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Interactive pattern search in time series
Author(s) -
Paolo Buono,
Aleks Aris,
Catherine Plaisant,
Amir Khella,
Ben Shneiderman
Publication year - 2005
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.587537
Subject(s) - computer science , series (stratigraphy) , data mining , scope (computer science) , information retrieval , time series , nearest neighbor search , similarity (geometry) , set (abstract data type) , filter (signal processing) , search engine indexing , machine learning , artificial intelligence , image (mathematics) , paleontology , computer vision , biology , programming language
The need for pattern discovery in long time series data led researchers to develop algorithms for similarity search. Most of the literature about time series focuses on algorithms that index time series and bring the data into the main storage, thus providing fast information retrieval on large time series. This paper reviews the state of the art in visualizing time series, and focuses on techniques that enable users to visually and interactively query time series. Then, it presents TimeSearcher 2, a tool that enables users to explore multidimensional data using synchronized tables and graphs with overview+detail, filter the time series data to reduce the scope of the search, select an existing pattern to find similar occurrences, and interactively adjust similarity parameters to narrow the result set. This tool is an extension of previous work, TimeSearcher 1, which uses graphical timeboxes to interactively query time series data

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