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.

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.

SPATIAL QUERY PROCESSING USING GENERALIZED FILTER

2000 ◽  
Vol 09 (01n02) ◽  
pp. 77-91 ◽  
Author(s):  
XIAOFANG ZHOU ◽  
XUEMIN LIN ◽  
CHENGFEI LIU ◽  
JINLI CAO
Keyword(s):  
Query Processing ◽  
Spatial Data ◽  
Environmental Data ◽  
Spatial Query ◽  
Candidate Sequence ◽  
Spatial Query Processing ◽  
Processing Step ◽  
Spatial Expressions ◽  
Using Data ◽  
Different Sources

Spatial data, ranging from various land information data to different types of environmental data, are typically collected and used by different custodians. The full benefits of using spatial data can be achieved by combining the data from different sources covering a common region. Due to organizational, political and technical reasons, it is unrealistic to physically integrate the vast amount of spatial data managed by different systems in different organizations. A practical approach is to provide interoperability to support multi-site data queries. In this paper, we study the performance aspect of complex spatial query processing. We propose a framework for processing queries with multiple spatial and aspatial predicates using data from multiple sites. Using a new concept called generalized filter, a query is processed in three steps. First, an aspatial filter that incorporates some conditions derived from spatial predicates is used to find a set of candidates, which is a superset of the final query results. Then, the candidates are manipulated and a refinement step is executed following an optimized candidate sequence. Finally, a post-processing step is used to handle spatial expressions in query results. The focus of this paper is to generate enhanced filters in order to minimize the need of transferring and processing complex spatial data.

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