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El autor Michael Vassilakopoulos ha publicado 3 artículo(s):

2 - Efficient query processing on large spatial databases: A performance study

Processing of spatial queries has been studied extensively in the literature. In most cases, it is accomplished by indexing spatial data using spatial access methods. Spatial indexes, such as those based on the Quadtree, are important in spatial databases for efficient execution of queries involving spatial constraints and objects. In this paper, we study a recent balanced disk-based index structure for point data, called xBR+-tree, that belongs to the Quadtree family and hierarchically decomposes space in a regular manner. For the most common spatial queries, like Point Location, Window, Distance Range, Nearest Neighbor and Distance-based Join, the R-tree family is a very popular choice of spatial index, due to its excellent query performance. For this reason, we compare the performance of the xBR+-tree with respect to the R?-tree and the R+-tree for tree building and processing the most studied spatial queries. To perform this comparison, we utilize existing algorithms and present new ones. We demonstrate through extensive experimental performance results (I/O efficiency and execution time), based on medium and large real and synthetic datasets, that the xBR+-tree is a big winner in execution time in all cases and a winner in I/O in most cases.

Autores: George Roumelis / Michael Vassilakopoulos / Antonio Corral / Yannis Manolopoulos / 
Palabras Clave: Performance evaluation - Quadtrees - query processing - R-trees - Spatial access methods - Spatial databases - xBR-trees

3 - Efficient Large-scale Distance-Based Join Queries in SpatialHadoop

Efficient processing of Distance-Based Join Queries (DBJQs) in spatial databases is of paramount importance in many application domains (e.g. image processing, location-based systems, geographical information systems (GIS), continuous monitoring in streaming data settings, road network systems, etc.). The most representative and known DBJQs are the K Closest Pairs Query (KCPQ) and the e Distance Join Query (eDJQ). These types of join queries are characterized by a number of desired pairs (K) or a distance threshold (e) between the components of the pairs in the nal result, over two spatial datasets. Both are expensive operations, since two spatial datasets are combined with additional constraints, and they become even more costly operations for large-scale data. Given the increasing volume of spatial data originating from multiple sources and stored in distributed servers, it is not always efficient to perform DBJQs on a centralized server. For this reason, this paper addresses the problem of computing DBJQs on big spatial datasets in SpatialHadoop, an extension of Hadoop-MapReduce that supports efficient processing of spatial queries in a cloud-based setting. SpatialHadoop injects spatial data awareness in each Hadoop layer, i.e. language, storage, MapReduce and operations layers.We propose novel algorithms, based on plane-sweep, to perform efficient parallel DBJQs on large-scale spatial datasets in SpatialHadoop. In addition to the plane-sweep base technique, we present a methodology for improving the performance of the KCPQ algorithms by the computation of an upper bound of the distance of the K-th closest pair. To demonstrate the benets of our proposed methodologies, we present the results of the execution of an extensive set of experiments that demonstrate the efficiency and scalability of our proposals using big synthetic and real-world points datasets.

Autores: Antonio Corral / Francisco Garcia-Garcia / Luis Iribarne / Michael Vassilakopoulos / Yannis Manolopoulos / 
Palabras Clave: eDJQ - KCPQ - MapReduce - Spatial Data Processing - Spatial Query Evaluation - SpatialHadoop