21,548 research outputs found
Multi-route query processing and optimization
A modern query optimizer typically picks a single query plan for all data based on overall data statistics. However, many have observed that real-life datasets tend to have non-uniform distributions. Selecting a single query plan may result in ineffective query execution for possibly large portions of the actual data. In addition most stream query processing systems, given the volume of data, cannot precisely model the system state much less account for uncertainty due to continuous variations. Such systems select a single query plan based upon imprecise statistics. In this paper, we present "Query Mesh" (or QM), a practical alternative to state-of-the-art data stream processing approaches. The main idea of QM is to compute multiple routes (i.e., query plans), each designed for a particular subset of the data with distinct statistical properties. We use terms "plans" and "routes" interchangeably in our work. A classifier model is induced and used to assign the best route to process incoming tuples based upon their data characteristics. We formulate the QM search space and analyze its complexity. Due to the substantial search space, we propose several cost-based query optimization heuristics designed to effectively find nearly optimal QMs. We propose the Self-Routing Fabric (SRF) infrastructure that supports query execution with multiple plans without physically constructing their topologies nor using a central router like Eddy. We also consider how to support uncertain route specification and execution in QM which can occur when imprecise statistics lead to more than one optimal route for a subset of data. Our experimental results indicate that QM consistently provides better query execution performance and incurs negligible overhead compared to the alternative state-of-the-art data stream approaches
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
Keyword-aware Optimal Route Search
Identifying a preferable route is an important problem that finds
applications in map services. When a user plans a trip within a city, the user
may want to find "a most popular route such that it passes by shopping mall,
restaurant, and pub, and the travel time to and from his hotel is within 4
hours." However, none of the algorithms in the existing work on route planning
can be used to answer such queries. Motivated by this, we define the problem of
keyword-aware optimal route query, denoted by KOR, which is to find an optimal
route such that it covers a set of user-specified keywords, a specified budget
constraint is satisfied, and an objective score of the route is optimal. The
problem of answering KOR queries is NP-hard. We devise an approximation
algorithm OSScaling with provable approximation bounds. Based on this
algorithm, another more efficient approximation algorithm BucketBound is
proposed. We also design a greedy approximation algorithm. Results of empirical
studies show that all the proposed algorithms are capable of answering KOR
queries efficiently, while the BucketBound and Greedy algorithms run faster.
The empirical studies also offer insight into the accuracy of the proposed
algorithms.Comment: VLDB201
Reverse k Nearest Neighbor Search over Trajectories
GPS enables mobile devices to continuously provide new opportunities to
improve our daily lives. For example, the data collected in applications
created by Uber or Public Transport Authorities can be used to plan
transportation routes, estimate capacities, and proactively identify low
coverage areas. In this paper, we study a new kind of query-Reverse k Nearest
Neighbor Search over Trajectories (RkNNT), which can be used for route planning
and capacity estimation. Given a set of existing routes DR, a set of passenger
transitions DT, and a query route Q, a RkNNT query returns all transitions that
take Q as one of its k nearest travel routes. To solve the problem, we first
develop an index to handle dynamic trajectory updates, so that the most
up-to-date transition data are available for answering a RkNNT query. Then we
introduce a filter refinement framework for processing RkNNT queries using the
proposed indexes. Next, we show how to use RkNNT to solve the optimal route
planning problem MaxRkNNT (MinRkNNT), which is to search for the optimal route
from a start location to an end location that could attract the maximum (or
minimum) number of passengers based on a pre-defined travel distance threshold.
Experiments on real datasets demonstrate the efficiency and scalability of our
approaches. To the best of our best knowledge, this is the first work to study
the RkNNT problem for route planning.Comment: 12 page
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
When Hashing Met Matching: Efficient Spatio-Temporal Search for Ridesharing
Carpooling, or sharing a ride with other passengers, holds immense potential
for urban transportation. Ridesharing platforms enable such sharing of rides
using real-time data. Finding ride matches in real-time at urban scale is a
difficult combinatorial optimization task and mostly heuristic approaches are
applied. In this work, we mathematically model the problem as that of finding
near-neighbors and devise a novel efficient spatio-temporal search algorithm
based on the theory of locality sensitive hashing for Maximum Inner Product
Search (MIPS). The proposed algorithm can find near-optimal potential
matches for every ride from a pool of rides in time and space for a small . Our
algorithm can be extended in several useful and interesting ways increasing its
practical appeal. Experiments with large NY yellow taxi trip datasets show that
our algorithm consistently outperforms state-of-the-art heuristic methods
thereby proving its practical applicability
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