3,018 research outputs found
Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams
Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems
(CPS) present novel challenges to Big Data platforms for performing online
analytics. Ubiquitous sensors from IoT deployments are able to generate data
streams at high velocity, that include information from a variety of domains,
and accumulate to large volumes on disk. Complex Event Processing (CEP) is
recognized as an important real-time computing paradigm for analyzing
continuous data streams. However, existing work on CEP is largely limited to
relational query processing, exposing two distinctive gaps for query
specification and execution: (1) infusing the relational query model with
higher level knowledge semantics, and (2) seamless query evaluation across
temporal spaces that span past, present and future events. These allow
accessible analytics over data streams having properties from different
disciplines, and help span the velocity (real-time) and volume (persistent)
dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP)
framework that provides domain-aware knowledge query constructs along with
temporal operators that allow end-to-end queries to span across real-time and
persistent streams. We translate this query model to efficient query execution
over online and offline data streams, proposing several optimizations to
mitigate the overheads introduced by evaluating semantic predicates and in
accessing high-volume historic data streams. The proposed X-CEP query model and
execution approaches are implemented in our prototype semantic CEP engine,
SCEPter. We validate our query model using domain-aware CEP queries from a
real-world Smart Power Grid application, and experimentally analyze the
benefits of our optimizations for executing these queries, using event streams
from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems,
October 27, 201
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
How much semantic data on small devices?
Semantic tools such as triple stores, reasoners and query en- gines tend to be designed for large-scale applications. However, with the rise of sensor networks, smart-phones and smart-appliances, new scenar- ios appear where small devices with restricted resources have to handle limited amounts of data. It is therefore important to assess how ex- isting semantic tools behave on such small devices, and how much data they can reasonably handle. There exist benchmarks for comparing triple stores and query engines, but these benchmarks are targeting large-scale applications and would not be applicable in the considered scenarios. In this paper, we describe a set of small to medium scale benchmarks explicitly targeting applications on small devices. We describe the re- sult of applying these benchmarks on three different tools (Jena, Sesame and Mulgara) on the smallest existing netbook (the Asus EEE PC 700), showing how they can be used to test and compare semantic tools in resource-limited environments
AceWiki: A Natural and Expressive Semantic Wiki
We present AceWiki, a prototype of a new kind of semantic wiki using the
controlled natural language Attempto Controlled English (ACE) for representing
its content. ACE is a subset of English with a restricted grammar and a formal
semantics. The use of ACE has two important advantages over existing semantic
wikis. First, we can improve the usability and achieve a shallow learning
curve. Second, ACE is more expressive than the formal languages of existing
semantic wikis. Our evaluation shows that people who are not familiar with the
formal foundations of the Semantic Web are able to deal with AceWiki after a
very short learning phase and without the help of an expert.Comment: To be published as: Proceedings of Semantic Web User Interaction at
CHI 2008: Exploring HCI Challenges, CEUR Workshop Proceeding
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Storing and Querying Ontologies in Logic Databases
The intersection of Description Logic inspired ontology languages with Logic Programs has been recently analyzed in [GHVD03]. The resulting language, called Description Logic Programs, covers RDF Schema and a notable portion of OWL Lite. However, the proposed mapping in [GHVD03] from the corresponding OWL fragment into Logic Programs has shown scalability as well as representational de�cits within
our experiments and analysis. In this paper we propose an alternative mapping resulting in lower computational complexity and more representational exibility. We also present benchmarking results for both mappings with ontologies of di�erent size and complexity
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
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