18,162 research outputs found
Physiology-Aware Rural Ambulance Routing
In emergency patient transport from rural medical facility to center tertiary
hospital, real-time monitoring of the patient in the ambulance by a physician
expert at the tertiary center is crucial. While telemetry healthcare services
using mobile networks may enable remote real-time monitoring of transported
patients, physiologic measures and tracking are at least as important and
requires the existence of high-fidelity communication coverage. However, the
wireless networks along the roads especially in rural areas can range from 4G
to low-speed 2G, some parts with communication breakage. From a patient care
perspective, transport during critical illness can make route selection patient
state dependent. Prompt decisions with the relative advantage of a longer more
secure bandwidth route versus a shorter, more rapid transport route but with
less secure bandwidth must be made. The trade-off between route selection and
the quality of wireless communication is an important optimization problem
which unfortunately has remained unaddressed by prior work.
In this paper, we propose a novel physiology-aware route scheduling approach
for emergency ambulance transport of rural patients with acute, high risk
diseases in need of continuous remote monitoring. We mathematically model the
problem into an NP-hard graph theory problem, and approximate a solution based
on a trade-off between communication coverage and shortest path. We profile
communication along two major routes in a large rural hospital settings in
Illinois, and use the traces to manifest the concept. Further, we design our
algorithms and run preliminary experiments for scalability analysis. We believe
that our scheduling techniques can become a compelling aid that enables an
always-connected remote monitoring system in emergency patient transfer
scenarios aimed to prevent morbidity and mortality with early diagnosis
treatment.Comment: 6 pages, The Fifth IEEE International Conference on Healthcare
Informatics (ICHI 2017), Park City, Utah, 201
Dynamic Time-Dependent Route Planning in Road Networks with User Preferences
There has been tremendous progress in algorithmic methods for computing
driving directions on road networks. Most of that work focuses on
time-independent route planning, where it is assumed that the cost on each arc
is constant per query. In practice, the current traffic situation significantly
influences the travel time on large parts of the road network, and it changes
over the day. One can distinguish between traffic congestion that can be
predicted using historical traffic data, and congestion due to unpredictable
events, e.g., accidents. In this work, we study the \emph{dynamic and
time-dependent} route planning problem, which takes both prediction (based on
historical data) and live traffic into account. To this end, we propose a
practical algorithm that, while robust to user preferences, is able to
integrate global changes of the time-dependent metric~(e.g., due to traffic
updates or user restrictions) faster than previous approaches, while allowing
subsequent queries that enable interactive applications
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
Parallel Processing of Large Graphs
More and more large data collections are gathered worldwide in various IT
systems. Many of them possess the networked nature and need to be processed and
analysed as graph structures. Due to their size they require very often usage
of parallel paradigm for efficient computation. Three parallel techniques have
been compared in the paper: MapReduce, its map-side join extension and Bulk
Synchronous Parallel (BSP). They are implemented for two different graph
problems: calculation of single source shortest paths (SSSP) and collective
classification of graph nodes by means of relational influence propagation
(RIP). The methods and algorithms are applied to several network datasets
differing in size and structural profile, originating from three domains:
telecommunication, multimedia and microblog. The results revealed that
iterative graph processing with the BSP implementation always and
significantly, even up to 10 times outperforms MapReduce, especially for
algorithms with many iterations and sparse communication. Also MapReduce
extension based on map-side join usually noticeably presents better efficiency,
although not as much as BSP. Nevertheless, MapReduce still remains the good
alternative for enormous networks, whose data structures do not fit in local
memories.Comment: Preprint submitted to Future Generation Computer System
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