31,393 research outputs found
On trip planning queries in spatial databases
In this paper we discuss a new type of query in Spatial Databases, called Trip Planning Query (TPQ). Given a set of points P in space, where each point belongs to a category, and given two points s and e, TPQ asks for the best trip that starts at s, passes through exactly one point from each category, and ends at e. An example of a TPQ is when a user wants to visit a set of different places and at the same time minimize the total travelling cost, e.g. what is the shortest travelling plan for me to visit an automobile shop, a CVS pharmacy outlet, and a Best Buy shop along my trip from A to B? The trip planning query is an extension of the well-known TSP problem and therefore is NP-hard. The difficulty of this query lies in the existence of multiple choices for each category. In this paper, we first study fast approximation algorithms for the trip planning query in a metric space, assuming that the data set fits in main memory, and give the theory analysis of their approximation bounds. Then, the trip planning query is examined for data sets that do not fit in main memory and must be stored on disk. For the disk-resident data, we consider two cases. In one case, we assume that the points are located in Euclidean space and indexed with an Rtree. In the other case, we consider the problem of points that lie on the edges of a spatial network (e.g. road network) and the distance between two points is defined using the shortest distance over the network. Finally, we give an experimental evaluation of the proposed algorithms using synthetic data sets generated on real road networks
On trip planning queries in spatial databases
In this paper we discuss a new type of query in Spatial Databases, called Trip Planning Query (TPQ). Given a set of points P in space, where each point belongs to a category, and given two points s and e, TPQ asks for the best trip that starts at s, passes through exactly one point from each category, and ends at e. An example of a TPQ is when a user wants to visit a set of different places and at the same time minimize the total travelling cost, e.g. what is the shortest travelling plan for me to visit an automobile shop, a CVS pharmacy outlet, and a Best Buy shop along my trip from A to B? The trip planning query is an extension of the well-known TSP problem and therefore is NP-hard. The difficulty of this query lies in the existence of multiple choices for each category. In this paper, we first study fast approximation algorithms for the trip planning query in a metric space, assuming that the data set fits in main memory, and give the theory analysis of their approximation bounds. Then, the trip planning query is examined for data sets that do not fit in main memory and must be stored on disk. For the disk-resident data, we consider two cases. In one case, we assume that the points are located in Euclidean space and indexed with an Rtree. In the other case, we consider the problem of points that lie on the edges of a spatial network (e.g. road network) and the distance between two points is defined using the shortest distance over the network. Finally, we give an experimental evaluation of the proposed algorithms using synthetic data sets generated on real road networks
Optimal Time-dependent Sequenced Route Queries in Road Networks
In this paper we present an algorithm for optimal processing of
time-dependent sequenced route queries in road networks, i.e., given a road
network where the travel time over an edge is time-dependent and a given
ordered list of categories of interest, we find the fastest route between an
origin and destination that passes through a sequence of points of interest
belonging to each of the specified categories of interest. For instance,
considering a city road network at a given departure time, one can find the
fastest route between one's work and his/her home, passing through a bank, a
supermarket and a restaurant, in this order. The main contribution of our work
is the consideration of the time dependency of the network, a realistic
characteristic of urban road networks, which has not been considered previously
when addressing the optimal sequenced route query. Our approach uses the A*
search paradigm that is equipped with an admissible heuristic function, thus
guaranteed to yield the optimal solution, along with a pruning scheme for
further reducing the search space. In order to compare our proposal we extended
a previously proposed solution aimed at non-time dependent sequenced route
queries, enabling it to deal with the time-dependency. Our experiments using
real and synthetic data sets have shown our proposed solution to be up to two
orders of magnitude faster than the temporally extended previous solution.Comment: 10 pages, 12 figures To be published as a short paper in the 23rd ACM
SIGSPATIA
Particular object retrieval with integral max-pooling of CNN activations
Recently, image representation built upon Convolutional Neural Network (CNN)
has been shown to provide effective descriptors for image search, outperforming
pre-CNN features as short-vector representations. Yet such models are not
compatible with geometry-aware re-ranking methods and still outperformed, on
some particular object retrieval benchmarks, by traditional image search
systems relying on precise descriptor matching, geometric re-ranking, or query
expansion. This work revisits both retrieval stages, namely initial search and
re-ranking, by employing the same primitive information derived from the CNN.
We build compact feature vectors that encode several image regions without the
need to feed multiple inputs to the network. Furthermore, we extend integral
images to handle max-pooling on convolutional layer activations, allowing us to
efficiently localize matching objects. The resulting bounding box is finally
used for image re-ranking. As a result, this paper significantly improves
existing CNN-based recognition pipeline: We report for the first time results
competing with traditional methods on the challenging Oxford5k and Paris6k
datasets
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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