Building Geo spatial Mashups to
Visualize Information for Crisis
Management
Authors:
Shubham Gupta and Craig A. Knoblock
Presented By:
Shrikanth Mayuram,
Akash Saxena,
Namrata Kaushik
Contents:
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Term Definitions
Problem Definition
Data Retrieval
Source Modeling
Data Cleaning
Data Integration
Data Visualization
Term Definitions
Mash up
• Heterogeneous data sources combined to suite users
needs *
Geospatial
• Data that is geographic and spatial in nature
Information Visualization
• Visualizing large data set in effective and judicious
manner to aid in decision making
Programming-by-demonstration
• Enables user to write programs by demonstrating concrete
examples through UI
Example of geospatial mashups
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WikiMapia (wikimapia.org)
Zillow (Zillow.com)
Yahoo’s Pipes (pipes.yahoo.com)
Intel’s MashMaker (mashmaker.intel.com)
Problem Addressed in Paper
• Existing tools use widgets
• Requires understanding of program concepts
• No customization for data visualization on
final mash up built
• Emergency Management
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Heterogeneous Data Sources
Time sensitive data visualization
Question?
• What are the problems associated with
existing mash up building tools?
a) Uses Widgets which requires programming
concepts
b) No customization for data visualization
c) Heterogeneous data sources
d) All of the above
Ans) d
Motivating Example
Drawbacks
• Time consumption
o Switching between data source
o Analyzing data using Various software
packages
Solution
• Programming by demonstration
• Geospatial Mash up with visualization
techniques
Geospatial Mash up Developed for
Analyst’s Scenario
Programming-By-Demonstration
• Advantage
o Saves time in constructing program.
o Making quick decisions by analyzing
data.
o Makes this solution ideal when no time
for training.
Tool: Karma
• Issues in mash-up creation process- Data Retrieval, Source Modeling, Data Cleaning, Data Integration
and Data Visualization.
• Karma solves all above issues in one interactive
process
Question?
• Question) Karma has the ability to work with
excel, text, database, semi-structured data
a) True
b) False
Ans) True
Data Retrieval
• The searching, selecting, and retrieving of
actual data from a personnel file, data bank, or
other file.
• In karma
Figure 6: Extracting data from Evacuation Centers List
(CSV Text file) using drag and drop in Karma
Data Retrieval Continued…
• Drag and Drop
• Constructs query to get similar data.
• Extracting semi-structured data using
wrappers.
 S/W Fetch Agent Platform
 Open Kapow
• Hence, a unified platform for accessing
and extracting data from heterogeneous
data sources.
Source Modeling
• Process of learning Underlying model
of data source with help of semantic
matching
• In Karma
o User
input by selecting the existing semantic type
ranked by previous learning/hypothesis
o Or user defines new semantic type
o Karma learns and maintains repository of these
learnt semantic types.
o Semantic type is a description of attribute that
helps in identifying the behavior of an attribute.
Data cleaning
• The act of detecting and correcting corrupt or
inaccurate records from a record set, table, or
database.
• Join operation aids data cleaning process.
• In karma user specifies how clean data should
be.
Figure 7:
Analyst provides example of cleaned data
in Karma during data cleaning
Data Integration
• Process of combining the data from multiple
sources to provide a unified view of data.
• Major challenge here is to identify related
sources being manipulated for the process of
integration.
• In karma
o
Automatic detection and ranking relation with other
sources based on attribute names and matching
semantic types.
Data Integration
• Default weights change based on learning.
Figure 8: Data Integration in Karma
Question?
