Methods and apparatus for parallel evaluation of pattern queries over large n-dimensional datasets to identify features of interest

Abstract

Pattern queries are evaluated in parallel over large N-dimensional datasets to identify features of interest. Similarity-based pattern matching tasks are executed over N-dimensional input datasets comprised of numeric values by providing data representations for the N-dimensional input datasets, a pattern query and one or more candidate solutions for the pattern query, such that the pattern query specifies a pattern of an N-dimensional body that is compared to at least one candidate solution corresponding to an N-dimensional body extracted from the N-dimensional input datasets; defining a distance metric that compares the N-dimensional body formed by the candidate solution extracted from the N-dimensional input datasets and the N-dimensional body formed by the pattern query, taking into account one or more of the following criteria: differences between mapped values, differences in scale and differences in shape; and executing, in parallel, a plurality of independent instances of at least one algebraic operator to generate and score the candidate solutions based on the distance metric. The exemplary algebraic operators comprise a Trace Match operator, a Ranking operator, a Candidate Solution operator, and a Query Clustering operator.