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Time series snippet discovery offers powerful tools for summarizing complex temporal data via representative subsequences. While advanced methods, such as Matrix Profile-based approaches and our RS4 (Restricted Search Space for Snippet Selection), achieve strong efficiency and pattern fidelity, their adoption remains limited by usability barriers. Existing algorithms typically produce raw numerical outputs that require technical expertise to interpret, and few accessible interfaces support configuration or visualization. In this demo, we present GUISSE, an interactive graphical interface for executing and evaluating snippet discovery algorithms. The platform enables flexible experimentation with RS4 and baseline methods, allowing users to configure clustering techniques, distance weightings, and snippet parameters. By bridging algorithmic execution with interactive exploration, GUISSE empowers a broad range of users to apply, interpret, and validate snippet discovery results without programming expertise, promoting transparent and reproducible time series summarization workflows.

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#	Name
1	Guilherme Fernandes(guilherme.sales@insightlab.ufc.br)
2	Lucas Gaspar(lucasperes@insightlab.ufc.br)
3	Lívia Cruz(livia@insightlab.ufc.br)
4	Regis Magalhães(regis@insightlab.ufc.br)
5	José Macêdo(jose.macedo@insightlab.ufc.br)

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Reference

#	Reference
1	Matrix Profile XIII: Time Series Snippets: A New Primitive for Time Series Data Mining
2	Matrix profile II: Exploiting a novel algorithm and GPUs to break the one hundred million barrier for time series motifs and joins
3	RS4: Restricted Search Space for Snippet Selection
4	STUMPY: A powerful and scalable library for time series data mining in Python
5	A data mining research on office building energy pattern based on time-series energy consumption data
6	Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets
7	sktime: A unified interface for machine learning with time series
8	Data-driven approaches for meteorological time series prediction: a comparative study of the state-of-the-art computational intelligence techniques

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