Spatial Data Mining Architecture and Technologies PPT
Team 12
Hari Kishan Bandaru
Sneha Anand Yeluguri
Parimi VSPVSK
Spatial Data
Mining
Architecture and
Technologies
Overview
Introduction to Spatial Data Mining
Related Work
Process of Spatial Data Mining
Process of Visual Space Data Mining
Common Data Mining Architecture
Spatial Data Mining Architecture
Visualization Data Model
Technologies
Advantages and Future Work
Conclusion
References
2/21
What is Spatial Data Mining?
Non–trivial search for interesting and unexpected spatial patterns
Non-trivial Search:
Large (e.g. exponential) search space of plausible hypothesis
Ex. Asiatic cholera: causes: water, food, air , insects,…; water delivery mechanisms: pumps, rivers, ponds, wells…
Interesting:
Useful in certain application domain
Ex. Shutting off identified water pumps => saved human life
Unexpected:
May provide a new understanding of world
Ex. Water pump – Cholera connection to the “germ” theory.
3/21
What is not a Spatial Data Mining?
Simple querying of Spatial data
Testing a hypothesis via a primary analysis
Uninteresting or obvious patterns in spatial data
Mining of non-spatial data
4/21
Motivation
To find new spatial patterns
To understand new geographic process for critical questions
To analyze the fast growing spatial data
To explore large number of geographic hypothesis
To reduce plausible hypothesis
To discover relationships between spatial and non spatial data
To build spatial knowledge-bases
5/21
Problem Statement
Rapid development in the technology of spatial data storage, query, display and analysis.
Accessing spatial data through access methods often need technology to spatial reasoning, geographic computing and knowledge of space showing.
Spatial data mining technology is used to convert spatial information of geographic coordinates into useful knowledge and effective tools.
Visualization technology is used to generate graphics from complex multi-dimensional data to display objective things and their intrinsic connections.
6/21
Related Work
Transformation of map information mode to an equilateral mode that consists of formalization , cognition and transmission.
Geo Visualization aim is to provide an information exchange and feedback mechanisms for the users.
Summarized visualization technology into three points: Feature Identification, Feature Comparison and Feature Interpretation.
7/21
Process of Spatial Data Mining
Inputting Spatial Datum
Feature Extraction and Feature Database Establishment
Data Warehouses Establishment
Data Extraction and forming Case Set
Create and Train Data Mining Model
Evaluating the Mined Out Model, discovering hidden knowledge
Knowledge Application
8/21
Process of Visualization Spatial Data Mining
Filter : Extracting data of interest
Mapping : Creating geometric primitives
Draw : Translate geometric primitives into image
Feedback : Display the image
9/21
Common Data Mining Architecture
The efficiency of the data mining should be improved
Historical method cant be obtained effective utilization
Interoperability between different systems is bad.
For different application object, pertinence is not strong.
10/21
Spatial Data Mining Architecture
11/21
Visualization Spatial Data Mining Model
Statistical Mapping
Technique
Color
Thematic map visualization techniques
12/21
Four TierJ2EE Technology
Applet Call Servlet:
Servlet Call Java Bean:
13/21
Four TierJ2EE Technology
JavaBean Call JAFMAS components:
Return the results from JAFMAS:
14/21
XML and Space Data Warehouse
Technology
Read data from data warehouse and generate xml document with unified expression form.
Change XML into DOM object model serves for upper accessing.
15/21
Advantages
Use of thematic map visualization technology helped users to explore spatial data
Increase the data processing speed significantly
Made abstract data much easier to understand
The proposed J2EE four tier architecture had resolved the synergic work between the layers of prototype system and between the components in the layer.
16/21
Future Work
Many existing theoretical and technical issues should be further explored and studied.
Ex: spatial data mining in structured modeling
Treatment of uncertain information
Need to explore similarity measure techniques of mining model produced by statistics, fuzzy logic , rough set methods.
17/21
Conclusion
Combination of visualization technology and spatial data mining has helped for analysis of spatial data exploration.
New process and architecture were presented for spatial datum data mining based on data ware house.
The characteristics for spatial datum were analyzed and difference between spatial data and traditional relationship data were analyzed.
18/21
References
1. He Yueshun, Li Xiang; A Study of Spatial Data Mining
Technique Based on Web; In the preceedings of
International Conference on Engineering Profession,
General Topics for Engineers (Math, Science &
Engineering); Page 1-4; 2009
2. Xiao Qiang, Yan Wei, Zhang Hanfei; Application of
Visualization Technology in Spatial Data Mining; In the preceedings of International Conference on Computing,
Control and Industrial Engineering; Page 153-157; 2010
3. He Yueshun, Xu Wei; A study of spatial data mining
architecture and technology; In the preceedings of 2nd
IEEE International Conference on Computing &
Processing (Hardware/Software); Page 163-166; 2009
21/21
Queries
19/20
20/20
