Partial Adaptive Indexing for Approximate Query Answering
Stavros Maroulis, Nikos Bikakis, Vassilis Stamatopoulos, and George, Papastefanatos

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,…
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Taxonomy
TopicsData Management and Algorithms · Algorithms and Data Compression · Advanced Database Systems and Queries
