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