6,397 research outputs found
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
Organic Design of Massively Distributed Systems: A Complex Networks Perspective
The vision of Organic Computing addresses challenges that arise in the design
of future information systems that are comprised of numerous, heterogeneous,
resource-constrained and error-prone components or devices. Here, the notion
organic particularly highlights the idea that, in order to be manageable, such
systems should exhibit self-organization, self-adaptation and self-healing
characteristics similar to those of biological systems. In recent years, the
principles underlying many of the interesting characteristics of natural
systems have been investigated from the perspective of complex systems science,
particularly using the conceptual framework of statistical physics and
statistical mechanics. In this article, we review some of the interesting
relations between statistical physics and networked systems and discuss
applications in the engineering of organic networked computing systems with
predictable, quantifiable and controllable self-* properties.Comment: 17 pages, 14 figures, preprint of submission to Informatik-Spektrum
published by Springe
Partitioning Complex Networks via Size-constrained Clustering
The most commonly used method to tackle the graph partitioning problem in
practice is the multilevel approach. During a coarsening phase, a multilevel
graph partitioning algorithm reduces the graph size by iteratively contracting
nodes and edges until the graph is small enough to be partitioned by some other
algorithm. A partition of the input graph is then constructed by successively
transferring the solution to the next finer graph and applying a local search
algorithm to improve the current solution.
In this paper, we describe a novel approach to partition graphs effectively
especially if the networks have a highly irregular structure. More precisely,
our algorithm provides graph coarsening by iteratively contracting
size-constrained clusterings that are computed using a label propagation
algorithm. The same algorithm that provides the size-constrained clusterings
can also be used during uncoarsening as a fast and simple local search
algorithm.
Depending on the algorithm's configuration, we are able to compute partitions
of very high quality outperforming all competitors, or partitions that are
comparable to the best competitor in terms of quality, hMetis, while being
nearly an order of magnitude faster on average. The fastest configuration
partitions the largest graph available to us with 3.3 billion edges using a
single machine in about ten minutes while cutting less than half of the edges
than the fastest competitor, kMetis
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
Global Computing II. Terms of reference for the FP6-EU-FET call.
The European Commission has decided to continue and develop its FET “Global Computing,” and will shortly announce the opening of “Global Computing II.” The call is expected in May 2004, with application deadlines in September, and expected start date for selected projects in March 2005
Augmented Mitotic Cell Count using Field Of Interest Proposal
Histopathological prognostication of neoplasia including most tumor grading
systems are based upon a number of criteria. Probably the most important is the
number of mitotic figures which are most commonly determined as the mitotic
count (MC), i.e. number of mitotic figures within 10 consecutive high power
fields. Often the area with the highest mitotic activity is to be selected for
the MC. However, since mitotic activity is not known in advance, an arbitrary
choice of this region is considered one important cause for high variability in
the prognostication and grading.
In this work, we present an algorithmic approach that first calculates a
mitotic cell map based upon a deep convolutional network. This map is in a
second step used to construct a mitotic activity estimate. Lastly, we select
the image segment representing the size of ten high power fields with the
overall highest mitotic activity as a region proposal for an expert MC
determination. We evaluate the approach using a dataset of 32 completely
annotated whole slide images, where 22 were used for training of the network
and 10 for test. We find a correlation of r=0.936 in mitotic count estimate.Comment: 6 pages, submitted to BVM 2019 (bvm-workshop.org
Small-world networks, distributed hash tables and the e-resource discovery problem
Resource discovery is one of the most important underpinning problems behind producing a scalable,
robust and efficient global infrastructure for e-Science. A number of approaches to the resource discovery
and management problem have been made in various computational grid environments and prototypes
over the last decade. Computational resources and services in modern grid and cloud environments can be
modelled as an overlay network superposed on the physical network structure of the Internet and World
Wide Web. We discuss some of the main approaches to resource discovery in the context of the general
properties of such an overlay network. We present some performance data and predicted properties based
on algorithmic approaches such as distributed hash table resource discovery and management. We describe
a prototype system and use its model to explore some of the known key graph aspects of the global
resource overlay network - including small-world and scale-free properties
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