  In data exploration, users need to analyze large data files quickly, aiming
to minimize data-to-analysis time. While recent adaptive indexing approaches
address this need, they are cases where demonstrate poor performance.
Particularly, during the initial queries, in regions with a high density of
objects, and in very large files over commodity hardware. This work introduces
an approach for adaptive indexing driven by both query workload and
user-defined accuracy constraints to support approximate query answering. The
approach is based on partial index adaptation which reduces the costs
associated with reading data files and refining indexes. We leverage a
hierarchical tile-based indexing scheme and its stored metadata to provide
efficient query evaluation, ensuring accuracy within user-specified bounds. Our
preliminary evaluation demonstrates improvement on query evaluation time,
especially during initial user exploration.
