Year: 2026
Type: article
Abstract: Visual exploration of time series data is essential for uncovering meaningful insights in domains such as healthcare monitoring and financial analysis, yet it remains computationally challenging due to the combinatorial explosion of potential subsequence comparisons. For long time series, an exhaustive comparison of all possible subsequence pairs becomes prohibitively expensive, limiting interactive exploration. This paper presents the TiVEx (Time Series Visual Exploration) family of algorithms for efficiently discovering the top-k most dissimilar subsequence pairs in comparative time series analysis. TiVEx achieves scalability through three complementary strategies: TiVEx-sharing exploits computational reuse across overlapping subsequence windows, eliminating redundant distance calculations; TiVEx-pruning employs distance-based upper bounds to eliminate unpromising candidates without exhaustive evaluation; and TiVEx-hybrid integrates both mechanisms to maximize efficiency gains. The key observation is that overlapping subsequences share a substantial computational structure, which can be systematically exploited while maintaining result optimality through provably correct pruning bounds. Extensive experiments on six diverse datasets demonstrate that TiVEx-hybrid achieves up to 84% reduction in distance calculations compared to exhaustive search while producing identical top-k results. Compared to state-of-the-art subsequence comparison methods, TiVEx-hybrid achieves 2.3× improvement in computational efficiency. Our effectiveness analysis confirms that TiVEx achieves result quality within 5% of exhaustive search even when exploring only a subset of candidate positions, enabling scalable visual exploration without compromising insight quality.
Source: Big Data and Cognitive Computing
DOI: 10.3390/bdcc10020064
Authors: Heba Helal, Mohamed A. Sharaf
Institutions: Mansoura University, United Arab Emirates University
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W7128993231
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