  Relational queries, and in particular join queries, often generate large
output results when executed over a huge dataset. In such cases, it is often
infeasible to store the whole materialized output if we plan to reuse it
further down a data processing pipeline. Motivated by this problem, we study
the construction of space-efficient compressed representations of the output of
conjunctive queries, with the goal of supporting the efficient access of the
intermediate compressed result for a given access pattern. In particular, we
initiate the study of an important tradeoff: minimizing the space necessary to
store the compressed result, versus minimizing the answer time and delay for an
access request over the result. Our main contribution is a novel parameterized
data structure, which can be tuned to trade off space for answer time. The
tradeoff allows us to control the space requirement of the data structure
precisely, and depends both on the structure of the query and the access
pattern. We show how we can use the data structure in conjunction with query
decomposition techniques, in order to efficiently represent the outputs for
several classes of conjunctive queries.
