5,754 research outputs found
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Expressive Path Queries on Graph with Data
Graph data models have recently become popular owing to their applications,
e.g., in social networks and the semantic web. Typical navigational query
languages over graph databases - such as Conjunctive Regular Path Queries
(CRPQs) - cannot express relevant properties of the interaction between the
underlying data and the topology. Two languages have been recently proposed to
overcome this problem: walk logic (WL) and regular expressions with memory
(REM). In this paper, we begin by investigating fundamental properties of WL
and REM, i.e., complexity of evaluation problems and expressive power. We first
show that the data complexity of WL is nonelementary, which rules out its
practicality. On the other hand, while REM has low data complexity, we point
out that many natural data/topology properties of graphs expressible in WL
cannot be expressed in REM. To this end, we propose register logic, an
extension of REM, which we show to be able to express many natural graph
properties expressible in WL, while at the same time preserving the
elementariness of data complexity of REMs. It is also incomparable to WL in
terms of expressive power.Comment: 39 page
Multi-Paradigm Reasoning for Access to Heterogeneous GIS
Accessing and querying geographical data in a uniform way has become easier in recent years. Emerging standards like WFS turn
the web into a geospatial web services enabled place. Mediation
architectures like VirGIS overcome syntactical and semantical heterogeneity
between several distributed sources. On mobile devices,
however, this kind of solution is not suitable, due to limitations,
mostly regarding bandwidth, computation power, and available storage
space. The aim of this paper is to present a solution for providing
powerful reasoning mechanisms accessible from mobile applications
and involving data from several heterogeneous sources.
By adapting contents to time and location, mobile web information
systems can not only increase the value and suitability of the
service itself, but can substantially reduce the amount of data delivered
to users. Because many problems pertain to infrastructures
and transportation in general and to way finding in particular, one
cornerstone of the architecture is higher level reasoning on graph
networks with the Multi-Paradigm Location Language MPLL. A
mediation architecture is used as a “graph provider” in order to
transfer the load of computation to the best suited component –
graph construction and transformation for example being heavy on
resources. Reasoning in general can be conducted either near the
“source” or near the end user, depending on the specific use case.
The concepts underlying the proposal described in this paper are
illustrated by a typical and concrete scenario for web applications
Context-Free Path Queries on RDF Graphs
Navigational graph queries are an important class of queries that canextract
implicit binary relations over the nodes of input graphs. Most of the
navigational query languages used in the RDF community, e.g. property paths in
W3C SPARQL 1.1 and nested regular expressions in nSPARQL, are based on the
regular expressions. It is known that regular expressions have limited
expressivity; for instance, some natural queries, like same generation-queries,
are not expressible with regular expressions. To overcome this limitation, in
this paper, we present cfSPARQL, an extension of SPARQL query language equipped
with context-free grammars. The cfSPARQL language is strictly more expressive
than property paths and nested expressions. The additional expressivity can be
used for modelling graph similarities, graph summarization and ontology
alignment. Despite the increasing expressivity, we show that cfSPARQL still
enjoys a low computational complexity and can be evaluated efficiently.Comment: 25 page
Web and Semantic Web Query Languages
A number of techniques have been developed to facilitate
powerful data retrieval on the Web and Semantic Web. Three categories
of Web query languages can be distinguished, according to the format
of the data they can retrieve: XML, RDF and Topic Maps. This article
introduces the spectrum of languages falling into these categories
and summarises their salient aspects. The languages are introduced using
common sample data and query types. Key aspects of the query
languages considered are stressed in a conclusion
Survey over Existing Query and Transformation Languages
A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability
of many current Semantic Web approaches to cope with data available in such diverging
representation formalisms as XML, RDF, or Topic Maps. A common query language is the first
step to allow transparent access to data in any of these formats. To further the understanding
of the requirements and approaches proposed for query languages in the conventional as well
as the Semantic Web, this report surveys a large number of query languages for accessing
XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from
all these areas. From the detailed survey of these query languages, a common classification
scheme is derived that is useful for understanding and differentiating languages within and
among all three areas
Querying Best Paths in Graph Databases
Querying graph databases has recently received much attention. We propose a new approach to this problem, which balances competing goals of expressive power, language clarity and computational complexity. A distinctive feature of our approach is the ability to express properties of minimal (e.g. shortest) and maximal (e.g. most valuable) paths satisfying given criteria. To express complex properties in a modular way, we introduce labelling-generating ontologies. The resulting formalism is computationally attractive - queries can be answered in non-deterministic
logarithmic space in the size of the database
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