WaZI: A Learned and Workload-aware Z-Index
Sachith Pai, Michael Mathioudakis, Yanhao Wang

TL;DR
WaZI is a workload-aware learned spatial index that optimizes storage and search structures, significantly improving range query performance while maintaining efficiency in construction and updates.
Contribution
It introduces a novel cost-based optimization for Z-indexes that incorporates workload awareness and a page-skipping mechanism to enhance spatial query efficiency.
Findings
Range query time improved by 40% on average
Outperforms or matches state-of-the-art spatial indexes
Maintains good point query performance
Abstract
Learned indexes fit machine learning (ML) models to the data and use them to make query operations more time and space-efficient. Recent works propose using learned spatial indexes to improve spatial query performance by optimizing the storage layout or internal search structures according to the data distribution. However, only a few learned indexes exploit the query workload distribution to enhance their performance. In addition, building and updating learned spatial indexes are often costly on large datasets due to the inefficiency of (re)training ML models. In this paper, we present WaZI, a learned and workload-aware variant of the Z-index, which jointly optimizes the storage layout and search structures, as a viable solution for the above challenges of spatial indexing. Specifically, we first formulate a cost function to measure the performance of a Z-index on a dataset for a…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · Time Series Analysis and Forecasting · Data Management and Algorithms
