  Traditional indexing techniques commonly employed in da\-ta\-ba\-se systems
perform poorly on multidimensional array scientific data. Bitmap indices are
widely used in commercial databases for processing complex queries, due to
their effective use of bit-wise operations and space-efficiency. However,
bitmap indices apply natively to relational or linearized datasets, which is
especially notable in binned or compressed indices.
  We propose a new method for multidimensional array indexing that overcomes
the dimensionality-induced inefficiencies. The hierarchical indexing method is
based on $n$-di\-men\-sional sparse trees for dimension partitioning, with
bound number of individual, adaptively binned indices for attribute
partitioning. This indexing performs well on range involving both dimensions
and attributes, as it prunes the search space early, avoids reading entire
index data, and does at most a single index traversal. Moreover, the indexing
is easily extensible to membership queries.
  The indexing method was implemented on top of a state of the art bitmap
indexing library Fastbit. We show that the hierarchical bitmap index
outperforms conventional bitmap indexing built on auxiliary attribute for each
dimension. Furthermore, the adaptive binning significantly reduces the amount
of bins and therefore memory requirements.
