2,058 research outputs found
Fast and Simple Relational Processing of Uncertain Data
This paper introduces U-relations, a succinct and purely relational
representation system for uncertain databases. U-relations support
attribute-level uncertainty using vertical partitioning. If we consider
positive relational algebra extended by an operation for computing possible
answers, a query on the logical level can be translated into, and evaluated as,
a single relational algebra query on the U-relation representation. The
translation scheme essentially preserves the size of the query in terms of
number of operations and, in particular, number of joins. Standard techniques
employed in off-the-shelf relational database management systems are effective
for optimizing and processing queries on U-relations. In our experiments we
show that query evaluation on U-relations scales to large amounts of data with
high degrees of uncertainty.Comment: 12 pages, 14 figure
Schema Independent Relational Learning
Learning novel concepts and relations from relational databases is an
important problem with many applications in database systems and machine
learning. Relational learning algorithms learn the definition of a new relation
in terms of existing relations in the database. Nevertheless, the same data set
may be represented under different schemas for various reasons, such as
efficiency, data quality, and usability. Unfortunately, the output of current
relational learning algorithms tends to vary quite substantially over the
choice of schema, both in terms of learning accuracy and efficiency. This
variation complicates their off-the-shelf application. In this paper, we
introduce and formalize the property of schema independence of relational
learning algorithms, and study both the theoretical and empirical dependence of
existing algorithms on the common class of (de) composition schema
transformations. We study both sample-based learning algorithms, which learn
from sets of labeled examples, and query-based algorithms, which learn by
asking queries to an oracle. We prove that current relational learning
algorithms are generally not schema independent. For query-based learning
algorithms we show that the (de) composition transformations influence their
query complexity. We propose Castor, a sample-based relational learning
algorithm that achieves schema independence by leveraging data dependencies. We
support the theoretical results with an empirical study that demonstrates the
schema dependence/independence of several algorithms on existing benchmark and
real-world datasets under (de) compositions
A Rule-Based Approach to Analyzing Database Schema Objects with Datalog
Database schema elements such as tables, views, triggers and functions are
typically defined with many interrelationships. In order to support database
users in understanding a given schema, a rule-based approach for analyzing the
respective dependencies is proposed using Datalog expressions. We show that
many interesting properties of schema elements can be systematically determined
this way. The expressiveness of the proposed analysis is exemplarily shown with
the problem of computing induced functional dependencies for derived relations.
The propagation of functional dependencies plays an important role in data
integration and query optimization but represents an undecidable problem in
general. And yet, our rule-based analysis covers all relational operators as
well as linear recursive expressions in a systematic way showing the depth of
analysis possible by our proposal. The analysis of functional dependencies is
well-integrated in a uniform approach to analyzing dependencies between schema
elements in general.Comment: Pre-proceedings paper presented at the 27th International Symposium
on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur,
Belgium, 10-12 October 2017 (arXiv:1708.07854
On Defining SPARQL with Boolean Tensor Algebra
The Resource Description Framework (RDF) represents information as
subject-predicate-object triples. These triples are commonly interpreted as a
directed labelled graph. We propose an alternative approach, interpreting the
data as a 3-way Boolean tensor. We show how SPARQL queries - the standard
queries for RDF - can be expressed as elementary operations in Boolean algebra,
giving us a complete re-interpretation of RDF and SPARQL. We show how the
Boolean tensor interpretation allows for new optimizations and analyses of the
complexity of SPARQL queries. For example, estimating the size of the results
for different join queries becomes much simpler
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