  With the recent proliferation of sensor data, there is an increasing need for
the efficient evaluation of analytical queries over multiple sensor datasets.
The magnitude of such datasets makes exact query answering infeasible, leading
researchers into the development of approximate query answering approaches.
However, existing approximate query answering algorithms are not suited for the
efficient processing of queries over sensor data, as they exhibit at least one
of the following shortcomings: (a) They do not provide deterministic error
guarantees, resorting to weaker probabilistic error guarantees that are in many
cases not acceptable, (b) they allow queries only over a single dataset, thus
not supporting the multitude of queries over multiple datasets that appear in
practice, such as correlation or cross-correlation and (c) they support
relational data in general and thus miss speedup opportunities created by the
special nature of sensor data, which are not random but follow a typically
smooth underlying phenomenon.
  To address these problems, we propose PlatoDB; a system that exploits the
nature of sensor data to compress them and provide efficient processing of
queries over multiple sensor datasets, while providing deterministic error
guarantees. PlatoDB achieves the above through a novel architecture that (a) at
data import time pre-processes each dataset, creating for it an intermediate
hierarchical data structure that provides a hierarchy of summarizations of the
dataset together with appropriate error measures and (b) at query processing
time leverages the pre-computed data structures to compute an approximate
answer and deterministic error guarantees for ad hoc queries even when these
combine multiple datasets.
