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

Abstract

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.

A Distributed Spatial Index With High Update Efficiency for Location-Based Real-Time Services

“…False-positive will occur when using that index to execute a query. To reduce the number of false-positives, Jo (Jo & Jung, 2018) build a multidimensional index above Hbase client, called quadrant-based minimum bounding rectangle tree (QbMBR-tree). QbMBR-tree partitions the index items more accurately and uses QbMBR-tree to index the index items.…”

Advanced applications of chemo‐responsive dyes based odor imaging technology for fast sensing food quality and safety: A review

“…The optimal parameter can be solved by Equation 3, that is, the optimal solution of the linear equation system is also the optimal parameter of the ellipse, and the center is calculated. Using minimum bounding rectangle method, the contour of the target image needs to be scanned first to obtain the outer regular rectangle (Jo & Jung, 2018). Then, the minimum bounding rectangle is obtained by rotating and translating the main or auxiliary axis of the regular rectangle, and the center point coordinates o (x, y) of the minimum bounding rectangle are calculated according to the four vertex coordinates p 1— p 4, as shown in Figure 10.…”

State-of-the-Art Geospatial Information Processing in NoSQL Databases

“…HBase does not have special geospatial functions to support geospatial data storage and querying [62]. However, researchers developed some methods and applications for processing geospatial data in HBase [63][64][65][66][67][68][69][70], such as a geographical database with geohash-based spatial indexes [63,71], big spatial data processing with Apache Spark [72], a geospatial data model [64], and a new spatial query method based on primary keys' indexing [73]. Additionally, an open source suite of tools, GeoMesa, was designed to implement large-scale geospatial analytics and querying in the cloud or in conjunction with the HBase and Cassandra databases [74].…”