  Over the last few years, data analytics shifted from a descriptive era,
confined to the explanation of past events, to the emergence of predictive
techniques. Nonetheless, existing predictive techniques still fail to
effectively explore alternative futures, which continuously diverge from
current situations when exploring the effects of what-if decisions. Enabling
prescriptive analytics therefore calls for the design of scalable systems that
can cope with the complexity and the diversity of underlying data models. In
this article, we address this challenge by combining graphs and time series
within a scalable storage system that can organize a massive amount of
unstructured and continuously changing data into multi-dimensional data models,
called Many-Worlds Graphs. We demonstrate that our open source implementation,
GreyCat, can efficiently fork and update thousands of parallel worlds composed
of millions of timestamped nodes, such as what-if exploration.