• In what sequence is the mash up built in Karma?
a) Data Retrieval -> Data Cleaning -> Data Integration ->
Source modeling -> Data Visualization
b) Data Retrieval -> Source modeling -> Data Cleaning ->
Data Integration -> Data Visualization
c) Data Cleaning -> Source modeling -> Data Cleaning ->
Data Integration -> Data Visualization
Ans) a
Data Visualization
• Advantages
o Detecting patterns
o Anomalies
o Relationship Between data
o Lowers the probability of incorrect decision
making
o Harness the capabilities of human visual system.
o Related factors
 Structure of underlined data set
 Task at hand
 Dimension of display
Figure 9: Statistical Data in Table Format
Figure 10: Statistical Data Visualized as Chart
Figure 11:
Sample data elements are dragged to the List Format interactive pane for bulleted list visualization.
A preview is also generated in the output preview window.
Figure 12: Data Visualization in Chart Format
Figure 13: Data Visualization in Paragraph Format
Figure 14: Data Visualization in Table Format
Figure 15: Data Visualization in List Format
Visualization in Karma
• Karma uses Google charts API that lets users
generate charts dynamically.
• Uses semantic type generated during semantic
mapping
• In geo spatial mash up this info appears as pop
ups of markups.
Similar Tools
• MIT’s Simile
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Emphasizes on Data Retrieval process
• CMU’s Marmite
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Has Widget approach, user requires Programming Knowledge
• Intel’s Mash Maker
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Browser extension, mash up on only current site.
Data retrieval is limited to web pages & integration
requires expert user.
• All the above tools lack the data visualization feature.
Karma’s Contribution..
• Programming-by-demonstration approach to
data visualization.
o
User can customize the output with out any
knowledge of programming.
• Mash up in one seamless interactive process
• solving all issues, including data visualization
the way user wants.
Future Work
• To include more visualization formats such as
scatter plots, 2D/3D iso surfaces and etc.
• Reading the geo spatial data to integrate with
in karma.
• To save the plans for extracting and
integrating the data, to apply when available.
References
For the working of Karma watch this video
http://www.youtube.com/watch?v=hKqcmsvP0No
• http://mashup.pubs.dbs.unileipzig.de/files/Wong2007Makingmashups
withmarmitetowardsenduserprogrammingfor.pdf
• Paper: Making Mash ups with Marmite: Towards End-User
Programming for the Web - Wong and Hong
• http://www.simile-widgets.org/exhibit/
• Paper: Intel Mash Maker: Join the Web - rob ennals, Eric Brewer,
Minos Garofalakis, Michael Shadle, Prashant Gandhi
Web-a-where: Geotagging Web
Content
Authors:
Einat Amitay, Nadav Har’El, Ron Sivan, Aya
Soffer
Contents
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Motivation
Problem
Ambiguity tackling till now
Tool: Web-a-Where
Page Focus Algorithm
Motivation
• Understanding place names benefits
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Data Mining Systems
Search Engines
Location-based services for mobile devices
Every page have 2 types of Geography associated with
it: source and target
Problem
• Ambiguity of place names
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Name of person (Jack London) and place name
Multiple places having same name i.e.US has 18 cities
named Jerusalem
Web Data to be processed huge so ambiguity resolution
should be fast
Ambiguity Tackling Till now
• NER(Name Entity Recognition)
o Uses Natural Language Processing with statistical-learning
o Machine learning from structure and context expensive require more
training data
o e.g. Charlotte Best pizza
o Slow for web data mining
• Data Mining
o Grounding/Localization: Using glossaries and gazetteers ( general
knowledge like all places in atlas)
• Plausible principles
o Single sense per discourse (Portland, OR …… Portland,…….)
o Nearby locations in one context (Vienna, Alexandria – Northern
Virginia)
• Web Pages
o URL, Language written in, phone numbers, zip codes, hyper link
connection
o Requires a lot of information about postal details, phone directories
easily available in US than other parts of world
Tool: Web-a-Where
• 3 Step processing to process any page
• Spotting: Identify geo location
o Finds and disambiguates geographic names
( taxonomy approach) with help of gazetteer
• Disambiguation: Assign meaning and confidence
• Focus Determination: Derive focus (Aggregate spots
and represent geographic focus of whole page)
• Most of the work is theoretical but in this paper
experimental proof of effectiveness is provided for
the tool.
