Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces

2025-08-31

AI Summary

Existing dimensionality reduction methods struggle to capture semantic evolution of data over time. This paper proposes a spatiotemporal mapping system that integrates force-directed projection with incremental streaming clustering to enable real-time topic modeling and interactive visualization for textual and image data. Its core contribution is the first synergistic integration of dynamic projection and time-aware streaming clustering, enabling low-latency, interpretable visualization of topic evolution within high-dimensional embedding spaces. The system unifies time-aware dimensionality reduction, incremental cluster updating, and real-time rendering into an end-to-end streaming visualization pipeline. Evaluated on multi-source temporal datasets from artistic creation and social media, the method accurately captures topic emergence, migration, and decay. Results demonstrate significant improvements in both efficiency and intuitiveness for exploring semantic evolutionary patterns.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Time-Series/Data Streams
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Many real-world datasets -- from an artist's body of work to a person's social media history -- exhibit meaningful semantic changes over time that are difficult to capture with existing dimensionality reduction methods. To address this gap, we introduce a visualization technique that combines force-based projection and streaming clustering methods to build a spatial-temporal map of embeddings. Applying this technique, we create Chronotome, a tool for interactively exploring evolving themes in time-based data -- in real time. We demonstrate the utility of our approach through use cases on text and image data, showing how it offers a new lens for understanding the aesthetics and semantics of temporal datasets.

Problem

• Capturing semantic changes over time in datasets
• Visualizing evolving themes in real-time streaming data
• Understanding aesthetics and semantics of temporal datasets

Innovation

• Combines force-based projection with streaming clustering
• Creates real-time spatial-temporal map of embeddings
• Enables interactive exploration of evolving themes