Towards a Scalable Dynamic Spatial Database System

With the rise of GPS-enabled smartphones and other similar mobile devices, massive amounts of location data are available. However, no scalable solutions for soft real-time spatial queries on large sets of moving objects have yet emerged. In this paper we explore and measure the limits of actual algorithms and implementations regarding different application scenarios. And finally we propose a novel distributed architecture to solve the scalability issues.Comment: (2012

The combination of spatial access methods and computational geometry in geographic database systems

Geographic database systems, known as geographic information systems (GISs) particularly among non-computer scientists, are one of the most important applications of the very active research area named spatial database systems. Consequently following the database approach, a GIS hag to be seamless, i.e. store the complete area of interest (e.g. the whole world) in one database map. For exhibiting acceptable performance a seamless GIS hag to use spatial access methods. Due to the complexity of query and analysis operations on geographic objects, state-of-the-art computational geomeny concepts have to be used in implementing these operations. In this paper, we present GIS operations based on the compuational geomeny technique plane sweep. Specifically, we show how the two ingredients spatial access methods and computational geomeny concepts can be combined für improving the performance of GIS operations. The fruitfulness of this combination is based on the fact that spatial access methods efficiently provide the data at the time when computational geomeny algorithms need it für processing. Additionally, this combination avoids page faults and facilitates the parallelization of the algorithms.

W42 - a scalable spatial database system for holding Digital Elevation Models

The design of a scalable system for holding spatial data in general and digital elevation models (DEMs) in specific has to account for the characteristics of data from various application fields. The data can be heterogeneous in coverage, as well as in resolution, information content and quality. A database aiming at the representation of world-wide DEMs has to consider these differences in the design of the system with respect to the structure and the algorithms. The database system W42, which is presented in the work at hand, is a scalable spatial database system capable of holding, extracting, mosaicking, and fusing spatial data represented in raster- as well as in vector-format. Design aspects for this task can be specified as holding spatial data in unique data structures and providing unique access functions to the data. These are subject of this work as well as first experiences gained from the implementation of part of the extensions made for the TanDEM-X mission

A global database for metacommunity ecology, integrating species, traits, environment and space

The use of functional information in the form of species traits plays an important role in explaining biodiversity patterns and responses to environmental changes. Although relationships between species composition, their traits, and the environment have been extensively studied on a case-by-case basis, results are variable, and it remains unclear how generalizable these relationships are across ecosystems, taxa and spatial scales. To address this gap, we collated 80 datasets from trait-based studies into a global database for metaCommunity Ecology: Species, Traits, Environment and Space; “CESTES”. Each dataset includes four matrices: species community abundances or presences/absences across multiple sites, species trait information, environmental variables and spatial coordinates of the sampling sites. The CESTES database is a live database: it will be maintained and expanded in the future as new datasets become available. By its harmonized structure, and the diversity of ecosystem types, taxonomic groups, and spatial scales it covers, the CESTES database provides an important opportunity for synthetic trait-based research in community ecology

An Algorithm for Data Reorganization in a Multi-dimensional Index

In spatial databases, data are associated with spatial coordinates and are retrieved based on spatial proximity. A spatial database uses spatial indexes to optimize spatial queries. An essential ingredient for efficient spatial query processing is spatial clustering of data and reorganization of spatial data. Traditional clustering algorithms and reorganization utilities lack in performance and execution. To solve this problem we have developed an algorithm to convert a two dimensional spatial index into a single dimensional value and then a reorganization is done on the spatial data. This report describes this algorithm as well as various experiments to validate its effectiveness

Optimizing Spatial Databases

This paper describes the best way to improve the optimization of spatial databases: through spatial indexes. The most commune and utilized spatial indexes are R-tree and Quadtree and they are presented, analyzed and compared in this paper. Also there are given a few examples of queries that run in Oracle Spatial and are being supported by an R-tree spatial index. Spatial databases offer special features that can be very helpful when needing to represent such data. But in terms of storage and time costs, spatial data can require a lot of resources. This is why optimizing the database is one of the most important aspects when working with large volumes of data.Spatial Database, Spatial Index, R-tree, Quadtree, Optimization

Optimization of Spatial Joins Using Filters

When viewing present-day technical applications that rely on the use of database systems, one notices that new techniques must be integrated in database management systems to be able to support these applications efficiently. This paper discusses one of these techniques in the context of supporting a Geographic Information System. It is known that the use of filters on geometric objects has a significant impact on the processing of 2-way spatial join queries. For this purpose, filters require approximations of objects. Queries can be optimized by filtering data not with just one but with several filters. Existing join methods are based on a combination of filters and a spatial index. The index is used to reduce the cost of the filter step and to minimize the cost of retrieving geometric objects from disk. In this paper we examine n-way spatial joins. Complex n-way spatial join queries require solving several 2-way joins of intermediate results. In this case, not only the profit gained from using both filters and spatial indices but also the additional cost due to using these techniques are examined. For 2-way joins of base relations these costs are considered part of physical database design. We focus on the criteria for mutually comparing filters and not on those for spatial indices. Important aspects of a multi-step filter-based n-way spatial join method are described together with performance experiments. The winning join method uses several filters with approximations that are constructed by rotating two parallel lines around the object