4,866 research outputs found
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
Efficient Spatial Keyword Search in Trajectory Databases
An increasing amount of trajectory data is being annotated with text
descriptions to better capture the semantics associated with locations. The
fusion of spatial locations and text descriptions in trajectories engenders a
new type of top- queries that take into account both aspects. Each
trajectory in consideration consists of a sequence of geo-spatial locations
associated with text descriptions. Given a user location and a
keyword set , a top- query returns trajectories whose text
descriptions cover the keywords and that have the shortest match
distance. To the best of our knowledge, previous research on querying
trajectory databases has focused on trajectory data without any text
description, and no existing work has studied such kind of top- queries on
trajectories. This paper proposes one novel method for efficiently computing
top- trajectories. The method is developed based on a new hybrid index,
cell-keyword conscious B-tree, denoted by \cellbtree, which enables us to
exploit both text relevance and location proximity to facilitate efficient and
effective query processing. The results of our extensive empirical studies with
an implementation of the proposed algorithms on BerkeleyDB demonstrate that our
proposed methods are capable of achieving excellent performance and good
scalability.Comment: 12 page
SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment
The increasing maturity of big data applications has led to a proliferation
of models targeting the same objectives within the same scenarios and datasets.
However, selecting the most suitable model that considers model's features
while taking specific requirements and constraints into account still poses a
significant challenge. Existing methods have focused on worker-task assignments
based on crowdsourcing, they neglect the scenario-dataset-model assignment
problem. To address this challenge, a new problem named the Scenario-based
Optimal Model Assignment (SOMA) problem is introduced and a novel framework
entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a
heterogeneous information framework that can integrate various types of
information to intelligently select a suitable dataset and allocate the optimal
model for a specific scenario. To comprehensively evaluate models, a new score
function that utilizes multi-head attention mechanisms is proposed. Moreover, a
novel memory mechanism named the mnemonic center is developed to store the
matched heterogeneous information and prevent duplicate matching. Six popular
traffic scenarios are selected as study cases and extensive experiments are
conducted on a dataset to verify the effectiveness and efficiency of SMAP and
the score function
Design & Evaluation of Path-based Reputation System for MANET Routing
Most of the existing reputation systems in mobile ad hoc networks (MANET) consider only node reputations when selecting routes. Reputation and trust are therefore generally ensured within a one-hop distance when routing decisions are made, which often fail to provide the most reliable, trusted route. In this report, we first summarize the background studies on the security of MANET. Then, we propose a system that is based on path reputation, which is computed from reputation and trust values of each and every node in the route. The use of path reputation greatly enhances the reliability of resulting routes. The detailed system architecture and components design of the proposed mechanism are carefully described on top of the AODV (Ad-hoc On-demand Distance Vector) routing protocol. We also evaluate the performance of the proposed system by simulating it on top of AODV. Simulation experiments show that the proposed scheme greatly improves network throughput in the midst of misbehavior nodes while requires very limited message overhead. To our knowledge, this is the first path-based reputation system proposal that may be implemented on top of a non-source based routing scheme such as AODV
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