  The SQL group-by operator plays an important role in summarizing and
aggregating large datasets in a data analytic stack.While the standard group-by
operator, which is based on equality, is useful in several applications,
allowing similarity aware grouping provides a more realistic view on real-world
data that could lead to better insights. The Similarity SQL-based Group-By
operator (SGB, for short) extends the semantics of the standard SQL Group-by by
grouping data with similar but not necessarily equal values. While existing
similarity-based grouping operators efficiently materialize this approximate
semantics, they primarily focus on one-dimensional attributes and treat
multidimensional attributes independently. However, correlated attributes, such
as in spatial data, are processed independently, and hence, groups in the
multidimensional space are not detected properly. To address this problem, we
introduce two new SGB operators for multidimensional data. The first operator
is the clique (or distance-to-all) SGB, where all the tuples in a group are
within some distance from each other. The second operator is the
distance-to-any SGB, where a tuple belongs to a group if the tuple is within
some distance from any other tuple in the group. We implement and test the new
SGB operators and their algorithms inside PostgreSQL. The overhead introduced
by these operators proves to be minimal and the execution times are comparable
to those of the standard Group-by. The experimental study, based on TPC-H and a
social check-in data, demonstrates that the proposed algorithms can achieve up
to three orders of magnitude enhancement in performance over baseline methods
developed to solve the same problem.
