Partial Adaptive Indexing for Approximate Query Answering

TL;DR

This paper presents a new adaptive indexing method that supports approximate query answering by balancing index refinement and data reading costs, improving initial exploration performance on large datasets.

Contribution

It introduces a partial index adaptation approach driven by workload and accuracy constraints, utilizing hierarchical tile-based indexing for efficient approximate queries.

Findings

Improved query evaluation time during initial data exploration.

Effective balance between index refinement costs and data access.

Supports user-defined accuracy bounds for approximate answers.

Abstract

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.