NDLI: Spatial bayesian learning algorithms for geographic information retrieval

Please wait, while we are loading the content...

Close pair queries in moving object databases

In-network surface simplification for sensor fields

Extracting spatial association rules from spatial transactions

A spatio-temporal access method based on snapshots and events

3D trajectory matching by pose normalization

Time relaxed spatiotemporal trajectory joins

Towards topological consistency and similarity of multiresolution geographical maps

A landform-based approach for the representation of terrain silhouettes

Installation of a tsunami early warning system in the Indian Ocean

Synthetic generation of cellular network positioning data

Monitoring dynamic spatial fields using responsive geosensor networks

Finding corresponding objects when integrating several geo-spatial datasets

Improving the R*-tree with outlier handling techniques

Use of rational numbers in the design of robust geometric primitives for three-dimensional spatial database systems

Two ellipse-based pruning methods for group nearest neighbor queries

ASPEN: an adaptive spatial peer-to-peer network

Automatic extraction of road intersections from raster maps

Shapes based trajectory queries for moving objects

Spatial queries in sensor networks

Quality-driven approximate methods for integrating GIS data

Image-based change detection of areal objects using differential snakes

Continuous level-of-detail modeling of buildings in 3D city models

Efficient query processing on spatial networks

Dimension-refined topological predicates

Visualizing the results of interactive queries for geographic data on mobile devices

Supporting uncertainty in moving objects in network databases

Error analysis of localization systems for sensor networks

Spatial bayesian learning algorithms for geographic information retrieval

Multi-resolution out-of-core modeling of terrain and teological data

Adaptive nearest neighbor queries in travel time networks

Contraflow network reconfiguration for evacuation planning: a summary of results

Implicit interaction profiling for recommending spatial content

Spatial bayesian learning algorithms for geographic information retrieval

Content Provider	ACM Digital Library
Author	Moody, Miles Walker, Arron R. Pham, Binh
Abstract	An increasing amount of freely available Geographic Information System (GIS) data on the Internet has stimulated recent research into Geographic Information Retrieval (GIR). Typically, GIR looks at the problem of retrieving GIS datasets on a theme by theme basis. However in practice, themes are generally not analysed in isolation. More often than not multiple themes are required to create a map for a particular analysis task. To do this using the current GIR techniques, each theme is retrieved one by one using traditional retrieval methods and manually added to the map. To automate map creation the traditional GIR paradigm of matching a query to a single theme type must be extended to include discovering relationships between different theme types.Bayesian Inference networks can and have recently been adapted to provide a theme to theme relevance ranking scheme which can be used to automate map creation [2]. The use of Bayesian inference for GIR relies on a manually created Bayesian network. The Bayesian network contains causal probability relationships between spatial themes. The next step in using Bayesian Inference for GIR is to develop algorithms to automatically create a Bayesian network from historical data. This paper discusses a process to utilize conventional Bayesian learning algorithms in GIR. In addition, it proposes three spatial learning Bayesian network algorithms that incorporate spatial relationships between themes into the learning process. The resulting Bayesian networks were loaded into an inference engine that was used to retrieve all relevant themes given a test set of user queries. The performance of the spatial Bayesian learning algorithms were evaluated and compared to performance of conventional non-spatial Bayesian learning algorithms.This contribution will increase the performance and efficiency of knowledge extraction from GIS by allowing users to focus on interpreting data, instead of focusing on finding which data is relevant to their analysis.
Starting Page	105
Ending Page	114
Page Count	10
File Format	PDF
ISBN	1595931465
DOI	10.1145/1097064.1097080
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-11-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Geographic information system Information retrieval Geographic information retrieval Learning bayesian networks Spatial bayesian learning
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Spatial Bayesian Learning Algorithms for Geographic Information Retrieval (2005)

Sparse Bayesian learning for ranking

MUSEMBLE: A Music Retrieval System Based on Learning Environment

Learning to rank for geographic information retrieval

A spatial algebra for content-based retrieval.

Geographic scope modeling for web documents

A bayesian framework for automated dataset retrieval in geographic information systems

Judging spatial relevance and document location for Geographic Information Retrieval, extended abstract (2006)

Neural networks for the automation of Arabic text categorization

Spatial bayesian learning algorithms for geographic information retrieval

Similar Documents

Spatial Bayesian Learning Algorithms for Geographic Information Retrieval (2005)

Sparse Bayesian learning for ranking

MUSEMBLE: A Music Retrieval System Based on Learning Environment

Learning to rank for geographic information retrieval

A spatial algebra for content-based retrieval.

Geographic scope modeling for web documents

A bayesian framework for automated dataset retrieval in geographic information systems

Judging spatial relevance and document location for Geographic Information Retrieval, extended abstract (2006)

Neural networks for the automation of Arabic text categorization

Spatial bayesian learning algorithms for geographic information retrieval