Data-Independent Space Partitionings for Summaries

Histograms are a standard tool in data management for describing multidimensional data. It is often convenient or even necessary to define data independent histograms, to partition space in advance without observing the data itself. Specific motivations arise in managing data when it is not suitable to frequently change the boundaries between histogram cells. For example, when the data is subject to many insertions and deletions; when data is distributed across multiple systems; or when producing a privacy-preserving representation of the data. The baseline approach is to consider an equiwidth histogram, i.e., a regular grid over the space. However, this is not optimal for the objective of splitting the multidimensional space into (possibly overlapping) bins, such that each box can be rebuilt using a set of non-overlapping bins with minimal excess (or deficit) of volume. Thus, we investigate how to split the space into bins and identify novel solutions that offer a good balance of desirable properties. As many data processing tools require a dataset as an input, we propose efficient methods how to obtain synthetic point sets that match the histograms over the overlapping bins.