Generic model for multidimensional data stream summary

With the advent of Big Data, we are witnessing a rapid and varied production of huge amounts of sequential data that can have multiple dimensions. We are talking about data streams. The characteristics of these data streams make their processing and storage very difficult and at the same time their querying a posteriori. Thus, to deal with this multidimensional aspect of data streams, new systems have been carried out. These propositions have a lot of advantages. However, they do not always enable to best meet the constraints subjected to data streams, such as processing and storage costs. In this sense, we propose in this paper a new generic model for summarizing multidimensional data streams and to deal with storage and processing constraints. This solution uses cascading cubes with different time window hierarchies combined with customizable aggregation and propagation functions for the processing and the storage of multidimensional data streams. Experimentations have been performed with electrical data stream and result were satisfactory.