10,181 research outputs found
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
RDF Querying
Reactive Web systems, Web services, and Web-based publish/
subscribe systems communicate events as XML messages, and in
many cases require composite event detection: it is not sufficient to react
to single event messages, but events have to be considered in relation to
other events that are received over time.
Emphasizing language design and formal semantics, we describe the
rule-based query language XChangeEQ for detecting composite events.
XChangeEQ is designed to completely cover and integrate the four complementary
querying dimensions: event data, event composition, temporal
relationships, and event accumulation. Semantics are provided as
model and fixpoint theories; while this is an established approach for rule
languages, it has not been applied for event queries before
Towards a property graph generator for benchmarking
The use of synthetic graph generators is a common practice among
graph-oriented benchmark designers, as it allows obtaining graphs with the
required scale and characteristics. However, finding a graph generator that
accurately fits the needs of a given benchmark is very difficult, thus
practitioners end up creating ad-hoc ones. Such a task is usually
time-consuming, and often leads to reinventing the wheel. In this paper, we
introduce the conceptual design of DataSynth, a framework for property graphs
generation with customizable schemas and characteristics. The goal of DataSynth
is to assist benchmark designers in generating graphs efficiently and at scale,
saving from implementing their own generators. Additionally, DataSynth
introduces novel features barely explored so far, such as modeling the
correlation between properties and the structure of the graph. This is achieved
by a novel property-to-node matching algorithm for which we present preliminary
promising results
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
Storing RDF as a Graph
RDF is the first W3C standard for enriching information resources of the Web with detailed meta data. The semantics of RDF data is defined using a RDF schema. The most expressive language for querying RDF is RQL, which enables querying of semantics. In order to support RQL, a RDF storage system has to map the RDF graph model onto its storage structure. Several storage systems for RDF data have been developed, which store the RDF data as triples in a relational database. To evaluate an RQL query on those triple structures, the graph model has to be rebuilt from the triples.
In this paper, we presented a new approach to store RDF data as a graph in a object-oriented database. Our approach avoids the costly rebuilding of the graph and efficiently queries the storage structure directly. The advantages of our approach have been shown by performance test on our prototype implementation OO-Store
Generating Preview Tables for Entity Graphs
Users are tapping into massive, heterogeneous entity graphs for many
applications. It is challenging to select entity graphs for a particular need,
given abundant datasets from many sources and the oftentimes scarce information
for them. We propose methods to produce preview tables for compact presentation
of important entity types and relationships in entity graphs. The preview
tables assist users in attaining a quick and rough preview of the data. They
can be shown in a limited display space for a user to browse and explore,
before she decides to spend time and resources to fetch and investigate the
complete dataset. We formulate several optimization problems that look for
previews with the highest scores according to intuitive goodness measures,
under various constraints on preview size and distance between preview tables.
The optimization problem under distance constraint is NP-hard. We design a
dynamic-programming algorithm and an Apriori-style algorithm for finding
optimal previews. Results from experiments, comparison with related work and
user studies demonstrated the scoring measures' accuracy and the discovery
algorithms' efficiency.Comment: This is the camera-ready version of a SIGMOD16 paper. There might be
tiny differences in layout, spacing and linebreaking, compared with the
version in the SIGMOD16 proceedings, since we must submit TeX files and use
arXiv to compile the file
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