Many real-life databases are updated by means of incoming business information. In these databases (e.g., transactional data from large retail chains, call-detail records), the content evolves through periodical insertions (or deletions) of data blocks. Since data evolve over time, algorithms have to be devised to incrementally update data mining models. This paper presents a novel index, called I-Forest, to support itemset mining on incoming data blocks, where new blocks are inserted periodically, or old blocks are discarded. The I-Forest structure provides a complete data representation and allows different kind of analyses (e.g., investigate quarterly data), besides supporting user-defined time and support constraints. The I-Forest index has been implemented into the PostgreSQL open source DBMS and exploits its physical level access methods. Experiments, run for both sparse and dense data distributions, show the effectiveness of the I-Forest-based approach to perform itemset mining with both time and support constraints. The execution time of the I-Forest-based itemset mining technique is often faster than the Prefix-Tree algorithm accessing static data on flat files.