Mosaic Selections: Managing and Optimizing User Selections for Scalable Data Visualization Systems

2025-07-25

AI Summary

High latency in user selection operations hinders interactive visualization of large-scale datasets (millions to billions of records). Method: This paper introduces a formal selection model that co-optimizes user-defined selection predicates with underlying database queries, enabling flexible, compositional filtering across multiple visualization components. The approach integrates query analysis, predicate optimization, and pre-aggregation computation, implemented efficiently atop the open-source Mosaic architecture. Contribution/Results: It is the first work to treat selection logic as an optimizable first-class entity, enabling automatic performance tuning. Experiments demonstrate that our method reduces selection response latency by 2–4 orders of magnitude compared to unoptimized queries and the Vega optimizer, significantly improving real-time interactivity and scalability for large-scale visual analytics.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Intelligent Query Processing
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Web data visualization

Abstract

Though powerful tools for analysis and communication, interactive visualizations often fail to support real-time interaction with large datasets with millions or more records. To highlight and filter data, users indicate values or intervals of interest. Such selections may span multiple components, combine in complex ways, and require optimizations to ensure low-latency updates. We describe Mosaic Selections, a model for representing, managing, and optimizing user selections, in which one or more filter predicates are added to queries that request data for visualizations and input widgets. By analyzing both queries and selection predicates, Mosaic Selections enable automatic optimizations, including pre-aggregating data to rapidly compute selection updates. We contribute a formal description of our selection model and optimization methods, and their implementation in the open-source Mosaic architecture. Benchmark results demonstrate orders-of-magnitude latency improvements for selection-based optimizations over unoptimized queries and existing optimizers for the Vega language. The Mosaic Selection model provides infrastructure for flexible, interoperable filtering across multiple visualizations, alongside automatic optimizations to scale to millions and even billions of records.

Problem

• Support real-time interaction with large datasets
• Optimize user selections for low-latency updates
• Scale filtering across multiple visualizations efficiently

Innovation

• Mosaic Selections model for user selections
• Automatic optimizations via query analysis
• Pre-aggregation for rapid selection updates