  The development of novel platforms and techniques for emerging "Big Data"
applications requires the availability of real-life datasets for data-driven
experiments, which are however out of reach for academic research in most cases
as they are typically proprietary. A possible solution is to use synthesized
datasets that reflect patterns of real ones in order to ensure high quality
experimental findings. A first step in this direction is to use inverse mining
techniques such as inverse frequent itemset mining (IFM) that consists of
generating a transactional database satisfying given support constraints on the
itemsets in an input set, that are typically the frequent ones. This paper
introduces an extension of IFM, called many-sorted IFM, where the schemes for
the datasets to be generated are those typical of Big Tables as required in
emerging big data applications, e.g., social network analytics.
