Due to the prevalence of GPS-equipped cameras (e.g., smartphones and surveillance cameras), massive amounts of geo-tagged images capturing urban streets are increasingly being collected. Consequently, many smart city applications have emerged, relying on efficient image search. Such searches include spatial-visual queries in which spatial and visual properties are used in tandem to retrieve similar images to a given query image within a given geographical region. Towards this end, new index structures that organize images based on both spatial and visual properties are needed to efficiently execute such queries. Based on our observation that street images are typically similar in the same spatial locality, index structures for spatial-visual queries can be effectively built on a spatial index (i.e., R*-tree). Therefore, we propose a class of R*-tree indexes, particularly, by associating each node with two separate minimum bounding rectangles (MBR), one for spatial and the other for (dimension-reduced) visual properties of their contained images, and adapting the R*-tree optimization criteria to both property types.

With the proliferation of global positioning systems (GPS) enabled devices, a growing number of database systems are capable of storing and querying different spatial objects including points, polylines and polygons. In this paper, we present our experience with supporting one important class of spatial queries in these database systems: distance queries. For example, a traveler may want to find hotels within 500 meters of a nearby beach. In addition, this paper presents new techniques implemented in Oracle Spatial for some distance-related problems, such as the maximum distance between complex spatial objects, and the diameter, the convex hull and the minimum bounding circle of complex spatial objects. We conduct our experiments by utilizing real-world data sets and demonstrate that these distance and distance-related queries can be significantly improved.

The 22nd ACM SIGSPATIAL Conference on Advances in Geographic Information Systems (GIS) was held in November of 2014 in Dallas, Texas, US. Following the success of last two events, we organized the 3rd programming contest associated with the conference, called the SIGSPATIAL GIS 2014. The subject of the competition was constrained generalization, which aims to generalize geometric features in the context of topological constraints. We describe the contest details, and the results, as well as the lessons learned during the process.

Transportation networks including roads and railways, and linear hydrography features like streams and canals are traditionally represented as complex lines in geographic information systems (GIS) and spatial database systems. In addition, as the Global Positioning System (GPS) becomes increasingly ubiquitous, GIS and spatial database systems are also encountering increasing use of trajectories of moving objects, which can also be represented as complex lines with each vertex not only containing location information, but also associated with some additional measures such as time. In this paper, we present our experience with supporting topological relationship queries for these complex lines. Furthermore, this paper presents our experience with supporting topological relationship queries for complex geometry collections, such as a composite hydrography feature, which can be comprised of complex lines (for narrow portions of rivers) and complex polygons (for wide portions of rivers, and lakes). We conduct our experiments by utilizing real-world data sets and demonstrate that topological relationship query performance for both complex lines and complex collections can be significantly improved.

The 21st ACM SIGSPATIAL Conference on Advances in Geographic Information Systems (GIS) was held in November of 2013 in Orlando, Florida. Following the success of last year's event, we organized the second programming contest associated with the conference, called the SIGSPATIAL GIS Cup 2013. The subject of the competition was Geo-fencing, which identifies the qualified point and area pairs using a virtual perimeter for a real-world geographic area. We describe the contest details, and the results, as well as the lessons learned during the process.

Oracle Spatial and Graph is a geographic information system (GIS) which provides users the ability to store spatial data alongside conventional data in Oracle. As a result of the coexistence of spatial and other data, we observe a trend towards users performing increasingly complex queries which involve spatial as well as non-spatial predicates. Accurate selectivity values, especially for queries with multiple predicates requiring joins among numerous tables, are essential for the database optimizer to determine a good execution plan. For queries involving spatial predicates, this requires that reasonably accurate statistics collection has been performed on the spatial data. For extensible data cartridges such as Oracle Spatial and Graph, the optimizer expects to receive accurate predicate selectivity and cost values from functions implemented within the data cartridge. Although statistics collection for spatial data has been researched in academia for a few years; to the best of our knowledge, this is the first work to present spatial statistics collection implementation details for a commercial GIS database. In this paper, we describe our experiences with implementation of statistics collection methods for complex geometry objects within Oracle Spatial and Graph. Firstly, we exemplify issues with previous partitioning-based algorithms in presence of complex geometry objects and suggest enhancements which resolve the issues. Secondly, we propose a main memory implementation which not only speeds up the disk-based partitioning algorithms but also utilizes existing R-tree indexes to provide surprisingly accurate selectivity estimates. Last but not the least, we provide extensive experimental results and an example study which displays the efficacy of our approach on Oracle query performance.

