1,043 research outputs found
Multi-scale data storage schemes for spatial information systems
This thesis documents a research project that has led to the design and prototype
implementation of several data storage schemes suited to the efficient multi-scale
representation of integrated spatial data. Spatial information systems will benefit from
having data models which allow for data to be viewed and analysed at various levels
of detail, while the integration of data from different sources will lead to a more
accurate representation of reality.
The work has addressed two specific problems. The first concerns the design of an
integrated multi-scale data model suited for use within Geographical Information
Systems. This has led to the development of two data models, each of which allow for
the integration of terrain data and topographic data at multiple levels of detail. The
models are based on a combination of adapted versions of three previous data
structures, namely, the constrained Delaunay pyramid, the line generalisation tree and
the fixed grid.
The second specific problem addressed in this thesis has been the development of an
integrated multi-scale 3-D geological data model, for use within a Geoscientific
Information System. This has resulted in a data storage scheme which enables the
integration of terrain data, geological outcrop data and borehole data at various levels
of detail.
The thesis also presents details of prototype database implementations of each of the
new data storage schemes. These implementations have served to demonstrate the
feasibility and benefits of an integrated multi-scale approach.
The research has also brought to light some areas that will need further research before
fully functional systems are produced. The final chapter contains, in addition to
conclusions made as a result of the research to date, a summary of some of these areas
that require future work
Efficient geographic information systems: Data structures, Boolean operations and concurrency control
Geographic Information Systems (GIS) are crucial to the ability of govern mental agencies and business to record, manage and analyze geographic data efficiently. They provide methods of analysis and simulation on geographic data that were previously infeasible using traditional hardcopy maps. Creation of realistic 3-D sceneries by overlaying satellite imagery over digital elevation models (DEM) was not possible using paper maps. Determination of suitable areas for construction that would have the fewest environmental impacts once required manual tracing of different map sets on mylar sheets; now it can be done in real time by GIS. Geographic information processing has significant space and time require ments. This thesis concentrates on techniques which can make existing GIS more efficient by considering these issues: Data Structure, Boolean Operations on Geographic Data, Concurrency Control. Geographic data span multiple dimensions and consist of geometric shapes such as points, lines, and areas, which cannot be efficiently handled using a traditional one-dimensional data structure. We therefore first survey spatial data structures for geographic data and then show how a spatial data structure called an R-tree can be used to augment the performance of many existing GIS. Boolean operations on geographic data are fundamental to the spatial anal ysis common in geographic data processing. They allow the user to analyze geographic data by using operators such as AND, OR, NOT on geographic ob jects. An example of a boolean operation query would be, Find all regions that have low elevation AND soil type clay. Boolean operations require signif icant time to process. We present a generalized solution that could significantly improve the time performance of evaluating complex boolean operation queries. Concurrency control on spatial data structures for geographic data processing is becoming more critical as the size and resolution of geographic databases increase. We present algorithms to enable concurrent access to R-tree spatial data structures so that efficient sharing of geographic data can occur in a multi user GIS environment
Geographic Information Systems: The Developer\u27s Perspective
Geographic information systems, which manage data describing the surface of the earth, are becoming increasingly popular. This research details the current state of the art of geographic data processing in terms of the needs of the geographic information system developer. The research focuses chiefly on the geographic data model--the basic building block of the geographic information system. The two most popular models, tessellation and vector, are studied in detail, as well as a number of hybrid data models.
In addition, geographic database management is discussed in terms of geographic data access and query processing. Finally, a pragmatic discussion of geographic information system design is presented covering such topics as distributed database considerations and artificial intelligence considerations
Monitoring and Detection of Hotspots using Satellite Images
Nowadays, the usage of optical remote sensing NOAA-AVHRR satellite data
is getting familiar as it is known can save cost in order to capture a wide coverage of
ground image. The captured images are meaningful after several processes done
over it to produce hotspot detection. Developing a specific database to store
information of Hotspots (LAC images) would make datamanagement and archiving
purpose in more efficient and systematic way. Real-time data gathered are monitored
countries such as Malaysia, Thailand, Singapore, Indonesia and Brunei within the
region of NOAA Satellite coverage area. PostGIS, PostgreSQL, Mapserver and
Autodesk MapGuide Studio software are to be studied as a guide to develop a
system with simple database using object-relational database management system to
store raster and vector images. This paper describes a solution for efficient handling
of large raster image data sets in a standard object-relational database management
system. By means of adequate indexing, retrieval techniques and multi resolution
cell indexing (Quad-Tree) can be achieved using a standard DBMS, even for very
large satellite images. Single image will be divided equally into 64 small squares (3
levels of image hierarchy - each level has 4 sub images of the higher image). Partial
information of Daily Haze report (processed Hotspot on image map) produces by
NREB can be viewed using web-based application. The final product of this project
is a web-based application for displaying Hotspots on maps (combination of raster
and vector images) with the ability to search record from database and functions to
zoom in or zoom out the map. The objective of this paper is also to show the way
satellite images and descriptive information are combined and amalgamated to form
an Internet or Intranet application
The OTree: multidimensional indexing with efficient data sampling for HPC
Spatial big data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of petabytes of spatial data per year. However, many authors have pointed out that the lack of specialized frameworks for multidimensional Big Data is limiting possible applications and precluding many scientific breakthroughs. Paramount in achieving High-Performance Data Analytics is to optimize and reduce the I/O operations required to analyze large data sets. To do so, we need to organize and index the data according to its multidimensional attributes. At the same time, to enable fast and interactive exploratory analysis, it is vital to generate approximate representations of large datasets efficiently. In this paper, we propose the Outlook Tree (or OTree), a novel Multidimensional Indexing with efficient data Sampling (MIS) algorithm. The OTree enables exploratory analysis of large multidimensional datasets with arbitrary precision, a vital missing feature in current distributed data management solutions. Our algorithm reduces the indexing overhead and achieves high performance even for write-intensive HPC applications. Indeed, we use the OTree to store the scientific results of a study on the efficiency of drug inhalers. Then we compare the OTree implementation on Apache Cassandra, named Qbeast, with PostgreSQL and plain storage. Lastly, we demonstrate that our proposal delivers better performance and scalability.Peer ReviewedPostprint (author's final draft
6 Access Methods and Query Processing Techniques
The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B +-tree, hashing with chaining, and, in som
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