COLT: continuous on-line tuning
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
To tune or not to tune?: a lightweight physical design alerter
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Physical design refinement: The ‘merge-reduce’ approach
ACM Transactions on Database Systems (TODS)
Self-tuning database systems: a decade of progress
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A Benchmark for Online Index Selection
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Autonomous Management of Soft Indexes
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Self-organizing tuple reconstruction in column-stores
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Self-selecting, self-tuning, incrementally optimized indexes
Proceedings of the 13th International Conference on Extending Database Technology
Merging what's cracked, cracking what's merged: adaptive indexing in main-memory column-stores
Proceedings of the VLDB Endowment
Stochastic database cracking: towards robust adaptive indexing in main-memory column-stores
Proceedings of the VLDB Endowment
Concurrency control for adaptive indexing
Proceedings of the VLDB Endowment
Holistic indexing: offline, online and adaptive indexing in the same kernel
PhD '12 Proceedings of the on SIGMOD/PODS 2012 PhD Symposium
NoDB: efficient query execution on raw data files
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Adaptive indexing in modern database kernels
Proceedings of the 15th International Conference on Extending Database Technology
| Hi-index | 0.00 |
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.