3,408 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
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
Real-time analytics that requires integration and aggregation of
heterogeneous and distributed streaming and static data is a typical task in
many industrial scenarios such as diagnostics of turbines in Siemens. OBDA
approach has a great potential to facilitate such tasks; however, it has a
number of limitations in dealing with analytics that restrict its use in
important industrial applications. Based on our experience with Siemens, we
argue that in order to overcome those limitations OBDA should be extended and
become analytics, source, and cost aware. In this work we propose such an
extension. In particular, we propose an ontology, mapping, and query language
for OBDA, where aggregate and other analytical functions are first class
citizens. Moreover, we develop query optimisation techniques that allow to
efficiently process analytical tasks over static and streaming data. We
implement our approach in a system and evaluate our system with Siemens turbine
data
Old Techniques for New Join Algorithms: A Case Study in RDF Processing
Recently there has been significant interest around designing specialized RDF
engines, as traditional query processing mechanisms incur orders of magnitude
performance gaps on many RDF workloads. At the same time researchers have
released new worst-case optimal join algorithms which can be asymptotically
better than the join algorithms in traditional engines. In this paper we apply
worst-case optimal join algorithms to a standard RDF workload, the LUBM
benchmark, for the first time. We do so using two worst-case optimal engines:
(1) LogicBlox, a commercial database engine, and (2) EmptyHeaded, our prototype
research engine with enhanced worst-case optimal join algorithms. We show that
without any added optimizations both LogicBlox and EmptyHeaded outperform two
state-of-the-art specialized RDF engines, RDF-3X and TripleBit, by up to 6x on
cyclic join queries-the queries where traditional optimizers are suboptimal. On
the remaining, less complex queries in the LUBM benchmark, we show that three
classic query optimization techniques enable EmptyHeaded to compete with RDF
engines, even when there is no asymptotic advantage to the worst-case optimal
approach. We validate that our design has merit as EmptyHeaded outperforms
MonetDB by three orders of magnitude and LogicBlox by two orders of magnitude,
while remaining within an order of magnitude of RDF-3X and TripleBit
Development of Use Cases, Part I
For determining requirements and constructs appropriate for a Web query language, or in fact
any language, use cases are of essence. The W3C has published two sets of use cases for XML
and RDF query languages. In this article, solutions for these use cases are presented using
Xcerpt. a novel Web and Semantic Web query language that combines access to standard Web
data such as XML documents with access to Semantic Web metadata
such as RDF resource
descriptions with reasoning abilities and rules familiar from logicprogramming.
To the
best knowledge of the authors, this is the first in depth study of how to solve use cases for
accessing XML and RDF in a single language: Integrated access to data and metadata
has been
recognized by industry and academia as one of the key challenges in data processing for the
next decade. This article is a contribution towards addressing this challenge by demonstrating
along practical and recognized use cases the usefulness of reasoning abilities, rules, and
semistructured
query languages for accessing both data (XML) and metadata
(RDF)
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Semantic Modeling of Analytic-based Relationships with Direct Qualification
Successfully modeling state and analytics-based semantic relationships of
documents enhances representation, importance, relevancy, provenience, and
priority of the document. These attributes are the core elements that form the
machine-based knowledge representation for documents. However, modeling
document relationships that can change over time can be inelegant, limited,
complex or overly burdensome for semantic technologies. In this paper, we
present Direct Qualification (DQ), an approach for modeling any semantically
referenced document, concept, or named graph with results from associated
applied analytics. The proposed approach supplements the traditional
subject-object relationships by providing a third leg to the relationship; the
qualification of how and why the relationship exists. To illustrate, we show a
prototype of an event-based system with a realistic use case for applying DQ to
relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).Comment: Proceedings of the 2015 IEEE 9th International Conference on Semantic
Computing (IEEE ICSC 2015
Twelve Theses on Reactive Rules for the Web
Reactivity, the ability to detect and react to events, is an
essential functionality in many information systems. In particular, Web
systems such as online marketplaces, adaptive (e.g., recommender) systems,
and Web services, react to events such as Web page updates or
data posted to a server.
This article investigates issues of relevance in designing high-level programming
languages dedicated to reactivity on the Web. It presents
twelve theses on features desirable for a language of reactive rules tuned
to programming Web and Semantic Web applications
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