scholarly journals A Distributed Approach to Continuous Monitoring of Constrained k-Nearest Neighbor Queries in Road Networks

2012 ◽  
Vol 8 (2) ◽  
pp. 107-126 ◽  
Author(s):  
Hyung-Ju Cho ◽  
Seung-Kwon Choe ◽  
Tae-Sun Chung
Keyword(s):  
Query Processing ◽  
Continuous Monitoring ◽  
Nearest Neighbor ◽  
Road Networks ◽  
K Nearest Neighbor ◽  
Central Processing ◽  
Distributed Approach ◽  
Query Result ◽  
On The Road ◽  
Road Segments

Given two positive parameters k and r, a constrained k-nearest neighbor (CkNN) query returns the k closest objects within a network distance r of the query location in road networks. In terms of the scalability of monitoring these CkNN queries, existing solutions based on central processing at a server suffer from a sudden and sharp rise in server load as well as messaging cost as the number of queries increases. In this paper, we propose a distributed and scalable scheme called DAEMON for the continuous monitoring of CkNN queries in road networks. Our query processing is distributed among clients (query objects) and server. Specifically, the server evaluates CkNN queries issued at intersections of road segments, retrieves the objects on the road segments between neighboring intersections, and sends responses to the query objects. Finally, each client makes its own query result using this server response. As a result, our distributed scheme achieves close-to-optimal communication costs and scales well to large numbers of monitoring queries. Exhaustive experimental results demonstrate that our scheme substantially outperforms its competitor in terms of query processing time and messaging cost.

scholarly journals k-Nearest Neighbor Search based on Node Density in MANETs

2014 ◽  
Vol 10 (4) ◽  
pp. 385-405 ◽  
Author(s):  
Yuka Komai ◽  
Yuya Sasaki ◽  
Takahiro Hara ◽  
Shojiro Nishio
Keyword(s):  
Query Processing ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
K Nearest Neighbor ◽  
Node Density ◽  
Neighbor Search ◽  
Query Result ◽  
Knn Query ◽  
The One ◽  
K Nearest Neighbor Search

In a kNN query processing method, it is important to appropriately estimate the range that includes kNNs. While the range could be estimated based on the node density in the entire network, it is not always appropriate because the density of nodes in the network is not uniform. In this paper, we propose two kNN query processing methods in MANETs where the density of nodes is ununiform; the One-Hop (OH) method and the Query Log (QL) method. In the OH method, the nearest node from the point specified by the query acquires its neighbors' location and then determines the size of a circle region (the estimated kNN circle) which includes kNNs with high probability. In the QL method, a node which relays a reply of a kNN query stores the information on the query result for future queries.

scholarly journals A Safe Exit Algorithm for Continuous Nearest Neighbor Monitoring in Road Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 37-53 ◽  
Author(s):  
Hyung-Ju Cho ◽  
Se Jin Kwon ◽  
Tae-Sun Chung
Keyword(s):  
Query Processing ◽  
Nearest Neighbor ◽  
Road Networks ◽  
Experimental Results ◽  
Exit Point ◽  
Communication Costs ◽  
Query Result ◽  
Safe Region ◽  
Conventional Algorithm

Query processing in road networks has been studied extensively in recent years. However, the processing of moving queries in road networks has received little attention. In this paper, we introduce a new algorithm called the Safe Exit Algorithm (SEA), which can efficiently compute the safe exit points of a moving nearest neighbor (NN) query on road networks. The safe region of a query is an area where the query result remains unchanged, provided that the query remains inside the safe region At each safe exit point, the safe region of a query and its non-safe region meet so that a set of safe exit points represents the border of the safe region. Before reaching a safe exit point, the client (query object) does not have to request the server to re-evaluate the query This significantly reduces the server processing costs and the communication costs between the server and moving clients. Extensive experimental results show that SEA outperforms a conventional algorithm by up to two orders of magnitude in terms of communication costs and computation costs.

scholarly journals Efficient Shared Execution Processing of k-Nearest Neighbor Joins in Road Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Hyung-Ju Cho
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Real Life ◽  
Road Networks ◽  
Nearest Neighbors ◽  
Superior Performance ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Primitive Operation ◽  
Nested Loop

We investigate the k-nearest neighbor (kNN) join in road networks to determine the k-nearest neighbors (NNs) from a dataset S to every object in another dataset R. The kNN join is a primitive operation and is widely used in many data mining applications. However, it is an expensive operation because it combines the kNN query and the join operation, whereas most existing methods assume the use of the Euclidean distance metric. We alternatively consider the problem of processing kNN joins in road networks where the distance between two points is the length of the shortest path connecting them. We propose a shared execution-based approach called the group-nested loop (GNL) method that can efficiently evaluate kNN joins in road networks by exploiting grouping and shared execution. The GNL method can be easily implemented using existing kNN query algorithms. Extensive experiments using several real-life roadmaps confirm the superior performance and effectiveness of the proposed method in a wide range of problem settings.

A Safe-Region Approach to a Moving k-RNN Queries in a Directed Road Network

2017 ◽  
Vol 26 (05) ◽  
pp. 1750071 ◽  
Author(s):  
Kamil Zeberga ◽  
Rize Jin ◽  
Hyung-Ju Cho ◽  
Tae-Sun Chung
Keyword(s):  
Road Network ◽  
Nearest Neighbor ◽  
Exact Formula ◽  
Specific Situation ◽  
The Road ◽  
Query Answer ◽  
Safe Region ◽  
On The Road ◽  
Conventional Solution ◽  
Road Segments

In road networks, [Formula: see text]-range nearest neighbor ([Formula: see text]-RNN) queries locate the [Formula: see text]-closest neighbors for every point on the road segments, within a given query region defined by the user, based on the network distance. This is an important task because the user's location information may be inaccurate; furthermore, users may be unwilling to reveal their exact location for privacy reasons. Therefore, under this type of specific situation, the server returns candidate objects for every point on the road segments and the client evaluates and chooses exact [Formula: see text] nearest objects from the candidate objects. Evaluating the query results at each timestamp to keep the freshness of the query answer, while the query object is moving, will create significant computation burden for the client. We therefore propose an efficient approach called a safe-region-based approach (SRA) for computing a safe segment region and the safe exit points of a moving nearest neighbor (NN) query in a road network. SRA avoids evaluation of candidate answers returned by the location-based server since it will have high computation cost in the query side. Additionally, we applied SRA for a directed road network, where each road network has a particular orientation and the network distances are not symmetric. Our experimental results demonstrate that SRA significantly outperforms a conventional solution in terms of both computational and communication costs.

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