64,924 research outputs found
Distributed stream reasoning
Stream Reasoning is the combination of reasoning techniques with data streams. In this paper, we present our approach to enable rule-based reasoning on semantic data streams in a distributed manne
A Semantic Graph-Based Approach for Mining Common Topics From Multiple Asynchronous Text Streams
In the age of Web 2.0, a substantial amount of unstructured
content are distributed through multiple text streams in an
asynchronous fashion, which makes it increasingly difficult
to glean and distill useful information. An effective way to
explore the information in text streams is topic modelling,
which can further facilitate other applications such as search,
information browsing, and pattern mining. In this paper, we
propose a semantic graph based topic modelling approach
for structuring asynchronous text streams. Our model in-
tegrates topic mining and time synchronization, two core
modules for addressing the problem, into a unified model.
Specifically, for handling the lexical gap issues, we use global
semantic graphs of each timestamp for capturing the hid-
den interaction among entities from all the text streams.
For dealing with the sources asynchronism problem, local
semantic graphs are employed to discover similar topics of
different entities that can be potentially separated by time
gaps. Our experiment on two real-world datasets shows that
the proposed model significantly outperforms the existing
ones
Semantic Stability in Social Tagging Streams
One potential disadvantage of social tagging systems is that due to the lack
of a centralized vocabulary, a crowd of users may never manage to reach a
consensus on the description of resources (e.g., books, users or songs) on the
Web. Yet, previous research has provided interesting evidence that the tag
distributions of resources may become semantically stable over time as more and
more users tag them. At the same time, previous work has raised an array of new
questions such as: (i) How can we assess the semantic stability of social
tagging systems in a robust and methodical way? (ii) Does semantic
stabilization of tags vary across different social tagging systems and
ultimately, (iii) what are the factors that can explain semantic stabilization
in such systems? In this work we tackle these questions by (i) presenting a
novel and robust method which overcomes a number of limitations in existing
methods, (ii) empirically investigating semantic stabilization processes in a
wide range of social tagging systems with distinct domains and properties and
(iii) detecting potential causes for semantic stabilization, specifically
imitation behavior, shared background knowledge and intrinsic properties of
natural language. Our results show that tagging streams which are generated by
a combination of imitation dynamics and shared background knowledge exhibit
faster and higher semantic stability than tagging streams which are generated
via imitation dynamics or natural language streams alone
Learning from Ontology Streams with Semantic Concept Drift
Data stream learning has been largely studied for extracting knowledge
structures from continuous and rapid data records. In the semantic Web, data is
interpreted in ontologies and its ordered sequence is represented as an
ontology stream. Our work exploits the semantics of such streams to tackle the
problem of concept drift i.e., unexpected changes in data distribution, causing
most of models to be less accurate as time passes. To this end we revisited (i)
semantic inference in the context of supervised stream learning, and (ii)
models with semantic embeddings. The experiments show accurate prediction with
data from Dublin and Beijing
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
A Linked-Data Model for Semantic Sensor Streams
This paper describes a semantic modelling scheme, a naming convention and a data distribution mechanism for sensor streams. The proposed solutions address important challenges to deal with large-scale sensor data emerging from the Internet of Things resources. While there are significant numbers of recent work on semantic sensor networks, semantic annotation and representation frameworks, there has been less focus on creating efficient and flexible schemes to describe the sensor streams and the observation and measurement data provided via these streams and to name and resolve the requests to these data. We present our semantic model to describe the sensor streams, demonstrate an annotation and data distribution framework and evaluate our solutions with a set of sample datasets. The results show that our proposed solutions can scale for large number of sensor streams with different types of data and various attributes
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