3,823 research outputs found
Towards an Efficient Evaluation of General Queries
Database applications often require to
evaluate queries containing quantifiers or disjunctions,
e.g., for handling general integrity constraints. Existing
efficient methods for processing quantifiers depart from the
relational model as they rely on non-algebraic procedures.
Looking at quantified query evaluation from a new angle,
we propose an approach to process quantifiers that makes
use of relational algebra operators only. Our approach
performs in two phases. The first phase normalizes the
queries producing a canonical form. This form permits to
improve the translation into relational algebra performed
during the second phase. The improved translation relies
on a new operator - the complement-join - that generalizes
the set difference, on algebraic expressions of universal
quantifiers that avoid the expensive division operator in
many cases, and on a special processing of disjunctions by
means of constrained outer-joins. Our method achieves an
efficiency at least comparable with that of previous
proposals, better in most cases. Furthermore, it is considerably
simpler to implement as it completely relies on
relational data structures and operators
A Tutorial on Visual Representations of Relational Queries
Query formulation is increasingly performed by systems that need to guess a
user's intent (e.g. via spoken word interfaces). But how can a user know that
the computational agent is returning answers to the "right" query? More
generally, given that relational queries can become pretty complicated, how can
we help users understand existing relational queries, whether human-generated
or automatically generated? Now seems the right moment to revisit a topic that
predates the birth of the relational model: developing visual metaphors that
help users understand relational queries.
This lecture-style tutorial surveys the key visual metaphors developed for
visual representations of relational expressions. We will survey the history
and state-of-the art of relationally-complete diagrammatic representations of
relational queries, discuss the key visual metaphors developed in over a
century of investigating diagrammatic languages, and organize the landscape by
mapping their used visual alphabets to the syntax and semantics of Relational
Algebra (RA) and Relational Calculus (RC).Comment: 4 page tutorial paper at VLDB 2023, tutorial web page with slides to
be posted in time:
https://northeastern-datalab.github.io/visual-query-representation-tutorial/.
arXiv admin note: text overlap with arXiv:2208.0161
Functional Collection Programming with Semi-Ring Dictionaries
This paper introduces semi-ring dictionaries, a powerful class of
compositional and purely functional collections that subsume other collection
types such as sets, multisets, arrays, vectors, and matrices. We developed
SDQL, a statically typed language that can express relational algebra with
aggregations, linear algebra, and functional collections over data such as
relations and matrices using semi-ring dictionaries. Furthermore, thanks to the
algebraic structure behind these dictionaries, SDQL unifies a wide range of
optimizations commonly used in databases (DB) and linear algebra (LA). As a
result, SDQL enables efficient processing of hybrid DB and LA workloads, by
putting together optimizations that are otherwise confined to either DB systems
or LA frameworks. We show experimentally that a handful of DB and LA workloads
can take advantage of the SDQL language and optimizations. Overall, we observe
that SDQL achieves competitive performance relative to Typer and Tectorwise,
which are state-of-the-art in-memory DB systems for (flat, not nested)
relational data, and achieves an average 2x speedup over SciPy for LA
workloads. For hybrid workloads involving LA processing, SDQL achieves up to
one order of magnitude speedup over Trance, a state-of-the-art nested
relational engine for nested biomedical data, and gives an average 40% speedup
over LMFAO, a state-of-the-art in-DB machine learning engine for two (flat)
relational real-world retail datasets
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