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Identifying trendline visualizations with desired patterns is a common andfundamental data exploration task. Existing visual analytics tools offerlimited flexibility and expressiveness for such tasks, especially when thepattern of interest is under-specified and approximate, and do not scale wellwhen the pattern searching needs are ad-hoc, as is often the case. We proposeShapeSearch, an efficient and flexible pattern-searching tool, that enables thesearch for desired patterns via multiple mechanisms: sketch, natural-language,and visual regular expressions. We develop a novel shape querying algebra, witha minimal set of primitives and operators that can express a large number ofShapeSearch queries, and design a natural-language and regex-based parser toautomatically parse and translate user queries to the algebra representation.To execute these queries within interactive response times, ShapeSearch uses afast shape algebra-based execution engine with query-aware optimizations, andperceptually-aware scoring methodologies. We present a thorough evaluation ofthe system, including a general-purpose user study, a case study involvinggenomic data analysis, as well as performance experiments, comparing againststate-of-the-art time series shape matching approaches---that togetherdemonstrate the usability and scalability of ShapeSearch.

ShapeSearch: A Flexible and Efficient System for Shape-based Exploration of Trendlines