Gazetteer
• To resolve disambiguate associates place with
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canonical taxonomy node (Paris/France/Europe)
abbreviations(Alabama, AL),
world co-ordinates and
population
Geo/non-geo –e.g. Different languages -“Of” (Turkey)
 Mobile is considered non-geo unless followed by
Alabama.
 Resolved by frequency and if not capitalized e.g.
Asbestos(Quebec)
 More frequency directly related to population – Metro ,
Indonesia
 Short abbreviations not used- Too ambiguousIN(Indiana or India). But helps in disambiguate other
spots like “Gary, IN”
Disambiguating Spots
Algorithm Steps:
1.Assigning confidence
 e.g. IL, Chicago (confidence=0.9) & London,
Germany (unassigned confidence)
2.Unresolved spots assigned confidence=0.5 to places
with largest population
3.Single Sense per discourse, Delegate qualified spot
confidence(0.8 to 0.9)
4.Diambiguating Context : Spots with confidence
<(0.7) context of the region considered.
 e.g. page data “London and Hamilton”
 resolved by London -> England, UK &
Ontario, Canada
 Hamilton -> Ohio, USA & Ontario, Canada
Page Focus
• Decides geographic mentions are incidental and which
constitute actual focus of the page
 Rationale of focus Algorithm
 e.g.- Search = California => page containing cities of
California rather than page containing San José,
Chicago and Louisiana
• Several regions of focus e.g. News mentioning 2 countries
• Coalesce into one region e.g. page listing 50 US-states have
page focus US
• Coalescing into continents not productive
• Page focus assigns higher weight if previous disambiguation
algorithm assigned high confidence and vice-versa
Outline of focus algorithm
• Mainly involves summing of taxonomy node
• E.g. Page contains :
 Orlando,Florida (Confidence 0.5)
 3 times Texas(Confidence 0.75)
 8times Fort Worth/Texas(0.75)
 Final scores:
 6.41 Texas/United States/North America
 4.50 Fort Worth/Texas/United States/North
America
 1.00 Orlando/Florida/United States (Second
Focus)
Focus Scoring Algorithm
• Algorithm loops over according to importance
of various levels of taxonomy nodes.
• Algorithm stops after 4 nodes or when the
confidence is lower than a threshold value.
• Algorithm skips over already covered node
o
E.g. United States/North America is contained in
North America
Question
• Focus Scoring Algorithm stops whenA. Confidence is higher than a threshold value
B. Confidence is equal to threshold value
C. Confidence is lower than a threshold value
Ans) C
Testing Page Focus
• Focus-Finding Algo is evaluated in first stage by
comparing its decision to those of human editors.
• Second Stage: Open Directory Project(ODP)
 Is the largest human-edited directory of the Web.
• Random sample of about 20,000 web-pages from
ODP’s Regional section is chosen.
• Web-a-Where is run on this sample and the foci is
compared to those listed in the ODP index.
• Performed quite well. It found a page focus 92%
correct up to country level.
Evaluation of Geotagging Process
• Web-a-Where is tested on three different web-page
collections:
 Arbitrary Collection
 “.GOV Collection”
 “ODP Collection”
• All 3 collections were geotagged with a Web-a-Where
and manually checked for correctness.
• Each geotags was labeled either “correct”, error of
type “Geo/Non-Geo”, error of type “Geo/Geo”, or
error of type “Not in Gazetteer”.
Question?
• Web-a-Where is run on the sample of web
pages and the foci is compared
to those listed in the ODP index
A). True
B). False
Ans) A
Future Work
• Main source of error was due to Geo/Non-geo
ambiguity
o To resolve this rule out all the uncapitalized
words in properly-capitalized text, part-ofspeech tagger
o Based on coordinates of places, linkage
among Web-pages
Thank You!!!