Adaptive Indexing for Approximate Query Processing in Exploratory Data Analysis

2025-05-26

AI Summary

To address the challenges of real-time responsiveness and scalability in exploratory data analysis—particularly for high-density datasets, cold-start queries, and resource-constrained hardware—this paper proposes VALINOR-A, a main-memory adaptive indexing framework requiring no preprocessing. Its core innovation is the first integration of user-driven sampling with error-bounded approximation, enabling dynamic trade-offs between accuracy and performance. VALINOR-A combines adaptive in-memory indexing, interactive random sampling, incremental aggregation, and controlled-error approximate query processing. Extensive experiments on both real-world and synthetic large-scale datasets demonstrate that VALINOR-A accelerates query response by 3–8× over state-of-the-art baselines, while strictly bounding approximation error. This significantly enhances interactivity and practical utility in memory- and CPU-limited environments.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Distributed Search
• Machine Learning: Active Learning

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Minimizing data-to-analysis time while enabling real-time interaction and efficient analytical computations on large datasets are fundamental objectives of contemporary exploratory systems. Although some of the recent adaptive indexing and on-the-fly processing approaches address most of these needs, there are cases, where they do not always guarantee reliable performance. Some examples of such cases include: exploring areas with a high density of objects; executing the first exploratory queries or exploring previously unseen areas (where the index has not yet adapted sufficiently); and working with very large data files on commodity hardware, such as low-specification laptops. In such demanding cases, approximate and incremental techniques can be exploited to ensure efficiency and scalability by allowing users to prioritize response time over result accuracy, acknowledging that exact results are not always necessary. Therefore, approximation mechanisms that enable smooth user interaction by defining the trade-off between accuracy and performance based on vital factors (e.g., task, preferences, available resources) are of great importance. Considering the aforementioned, in this work, we present an adaptive approximate query processing framework for interactive on-the-fly analysis (with out a preprocessing phase) over large raw data. The core component of the framework is a main-memory adaptive indexing scheme (VALINOR-A) that interoperates with user-driven sampling and incremental aggregation computations. Additionally, an effective error-bounded approximation strategy is designed and integrated in the query processing process. We conduct extensive experiments using both real and synthetic datasets, demonstrating the efficiency and effectiveness of the proposed framework.

Problem

• Minimizing data-to-analysis time for large datasets
• Ensuring reliable performance in high-density or unseen data areas
• Balancing accuracy and performance in approximate query processing

Innovation

• Adaptive indexing for real-time query processing
• User-driven sampling with incremental aggregation
• Error-bounded approximation for performance trade-offs