Efficient Group K Nearest-Neighbor Spatial Query Processing in Apache Spark

Aiming at the problem of spatial query processing in distributed computing systems, the design and implementation of new distributed spatial query algorithms is a current challenge. Apache Spark is a memory-based framework suitable for real-time and batch processing. Spark-based systems allow users to work on distributed in-memory data, without worrying about the data distribution mechanism and fault-tolerance. Given two datasets of points (called Query and Training), the group K nearest-neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum of distances to every point of the Query. This spatial query has been actively studied in centralized environments and several performance improving techniques and pruning heuristics have been also proposed, while, a distributed algorithm in Apache Hadoop was recently proposed by our team. Since, in general, Apache Hadoop exhibits lower performance than Spark, in this paper, we present the first distributed GKNN query algorithm in Apache Spark and compare it against the one in Apache Hadoop. This algorithm incorporates programming features and facilities that are specific to Apache Spark. Moreover, techniques that improve performance and are applicable in Apache Spark are also incorporated. The results of an extensive set of experiments with real-world spatial datasets are presented, demonstrating that our Apache Spark GKNN solution, with its improvements, is efficient and a clear winner in comparison to processing this query in Apache Hadoop.

A Privacy-Preserving Spatial Index for Spatial Query Processing

An increasing amount of active research is being conducted to protect the locations of mobile device users. Users must tune to more data than they would like to in order to hide their location. In particular, if a user requests a query over kNN, the number of objects the user must receive may increase. Several studies have been proposed to solve these problems. However, problems have been identified during the course of query processing, such as errors and increased query processing times. When the tuning time is increased, the amount of data to download and the battery consumption of the client also increase. In this study, we propose the Privacy-preserving Spatial Index (PSI), an index that allows users to reduce their tuning time while being satisfied with the results of their queries. The querier (q) requests the object in the area protecting his/her location from the server. The server sends the requested data of points of interest (POIs) (DPOIs) in the Privacy-preserving Region (PR) to q. Finally, q reduces tuning time by selectively tuning to the desired data of POIs (Dw) through PSI. The superiority of PSI over previous techniques is experimentally proven.

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

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.