Jayant Sharma

Indexing medium-dimensionality data in Oracle

With the advent of GIS, multi-media, and warehousing technologies, database systems have started focusing on storage and access of multi-dimensional data such as spatial, OLAP, image, audio, and video attributes. As a step in this direction, Oracle% launched the interMedia product to support spatial and image data, and Materialized Views (MV) to support warehousing applications. Although 2-dimensional spatial data is efficiently indexed using OracleBi Spatial, and highdimensional image data using a combination of bitmap indexing and the Visual Information Retrieval (VIR) product, there is still a need for efficient indexing mechanisms for medium-dimensionality data such as OLAP, and CAD/CAM applications. In this paper, we describe the implementation of a new indextype, called the R-tree, to support medium-dimensionality data (i.e., data whose dimensionality is in the range of 310) using the extensible indexing framework [DDSS95] of OracleSi. This indextype combines some of the best features of existing R-tree variants [Gut84, BKSSSO, WJ96, LLE97, RKV95].

Oracle8i Spatial: Experiences with Extensible Databases

Conventional relational databases often do not have the technology required to handle complex data like spatial data. Unlike the traditional applications of databases, spatial applications require that databases understand complex data types like points, lines, and polygons. Typically, operations on these types are complex when compared to the operations on simple types. Hence relational database systems need to be extended in several areas to facilitate the storage and retrieval of spatial data. Several research reports have described the requirements from a database system and prioritized the research needs in this area.

Hierarchical reasoning about direction relations

Spatial reasoning is an important area of GIS and Spatial Databases research. This paper deals with reasoning about direction relations (east, northeast) in geographic hierarchies. We assume a database that stores the direction relations between objects in the same geographic entity and propose algorithms for the inference of relations between objects in different entities. We present two types of inference: the first one uses the relations of ancestor regions, while the second is based on composition of spatial relations and path consistency. For both types we provide inference rules, illustrate examples, and study the computational complexity. Although we use a specific set of relations for demonstration purposes, the algorithms are applicable to any set of direction relations provided with appropriate inference rules.

Frontiers of Geographic Information Technology

Geographic Information Technologies - Overview (Sanjay Rana and Jayant Sharma).- Soft Computing and Geographical Information Systems (Yingjie Yang, David Gillingwater and Chris Hinde).- Using Geospatial Information for Autonomous Systems Control (Ayanna Howard and Edward Tunstel).- Simulating Spatial Dynamics Using Cell & Agent-Based Urban Models (Mike Batty).- Distributed Geospatial Information Services (Chaowei (Phil) Yang and C. Vincent Tao).- Geospatial Grid (Liping Di).- Geospatial Semantic Web (Yaser Bishr).- The Role of DBMS in New Generation GIS Architecture (Sisi Zlatanova and Jantien Stoter).- Multimodal Interfaces for Representing and Accessing Geospatial Information (Reg Golledge, Matthew T. Rice and R. Daniel Jacobson).- Way Finding With Mobile Devices Decision Support (Sabine Timpf).- Augmented Reality Visualisation of Geospatial Data (Daniel Holweg and Ursula Kretschmer).- GeoICT and Development - The Inverted Pyramid Syndrome (Satya Prakash, Ayon Kumar Tarafdar and Ravi Gupta).- Privacy Issues in Geographic Information Technologies (Alastair Beresford).- Frontiers of Geographic Information Technology (Sanjay Rana and Jayant Sharma).

Integrating maps and location analysis with Oracle business intelligence

In this session we will demonstrate the integration of Oracles geospatial and enterprise business intelligence technologies. Oracle Spatial, a feature of the Oracle database, contains a wealth of capabilities for geospatial data management, analysis, and provisioning. Oracle MapViewer is a feature of Oracle Fusion Middleware providing light-weight interactive mapping for integration with enterprise applications. Oracle Business Intelligence Suite Enterprise Edition Plus (Oracle BI EE Plus) is a comprehensive suite of enterprise BI products that delivers a full range of analysis and reporting capabilities. Featuring a unified, highly scalable, modern architecture, Oracle BI EE Plus provides intelligence and analytics from data spanning enterprise sources and applications---empowering the largest communities with complete and relevant insight. By location-enabling the BI platform, we are able to enhance traditional BI interactions with location-aware analytics, cartographic views of analytic results, and geography-based drilling and analyses. As opposed to rigid, static integrations, we will demonstrate a highly interactive, customizable, and fully-embedded geospatial BI solution.

An Overview of Oracle Chart Builder and Map Viewer

Oracle Chart Builder is a real-time Java charting API. It enables more effective communication and analysis of information for business graphics and performance applications. MapViewer is a programmable tool for rendering maps using spatial data managed by Oracle Spatial. MapViewer provides tools that hide the complexity of spatial data queries and cartographic rendering, while providing customizable options for more advanced users. This paper presents an overview of data visualization features in the 9i Application Server product from Oracle.