Although geographic information systems (GIS) and spatial database communities have extensively studied topological relationships for more than two decades, there is little literature describing how to efficiently implement them in GIS and spatial database systems. This is rather surprising considering that topological relationship queries are supported in many GIS and spatial database systems including IBM Informix Spatial and Geodetic DataBlades, ESRI SDE, Microsoft SQL server 2008, Oracle Spatial and PostGIS. In order to bridge this gap, we report our experience with implementing several optimization techniques in Oracle Spatial to speed up topological relationship query processing for query windows represented by complex regions (such as polygons or multi-polygons). Our experiments, utilizing real-world data sets, demonstrate that topological relationship query performance can be significantly improved using the proposed techniques.

In this paper, we briefly describe the implementation of various Open Geospatial Consortium Web Service Interface Standards in Oracle Spatial 11g. We highlight how we utilize Oracle's implementation of OASIS Web Services Security (WSS) to provide a robust security framework for these OGC Web Services. We also discuss our future direction in supporting OGC Web Service Interface Standards.

The Resource Description Framework (RDF) is a standard for representing information that can be identified using a Uniform Resource Identifier. In particular, it is intended for representing metadata about Web resources. RDF is being used in numerous application areas, including Life Sciences, Digital Libraries, and Intelligence. The RDF data structure is a directed graph or network. Current solutions to managing RDF data utilize flat relational tables for database storage. This paper presents an alternative approach to managing RDF data in the database. We introduce a new object type for storing RDF data. The object type is built on top of the Oracle Spatial Network Data Model (NDM), which is Oracle’s network solution in the database. This exposes the NDM functionality to RDF data, allowing RDF data to be managed as objects and analyzed as networks. Reification, a means of providing metadata for the RDF data, puts a strain on storage. We present a streamlined approach to representing reified RDF data for faster retrievals. An RDF object type and reification in the database provide the basic infrastructure for effective metadata management.

Much research has been devoted to scalable storage and retrieval techniques for domain databases such as spatial, text, XML and gene sequence data. Many efficient indexing techniques have been developed in this context. Given the improvement in the underlying technology, database applications are increasingly using domain data in transactional semantics. We examine the issue of when during the lifetime of a transaction is it better to incorporate updates in domain indexes. We present our experiences with R-tree indexes in Oracle. We examine two approaches for incorporating updates in spatial R-tree indexes: the first at update time, and the second at commit time. The first approach immediately incorporates changes in the index right away using system transactions and at commit time makes them visible to other transactions. The second approach, referred to as the deferred-incorporate approach, defers the updates in a secondary table and incorporates the changes in the index only at commit time. In experiments on real data sets, we compare the performance of the two approaches. For most transactions with reasonable number of update operations, we observe that the deferred approach outperforms the immediate-incorporate approach significantly for update operations and with appropriate optimizations achieves comparable query performance.

Spatial joins and spatial index creation are two of the most expensive operations in Oracle Spatial. Since spatial indexing is implemented in extensible indexing framework where queries only return rows from a single table, spatial joins could not be effectively and efficiently implemented in Oracle8i and prior releases. On the other hand, spatial indexcreationinvolvesmuchcomputationorI/Othatcouldbe easily parallelized. In this paper, we describe how Oracle Spatial applies parallel and pipelined table function technology to perform fast spatial joins and parallel index creation. This technologyhas been introduced in Oracle9i and allows users to iteratively return subsets of result rows to be used in the “from” clause of a SQL query. We present our experiences with these implementations and examine the performance on real datasets.

Spatial indexing has been one of the active focus areas in recent database research. Several variants of Quadtree and R-tree indexes have been proposed in database literature. In this paper, we first describe briefly our implementation of Quadtree and R-tree index structures and related optimizations in Oracle Spatial. We then examine the relative merits of two structures as implemented in Oracle Spatial and compare their performance for different types of queries and other operations. Finally, we summarize experiences with these different structures in indexing large GIS datasets in Oracle Spatial.

No abstract available.