Efficient windows query processing with expanded grid cells on wireless spatial data broadcasting for pervasive computing

2014 ◽  
Vol 7 ◽  
pp. 785-790 ◽  
Author(s):  
Seokjin Im ◽  
Kilhong Joo ◽  
Jintak Choi
Keyword(s):  
Query Processing ◽  
Pervasive Computing ◽  
Spatial Data ◽  
Data Broadcasting ◽  
Grid Cells

Spatial Data on the Move

2011 ◽  
pp. 103-118
Author(s):  
Wee Hyong Tok ◽  
Stéphane Bresan ◽  
Panagiotis Kalnis ◽  
Baihua Zhengl
Keyword(s):  
Query Processing ◽  
Spatial Data ◽  
Location Based Services ◽  
Spatial Query ◽  
Spatial Query Processing ◽  
Wide Availability ◽  
Research Problems ◽  
Processing Techniques ◽  
Exciting Area ◽  
State Of Art

The pervasiveness of mobile computing devices and wide-availability of wireless networking infrastructure have empowered users with applications that provides location-based services as well as the ability to pose queries to remote servers. This necessitates the need for adaptive, robust, and efficient techniques for processing the queries. In this chapter, we identify the issues and challenges of processing spatial data on the move. Next, we present insights on state-of-art spatial query processing techniques used in these dynamic, mobile environments. We conclude with several potential open research problems in this exciting area.

Spatial Data on the Move

2010 ◽  
pp. 850-864
Author(s):  
Wee Hyong Tok ◽  
Stéphane Bressan ◽  
Panagiotis Kalnis ◽  
Baihua Zheng
Keyword(s):  
Query Processing ◽  
Spatial Data ◽  
Location Based Services ◽  
Spatial Query ◽  
Spatial Query Processing ◽  
Wide Availability ◽  
Research Problems ◽  
Processing Techniques ◽  
Exciting Area ◽  
State Of Art

The pervasiveness of mobile computing devices and wide-availability of wireless networking infrastructure have empowered users with applications that provides location-based services as well as the ability to pose queries to remote servers. This necessitates the need for adaptive, robust, and efficient techniques for processing the queries. In this chapter, we identify the issues and challenges of processing spatial data on the move. Next, we present insights on state-of-art spatial query processing techniques used in these dynamic, mobile environments. We conclude with several potential open research problems in this exciting area.

Unbounded Spatial Data Stream Query Processing using Spatial Semijoins

2021 ◽  
Vol 15 (02) ◽  
pp. 33-41
Author(s):  
Wendy Osborn
Keyword(s):  
Query Processing ◽  
Data Streams ◽  
Spatial Data ◽  
Data Stream ◽  
Spatial Extent ◽  
Common Region ◽  
Data Set ◽  
Spatial Join ◽  
The Common ◽  
Spatial Data Stream

In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified.

scholarly journals Quadrant-Based Minimum Bounding Rectangle-Tree Indexing Method for Similarity Queries over Big Spatial Data in HBase

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3032 ◽  
Author(s):  
Bumjoon Jo ◽  
Sungwon Jung
Keyword(s):  
Query Processing ◽  
Spatial Data ◽  
Rapid Development ◽  
Spatial Proximity ◽  
Spatial Query ◽  
Spatial Query Processing ◽  
Indexing Structure ◽  
Indexing Method ◽  
Minimum Bounding Rectangle ◽  
Linearization Techniques

With the rapid development of mobile devices and sensors, effective searching methods for big spatial data have recently received a significant amount of attention. Owing to their large size, many applications typically store recently generated spatial data in NoSQL databases such as HBase. As the index of HBase only supports a one-dimensional row keys, the spatial data is commonly enumerated using linearization techniques. However, the linearization techniques cannot completely guarantee the spatial proximity of data. Therefore, several studies have attempted to reduce false positives in spatial query processing by implementing a multi-dimensional indexing layer. In this paper, we propose a hierarchical indexing structure called a quadrant-based minimum bounding rectangle (QbMBR) tree for effective spatial query processing in HBase. In our method, spatial objects are grouped more precisely by using QbMBR and are indexed based on QbMBR. The QbMBR tree not only provides more selective query processing, but also reduces the storage space required for indexing. Based on the QbMBR tree index, two query-processing algorithms for range query and kNN query are also proposed in this paper. The algorithms significantly reduce query execution times by prefetching the necessary index nodes into memory while traversing the QbMBR tree. Experimental analysis demonstrates that our method significantly outperforms existing methods.

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