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.

Cracking a Scientific Database
TPC Technology Conference on Performance Evaluation and Benchmarking
Database Architectures

Goetz, G, Idreos, S, Kuno, H, & Manegold, S. (2010). Benchmarking adaptive indexing. In Proceedings of TPC Technology Conference on Performance Evaluation & Benchmarking 2010 (2). LNCS.