  In recent times, the production of multidimensional data in various domains
and their storage in array databases has witnessed a sharp increase; this rapid
growth in data volumes necessitates compression in array databases. However,
existing compression schemes used in array databases are general-purpose and
not designed specifically for the databases. They could degrade query
performance with complex analytical tasks, which incur huge computing costs.
Thus, a compression scheme that considers the workflow of array databases is
required. This study presents a compression scheme, SEACOW, for storing and
querying multidimensional array data. The scheme is specially designed to be
efficient for both dimension-based and value-based exploration. It considers
data access patterns for exploration queries and embeds a synopsis, which can
be utilized as an index, in the compressed array. In addition, we implement an
array storage system, namely MSDB, to perform experiments. We evaluate query
performance on real scientific datasets and compared it with those of existing
compression schemes. Finally, our experiments demonstrate that SEACOW provides
high compression rates compared to existing compression schemes, and the
synopsis improves analytical query processing performance.
