Ideally, realizing the best physical design for the current and all subsequent
workloads would impact neither performance nor storage usage.
In reality, workloads and datasets can
change dramatically over time and index creation impacts the
performance of concurrent user and system activity.
We propose a framework that evaluates the key premise
of adaptive indexing --- a new indexing paradigm where index creation and re-organization
take place automatically and incrementally,
as a side-effect of query execution.
We focus on how the incremental costs and benefits of dynamic
reorganization are distributed across the workload's lifetime.
We believe measuring
the costs and utility of the stages of adaptation
are relevant metrics
for evaluating new query processing paradigms
and comparing them to traditional approaches