Spatial query processing in road networks for wireless data broadcast

Wang, Yanqiu; Xu, Chuanfei; Gu, Yu; Chen, Mo

doi:10.1007/s11276-012-0479-3

Cited by 17 publications

(5 citation statements)

References 21 publications

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“…In this paper, authors consider data retrieval scheduling over multiple channels. In [32], authors address the problem of answering KNN (K-Nearest Neighbor), range, and RNN (Reverse Nearest Neighbor) queries in road networks via broadcast channels. Then, they propose ISW (index for spatial queries in wireless broadcast environments)-index for spatial queries in wireless broadcast environments.…”

Section: Data Broadcastingmentioning

confidence: 99%

DZI: An air index for spatial queries in one-dimensional channels

Park

Joly

Valduriez

2019

Data & Knowledge Engineering

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The wireless data broadcast environment characteristics cause the data to be delivered sequentially via one-dimensional channels. A space-filling curve has been proposed for recent wireless data broadcast environments. However, air indexing introduces various problems, including the increase in the size of the index, conversion costs, and an increase in the search space because of an inefficient structure. In this paper, we propose a distribution-based Z-order air index and query processing algorithms suitable for a wireless data broadcast environment. The proposed index organizes the object identification (hereafter called ID) hierarchically only in terms of objects that are present. We compare the proposed technique with the well-known spatial indexing technique DSI by creating equations that represent the access time and tuning time, followed by conducting a simulation-based performance evaluation. The results from experimental show that our proposed index and algorithms support efficient query processing in both range queries and K-nearest neighbor queries.

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Section: Data Broadcastingmentioning

confidence: 99%

DZI: An air index for spatial queries in one-dimensional channels

Park

Joly

Valduriez

2019

Data & Knowledge Engineering

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“…As shown in Fig. 2, N 3 is connected to N 4 and N 15 . That is to say, in C 3,4,15 , the main node is 3 and connected minor nodes are 4 and 15.…”

Section: System Modelmentioning

confidence: 99%

“…When backtracking occurs, query processing time increases, thereby decreasing users' satisfaction as well as increasing mobile devices' battery consumption. Recently, studies on spatial query processing in the road network environment over a wireless broadcasting environment have been proposed [15]. Index for spatial queries in wireless broadcast environment (ISW), proposed in Ref.…”

Section: Road Network Based Spatial Query Processingmentioning

confidence: 99%

“…Index for spatial queries in wireless broadcast environment (ISW), proposed in Ref. [15], is a technique that supports snapshot spatial query processing effectively based on the road network over a wireless broadcasting environment; it can support range query, kNN query, and reverse nearest neighbor (RNN) query processing. However, since a node constructed a tree using a method of MBR construction, the size of MBR increased as the number of nodes increased, thereby increasing the query processing time.…”

Section: Road Network Based Spatial Query Processingmentioning

confidence: 99%

“…A client can receive a query result by tuning to broadcasting related to query-related data selectively among the entire broadcasting cycle that a server transmits. Despite such advantages of wireless broadcasting, few studies have been done on spatial query processing based on the road network in a wireless broadcasting environment [15]. Therefore, in this work, we propose a novel technique called the bitmap lattice index (BLI), which supports spatial queries in the road network over a wireless broadcasting environment.…”

Section: Introductionmentioning

confidence: 99%

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Bitmap lattice index in road networks

Song

Lee

Park

2014

J. Cent. South Univ.

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A novel technique called the bitmap lattice index (BLI) is proposed, which combines the advantages of a wireless broadcasting environment with a road network. Existing road networks are based on the on-demand method: a server's workload increases as the query request increases when a server sends a client information. To solve this problem, we propose the BLI. The BLI denotes an object and a node as 0 and 1 in the Hilbert curve (HC) map. The BLI can identify the position of a node and an object through bit information; it can also reduce the broadcasting frequency of a server by reducing the size of the index, thereby decreasing the access latency and query processing times. Moreover, the BLI is highly effective for data filtering, as it can identify the positions of both an object and a node. In a road network, if filtering is done via the Euclidean distance, it may result in an error. To prevent this, we add another validation procedure. The experiment is conducted by applying the BLI to kNN query, and the technique is assessed by a performance evaluation experiment.

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