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Hierarchical Graph Traversal for Aggregate k Nearest Neighbors Search in Road Networks (Extended Abstract)

Tenindra Abeywickrama, Muhammad Aamir Cheema, Sabine Storandt
2021 Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence   unpublished
In this paper, we study a natural extension of the kNN query for multiple agents, namely, the Aggregate k Nearest Neighbors (AkNN) query.  ...  A k nearest neighbors (kNN) query finds k closest points-of-interest (POIs) from an agent's location.  ...  An AkNN query returns k POIs with the smallest aggregate distances. Yiu et al. [Yiu et al., 2005] solve the AkNN query using a hierarchical search on the road network.  ... 
doi:10.24963/ijcai.2021/640 fatcat:ms2hrns2jja7vb2to75v2xxltq

Aggregate nearest neighbor queries in road networks

M.L. Yiu, N. Mamoulis, D. Papadias
2005 IEEE Transactions on Knowledge and Data Engineering  
Aggregate nearest neighbor queries return the object that minimizes an aggregate distance function with respect to a set of query points.  ...  We study the processing of such queries for the case where the position and accessibility of spatial objects are constrained by spatial (e.g., road) networks.  ...  CONCLUSION In this paper, we have studied the interesting problem of aggregate nearest neighbor queries in road networks.  ... 
doi:10.1109/tkde.2005.87 fatcat:434t6roxanb6da5oo3wgchlmw4

Efficient methods for finding an optimal network location for travel planning

Junkyu Lee, Seog Park
2021 Journal of Supercomputing  
In this paper, we describe an optimal network location for travel planning (ONLTP) query, a type of optimal location query.  ...  However, their queries using an exact method perform efficiently only when the users are closely distributed, not spread out in large road networks.  ...  The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.  ... 
doi:10.1007/s11227-021-03776-7 fatcat:zpzy7xe3hjgalnc4bcvxsxfdmm

Clustering objects on a spatial network

Man Lung Yiu, Nikos Mamoulis
2004 Proceedings of the 2004 ACM SIGMOD international conference on Management of data - SIGMOD '04  
We propose variants of partitioning, densitybased, and hierarchical methods. Their effectiveness and efficiency is evaluated for collections of objects which appear on real road networks.  ...  Clustering is one of the most important analysis tasks in spatial databases. We study the problem of clustering objects, which lie on edges of a large weighted spatial network.  ...  CHAMELEON [10] is a general-purpose algorithm, which transforms the problem space into a weighted k-NN graph, where each object is connected with its k nearest neighbors.  ... 
doi:10.1145/1007568.1007619 dblp:conf/sigmod/YiuM04 fatcat:2fbbbcr4nzhebosn6bz2clwdcq

Efficient reverse k-nearest neighbor estimation

Elke Achtert, Christian Böhm, Peer Kröger, Peter Kunath, Alexey Pryakhin, Matthias Renz
2007 Informatik - Forschung und Entwicklung  
The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, has received increasing attention recently.  ...  In this paper, we propose the first approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time.  ...  In addition, for each data node N, the maximum of the 1-nearest neighbor distance of the objects in N is aggregated.  ... 
doi:10.1007/s00450-007-0027-z fatcat:iiqfjtwprvcsvixyr6arpslwwm


1996 International journal on artificial intelligence tools  
This capability is important for robotic vehicles( Martian Rovers, etc.) and for planning o -road military maneuvers.  ...  This work was motivated by anticipation of the need to search a grid of a trillion points for optimum routes.  ...  Acknowledgements We thank Anne Brink for writing the initial grid-level route planner which was the basis for the design of the PIMTAS grid-level planner and we thank William Seemuller for writing software  ... 
doi:10.1142/s0218213096000146 fatcat:b75l2rwlyfhrph4vnaigiozv3a

A Hybrid Shortest Path Algorithm for Intra-Regional Queries on Hierarchical Networks [chapter]

Gutemberg Guerra-Filho, Hanan Samet
2012 Advances in Geographic Information Science  
For higher levels, the path view approach takes O(1) time and requires O(c k m) space.  ...  a hierarchical network by merging a constant number c of adjacent regions.  ...  In particular, finding nearest neighbors in a spatial network presumes that the shortest path to the neighbors have been computed already.  ... 
doi:10.1007/978-3-642-32316-4_4 fatcat:mefn2mdgove75hjw54fxqtj7ki

G-Tree: An Efficient and Scalable Index for Spatial Search on Road Networks

Ruicheng Zhong, Guoliang Li, Kian-Lee Tan, Lizhu Zhou, Zhiguo Gong
2015 IEEE Transactions on Knowledge and Data Engineering  
Three types of location-based queries on road networks, single-pair shortest path query, k nearest neighbor (kNN) query, and keyword-based kNN query, are widely used in location-based systems.  ...  The space complexity of G-tree is O(|V| log |V|) where |V| is the number of vertices in the road network.  ...  K Nearest Neighbor Queries on Road Network: [13] , [16] , [19] addressed the kNN search problem on road networks.  ... 
doi:10.1109/tkde.2015.2399306 fatcat:p3uito4dyvejxgdt4auqdx3dnm

NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space

Tianfu Wang, Chang Ren, Yun Luo, Jing Tian
2019 ISPRS International Journal of Geo-Information  
Spatial clustering algorithms in the Euclidean space are relatively mature, while those in the network space are less well researched.  ...  The NS-DBSCAN algorithm was compared with the classical hierarchical clustering algorithm and the recently proposed density-based clustering algorithm with network-constraint Delaunay triangulation (NC_DT  ...  For example, the partitioning algorithms are insufficient in clustering network events because they are very time-consuming due to repeated graph traverse and are not effective at all [49] .  ... 
doi:10.3390/ijgi8050218 fatcat:2bqulmpzfrepnp2sohy3eemqq4

Natural Variability [chapter]

2017 Encyclopedia of GIS  
In: Proceedings of the ACM SIG-MOD international conference on management of data, San Jose, pp 71-79 Nearest Neighbor Search k-NN Search in Time-Dependent Road Networks Trip Planning Queries in Road Network  ...  segment (white), and (iii) depending on the N Nearest Neighbor Queries in Network Databases, Fig. 3 Graph modeling of the road network (a) a road network, (b) the modeling graph application, additional  ...  The GIS applications also provide testbeds for benchmarking the performance of a network GIS. Research on computer networks has lots of similarities with research on network GIS.  ... 
doi:10.1007/978-3-319-17885-1_101534 fatcat:aj3jjedtq5ezbpoakmsuxw7pba

Efficient reverse k-nearest neighbor search in arbitrary metric spaces

Elke Achtert, Christian Böhm, Peer Kröger, Peter Kunath, Alexey Pryakhin, Matthias Renz
2006 Proceedings of the 2006 ACM SIGMOD international conference on Management of data - SIGMOD '06  
The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, is a generalization of the reverse 1-nearest neighbor  ...  In particular, we approximate the k-nearest neighbor distance for each data object by upper and lower bounds using two functions of only two parameters each.  ...  Acknowledgement We are grateful with Yufei Tao, Dimitris Papadias, Xiang Lian and Xiaokui Xiao providing us the source code of their Reverse kNN Search in [11] .  ... 
doi:10.1145/1142473.1142531 dblp:conf/sigmod/AchtertBKKPR06 fatcat:3qgpvzngvrbf3gxvofqb26vxge

Fast object search on road networks

Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng
2009 Proceedings of the 12th International Conference on Extending Database Technology Advances in Database Technology - EDBT '09  
By exploiting search space pruning technique and providing a dynamic object mapping mechanism, ROAD is very efficient and flexible for various types of queries, namely, range search and nearest neighbor  ...  In this paper, we present ROAD, a general framework to evaluate Location-Dependent Spatial Queries (LDSQ)s that searches for spatial objects on road networks.  ...  SEARCH ALGORITHMS While ROAD is designed to support different types of LD-SQs, in this paper, we focus on k-nearest neighbor (kNN) queries and range queries.  ... 
doi:10.1145/1516360.1516476 dblp:conf/edbt/LeeLZ09 fatcat:ibihsetr55cz3b3uptzxg3mmb4

Spatial ordering and encoding for geographic data mining and visualization

Diansheng Guo, Mark Gahegan
2006 Journal of Intelligent Information Systems  
., locations, networks, and nearest neighbors) are unique and different from other aspatial attributes (e.g., population, sales, or income).  ...  graphs.  ...  However, any opinions, findings, and conclusions or recommendations in this document are those of the authors and do not necessarily reflect views of the U.S. Department of Homeland Security.  ... 
doi:10.1007/s10844-006-9952-8 fatcat:i6ntrc63y5dd5ecdpekrm3x33y

A Survey on Trajectory Data Management, Analytics, and Learning [article]

Sheng Wang, Zhifeng Bao, J. Shane Culpepper, Gao Cong
2020 arXiv   pre-print
Recent advances in sensor and mobile devices have enabled an unprecedented increase in the availability and collection of urban trajectory data, thus increasing the demand for more efficient ways to manage  ...  We also explore four closely related analytical tasks commonly used with trajectory data in interactive or real-time processing. Deep trajectory learning is also reviewed for the first time.  ...  Query types range from basic trajectory search, top-k trajectory similarity search (spatial-only and spatial-textual), to more complex operations such as reverse k nearest neighbors search, and trajectory  ... 
arXiv:2003.11547v2 fatcat:5gf5h5skqjbrhf67cflygggnky

Memory-Efficient RkNN Retrieval by Nonlinear k-Distance Approximation [article]

Sandra Obermeier, Max Berrendorf, Peer Kröger
2020 arXiv   pre-print
The reverse k-nearest neighbor (RkNN) query is an established query type with various applications reaching from identifying highly influential objects over incrementally updating kNN graphs to optimizing  ...  In this work, we investigate this assumption and uncover that it is violated in regions of changing density, which we show are typical for real-life datasets.  ...  The underlying hash function is replaced by a hierarchy of recurrent neural network models. Dong et al. [19] propose Neural LSH for fast approximate nearest neighbor search.  ... 
arXiv:2011.01773v1 fatcat:vbgf2ksczvhxvjtdwasczhrlrq
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