HIDS: a multifunctional generator of hierarchical data streams

In the research of high-speed data streams, large amounts of synthetic data are needed. These days, more and more researchers focus on hierarchical multi-dimensional data streams or data sets, which is beyond the ability of traditional synthetic data generators. In this paper we propose a two-phased method to generate hierarchical multi-dimensional data streams, in which a tree-like structure is built first, and then an unlimited number of items chosen among the tree leaves according to a distribution are inserted into the stream. Our generator, HIDS, integrates all of the functions of existing data generators, and can customize the tree structure according to usersý requirements, producing tree structures such as equal-depth trees, equal-fan-out trees, balanced trees and different-fan-out trees. An experimental study using real data streams shows that HIDS can generate data streams tailored to specific applications.