  This article provides a comprehensive description of Text Analytics Directly
on Compression (TADOC), which enables direct document analytics on compressed
textual data. The article explains the concept of TADOC and the challenges to
its effective realizations. Additionally, a series of guidelines and technical
solutions that effectively address those challenges, including the adoption of
a hierarchical compression method and a set of novel algorithms and data
structure designs, are presented. Experiments on six data analytics tasks of
various complexities show that TADOC can save 90.8% storage space and 87.9%
memory usage, while halving data processing times.
