Partial Adaptive Indexing for Approximate Query Answering
<|reference_start|>Partial Adaptive Indexing for Approximate Query Answering: 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.<|reference_end|>
@article{maroulis2024partial,
    title={Partial Adaptive Indexing for Approximate Query Answering},
    author={Stavros Maroulis, Nikos Bikakis, Vassilis Stamatopoulos, and George
  Papastefanatos},
    journal={arXiv preprint arXiv:2407.18702},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18702},
    primaryClass={cs.DB}
}