State-of-the-art OLAP systems tend to use columnar data representations,
as these are both suitable for analytics and amenable to compression.
Local dictionary value encoding has been shown to achieve high
compression rates for string columns while still allowing fast filtered
scans. In this paper, we argue that the effectiveness and efficiency of
local dictionary compression is limited by data repetition across file
blocks and by dictionary look-ups inside each block during filtered scan
execution. To address this problem, we introduce an adaptive compression
technique that is based on differential dictionaries and targets both
storage efficiency and query performance. The proposed scheme reduces
dramatically the need to store repeated values across different file
blocks and significantly accelerates read operations by reducing the
time needed for dictionary look-ups. A preliminary set of experiments
has given very promising results, showing that, in many cases, the
proposed new dictionary compression scheme is much more efficient than
existing techniques, occasionally up to an order of magnitude