6,764 research outputs found
Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams
Wildfires are frequent, devastating events in Australia that regularly cause
significant loss of life and widespread property damage. Fire weather indices
are a widely-adopted method for measuring fire danger and they play a
significant role in issuing bushfire warnings and in anticipating demand for
bushfire management resources. Existing systems that calculate fire weather
indices are limited due to low spatial and temporal resolution. Localized
wireless sensor networks, on the other hand, gather continuous sensor data
measuring variables such as air temperature, relative humidity, rainfall and
wind speed at high resolutions. However, using wireless sensor networks to
estimate fire weather indices is a challenge due to data quality issues, lack
of standard data formats and lack of agreement on thresholds and methods for
calculating fire weather indices. Within the scope of this paper, we propose a
standardized approach to calculating Fire Weather Indices (a.k.a. fire danger
ratings) and overcome a number of the challenges by applying Semantic Web
Technologies to the processing of data streams from a wireless sensor network
deployed in the Springbrook region of South East Queensland. This paper
describes the underlying ontologies, the semantic reasoning and the Semantic
Fire Weather Index (SFWI) system that we have developed to enable domain
experts to specify and adapt rules for calculating Fire Weather Indices. We
also describe the Web-based mapping interface that we have developed, that
enables users to improve their understanding of how fire weather indices vary
over time within a particular region.Finally, we discuss our evaluation results
that indicate that the proposed system outperforms state-of-the-art techniques
in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure
A Lightweight Distributed Solution to Content Replication in Mobile Networks
Performance and reliability of content access in mobile networks is
conditioned by the number and location of content replicas deployed at the
network nodes. Facility location theory has been the traditional, centralized
approach to study content replication: computing the number and placement of
replicas in a network can be cast as an uncapacitated facility location
problem. The endeavour of this work is to design a distributed, lightweight
solution to the above joint optimization problem, while taking into account the
network dynamics. In particular, we devise a mechanism that lets nodes share
the burden of storing and providing content, so as to achieve load balancing,
and decide whether to replicate or drop the information so as to adapt to a
dynamic content demand and time-varying topology. We evaluate our mechanism
through simulation, by exploring a wide range of settings and studying
realistic content access mechanisms that go beyond the traditional
assumptionmatching demand points to their closest content replica. Results show
that our mechanism, which uses local measurements only, is: (i) extremely
precise in approximating an optimal solution to content placement and
replication; (ii) robust against network mobility; (iii) flexible in
accommodating various content access patterns, including variation in time and
space of the content demand.Comment: 12 page
Window-based Streaming Graph Partitioning Algorithm
In the recent years, the scale of graph datasets has increased to such a
degree that a single machine is not capable of efficiently processing large
graphs. Thereby, efficient graph partitioning is necessary for those large
graph applications. Traditional graph partitioning generally loads the whole
graph data into the memory before performing partitioning; this is not only a
time consuming task but it also creates memory bottlenecks. These issues of
memory limitation and enormous time complexity can be resolved using
stream-based graph partitioning. A streaming graph partitioning algorithm reads
vertices once and assigns that vertex to a partition accordingly. This is also
called an one-pass algorithm. This paper proposes an efficient window-based
streaming graph partitioning algorithm called WStream. The WStream algorithm is
an edge-cut partitioning algorithm, which distributes a vertex among the
partitions. Our results suggest that the WStream algorithm is able to partition
large graph data efficiently while keeping the load balanced across different
partitions, and communication to a minimum. Evaluation results with real
workloads also prove the effectiveness of our proposed algorithm, and it
achieves a significant reduction in load imbalance and edge-cut with different
ranges of dataset
Circulant temporal encoding for video retrieval and temporal alignment
We address the problem of specific video event retrieval. Given a query video
of a specific event, e.g., a concert of Madonna, the goal is to retrieve other
videos of the same event that temporally overlap with the query. Our approach
encodes the frame descriptors of a video to jointly represent their appearance
and temporal order. It exploits the properties of circulant matrices to
efficiently compare the videos in the frequency domain. This offers a
significant gain in complexity and accurately localizes the matching parts of
videos. The descriptors can be compressed in the frequency domain with a
product quantizer adapted to complex numbers. In this case, video retrieval is
performed without decompressing the descriptors. We also consider the temporal
alignment of a set of videos. We exploit the matching confidence and an
estimate of the temporal offset computed for all pairs of videos by our
retrieval approach. Our robust algorithm aligns the videos on a global timeline
by maximizing the set of temporally consistent matches. The global temporal
alignment enables synchronous playback of the videos of a given scene
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Geo-Social Group Queries with Minimum Acquaintance Constraint
The prosperity of location-based social networking services enables
geo-social group queries for group-based activity planning and marketing. This
paper proposes a new family of geo-social group queries with minimum
acquaintance constraint (GSGQs), which are more appealing than existing
geo-social group queries in terms of producing a cohesive group that guarantees
the worst-case acquaintance level. GSGQs, also specified with various spatial
constraints, are more complex than conventional spatial queries; particularly,
those with a strict NN spatial constraint are proved to be NP-hard. For
efficient processing of general GSGQ queries on large location-based social
networks, we devise two social-aware index structures, namely SaR-tree and
SaR*-tree. The latter features a novel clustering technique that considers both
spatial and social factors. Based on SaR-tree and SaR*-tree, efficient
algorithms are developed to process various GSGQs. Extensive experiments on
real-world Gowalla and Dianping datasets show that our proposed methods
substantially outperform the baseline algorithms based on R-tree.Comment: This is the preprint version that is accepted by the Very Large Data
Bases Journa
A Query Integrator and Manager for the Query Web
We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions
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