrastructure. A high number of EC nodes become the hosts of distributed
datasets where various processing activities can be realized in close distance
with end users. This approach can limit the latency in the provision of
responses. In this paper, we focus on a model that proactively decides where
the collected data should be stored in order to maximize the accuracy of
datasets present at the EC infrastructure. We consider that the accuracy is
defined by the solidity of datasets exposed as the statistical resemblance of
data. We argue upon the similarity of the incoming data with the available
datasets and select the most appropriate of them to store the new information.
For alleviating processing nodes from the burden of a continuous, complicated
statistical processing, we propose the use of synopses as the subject of the
similarity process. The incoming data are matched against the available
synopses based on an ensemble scheme, then, we select the appropriate host to
store them and perform the update of the corresponding synopsis. We provide the
description of the problem and the formulation of our solution. Our
experimental evaluation targets to reveal the performance of the proposed
approach.
