TY - GEN
T1 - ShapeSearch
T2 - 2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020
AU - Siddiqui, Tarique
AU - Luh, Paul
AU - Wang, Zesheng
AU - Karahalios, Karrie
AU - Parameswaran, Aditya
N1 - Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/6/14
Y1 - 2020/6/14
N2 - Identifying trendline visualizations with desired patterns is a common task during data exploration. Existing visual analytics tools offer limited flexibility, expressiveness, and scalability for such tasks, especially when the pattern of interest is under-specified and approximate. 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 wide variety of shape search queries, and design a natural- language and regex-based parser to translate user queries to the algebraic representation. To execute these queries within interactive response times, ShapeSearch uses a fast shape algebra execution engine with query-aware optimizations, and perceptually-aware scoring methodologies. We present a thorough evaluation of the system, including a user study, a case study involving genomics data analysis, as well as performance experiments, comparing against state-of-the-art trendline shape matching approaches-that together demonstrate the usability and scalability of ShapeSearch.
AB - Identifying trendline visualizations with desired patterns is a common task during data exploration. Existing visual analytics tools offer limited flexibility, expressiveness, and scalability for such tasks, especially when the pattern of interest is under-specified and approximate. 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 wide variety of shape search queries, and design a natural- language and regex-based parser to translate user queries to the algebraic representation. To execute these queries within interactive response times, ShapeSearch uses a fast shape algebra execution engine with query-aware optimizations, and perceptually-aware scoring methodologies. We present a thorough evaluation of the system, including a user study, a case study involving genomics data analysis, as well as performance experiments, comparing against state-of-the-art trendline shape matching approaches-that together demonstrate the usability and scalability of ShapeSearch.
KW - natural language
KW - pattern querying
KW - query processing
KW - regular expression
KW - shape algebra
KW - time series
UR - http://www.scopus.com/inward/record.url?scp=85086240189&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086240189&partnerID=8YFLogxK
U2 - 10.1145/3318464.3389722
DO - 10.1145/3318464.3389722
M3 - Conference contribution
AN - SCOPUS:85086240189
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 51
EP - 65
BT - SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
PB - Association for Computing Machinery
Y2 - 14 June 2020 through 19 June 2020
ER -