Cracking KD-Tree: The First Multidimensional Adaptive Indexing (Position Paper)

Pedro Holanda, Matheus Nerone, Eduardo C. de Almeida, Stefan Manegold

Abstract

Workload-aware physical data access structures are crucial to achieve short response time with (exploratory) data analysis tasks as commonly required for Big Data and Data Science applications. Recently proposed techniques such as automatic index advisers (for a priori known static workloads) and query-driven adaptive incremental indexing (for a priori unknown dynamic workloads) form the state-of-the-art to build single-dimensional indexes for single-attribute query predicates. However, similar techniques for more demanding multi-attribute query predicates, which are vital for any data analysis task, have not been proposed, yet. In this paper, we present our on-going work on a new set of workload-adaptive indexing techniques that focus on creating multidimensional indexes. We present our proof-of-concept, the Cracking KD-Tree, an adaptive indexing approach that generates a KD-Tree based on multidimensional range query predicates. It works by incrementally creating partial multidimensional indexes as a by-product of query processing. The indexes are produced only on those parts of the data that are accessed, and their creation cost is effectively distributed across a stream of queries. Experimental results show that the Cracking KD-Tree is three times faster than creating a full KD-Tree, one order of magnitude faster than executing full scans and two orders of magnitude faster than using uni-dimensional full or adaptive indexes on multiple columns.

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Paper Citation


in Harvard Style

Holanda P., Nerone M., Almeida E. and Manegold S. (2018). Cracking KD-Tree: The First Multidimensional Adaptive Indexing (Position Paper).In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: EDDY, ISBN 978-989-758-318-6, pages 393-399. DOI: 10.5220/0006944203930399


in Bibtex Style

@conference{eddy18,
author={Pedro Holanda and Matheus Nerone and Eduardo C. de Almeida and Stefan Manegold},
title={Cracking KD-Tree: The First Multidimensional Adaptive Indexing (Position Paper)},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: EDDY,},
year={2018},
pages={393-399},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006944203930399},
isbn={978-989-758-318-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: EDDY,
TI - Cracking KD-Tree: The First Multidimensional Adaptive Indexing (Position Paper)
SN - 978-989-758-318-6
AU - Holanda P.
AU - Nerone M.
AU - Almeida E.
AU - Manegold S.
PY - 2018
SP - 393
EP - 399
DO - 10.5220/0006944203930399