48,196 research outputs found
vSPARQL: A View Definition Language for the Semantic Web
Translational medicine applications would like to leverage the biological and biomedical ontologies, vocabularies, and data sets available on the semantic web. We present a general solution for RDF information set reuse inspired by database views. Our view definition language, vSPARQL, allows applications to specify the exact content that they are interested in and how that content should be restructured or modified. Applications can access relevant content by querying against these view definitions. We evaluate the expressivity of our approach by defining views for practical use cases and comparing our view definition language to existing query languages
A Theory of Formal Synthesis via Inductive Learning
Formal synthesis is the process of generating a program satisfying a
high-level formal specification. In recent times, effective formal synthesis
methods have been proposed based on the use of inductive learning. We refer to
this class of methods that learn programs from examples as formal inductive
synthesis. In this paper, we present a theoretical framework for formal
inductive synthesis. We discuss how formal inductive synthesis differs from
traditional machine learning. We then describe oracle-guided inductive
synthesis (OGIS), a framework that captures a family of synthesizers that
operate by iteratively querying an oracle. An instance of OGIS that has had
much practical impact is counterexample-guided inductive synthesis (CEGIS). We
present a theoretical characterization of CEGIS for learning any program that
computes a recursive language. In particular, we analyze the relative power of
CEGIS variants where the types of counterexamples generated by the oracle
varies. We also consider the impact of bounded versus unbounded memory
available to the learning algorithm. In the special case where the universe of
candidate programs is finite, we relate the speed of convergence to the notion
of teaching dimension studied in machine learning theory. Altogether, the
results of the paper take a first step towards a theoretical foundation for the
emerging field of formal inductive synthesis
First-Class Functions for First-Order Database Engines
We describe Query Defunctionalization which enables off-the-shelf first-order
database engines to process queries over first-class functions. Support for
first-class functions is characterized by the ability to treat functions like
regular data items that can be constructed at query runtime, passed to or
returned from other (higher-order) functions, assigned to variables, and stored
in persistent data structures. Query defunctionalization is a non-invasive
approach that transforms such function-centric queries into the data-centric
operations implemented by common query processors. Experiments with XQuery and
PL/SQL database systems demonstrate that first-order database engines can
faithfully and efficiently support the expressive "functions as data" paradigm.Comment: Proceedings of the 14th International Symposium on Database
Programming Languages (DBPL 2013), August 30, 2013, Riva del Garda, Trento,
Ital
PQL: A Declarative Query Language over Dynamic Biological Schemata
We introduce the PQL query language (PQL) used in the GeneSeek genetic data integration project. PQL incorporates many features of query languages for semi-structured data. To this we add the ability to express metadata constraints like intended semantics and database curation approach. These constraints guide the dynamic generation of potential query plans. This allows a single query to remain relevant even in the presence of source and mediated schemas that are continually evolving, as is often the case in data integration
Learning cover context-free grammars from structural data
We consider the problem of learning an unknown context-free grammar when the
only knowledge available and of interest to the learner is about its structural
descriptions with depth at most The goal is to learn a cover
context-free grammar (CCFG) with respect to , that is, a CFG whose
structural descriptions with depth at most agree with those of the
unknown CFG. We propose an algorithm, called , that efficiently learns
a CCFG using two types of queries: structural equivalence and structural
membership. We show that runs in time polynomial in the number of
states of a minimal deterministic finite cover tree automaton (DCTA) with
respect to . This number is often much smaller than the number of states
of a minimum deterministic finite tree automaton for the structural
descriptions of the unknown grammar
Logic Programming Applications: What Are the Abstractions and Implementations?
This article presents an overview of applications of logic programming,
classifying them based on the abstractions and implementations of logic
languages that support the applications. The three key abstractions are join,
recursion, and constraint. Their essential implementations are for-loops, fixed
points, and backtracking, respectively. The corresponding kinds of applications
are database queries, inductive analysis, and combinatorial search,
respectively. We also discuss language extensions and programming paradigms,
summarize example application problems by application areas, and touch on
example systems that support variants of the abstractions with different
implementations
gMark: Schema-Driven Generation of Graphs and Queries
Massive graph data sets are pervasive in contemporary application domains.
Hence, graph database systems are becoming increasingly important. In the
experimental study of these systems, it is vital that the research community
has shared solutions for the generation of database instances and query
workloads having predictable and controllable properties. In this paper, we
present the design and engineering principles of gMark, a domain- and query
language-independent graph instance and query workload generator. A core
contribution of gMark is its ability to target and control the diversity of
properties of both the generated instances and the generated workloads coupled
to these instances. Further novelties include support for regular path queries,
a fundamental graph query paradigm, and schema-driven selectivity estimation of
queries, a key feature in controlling workload chokepoints. We illustrate the
flexibility and practical usability of gMark by showcasing the framework's
capabilities in generating high quality graphs and workloads, and its ability
to encode user-defined schemas across a variety of application domains.Comment: Accepted in November 2016. URL:
http://ieeexplore.ieee.org/document/7762945/. in IEEE Transactions on
Knowledge and Data Engineering 201
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