Transcript Document

Integrating Geographical
Information Systems and Grid
Applications
Marlon Pierce
Contributions: Ahmet Sayar, Galip Aydin, Mehmet Aktas,
Harshawardhan Gadgil
Community Grids Lab
Indiana University
Acknowledgements
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The real work was done by (in alphabetical
order).
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Project web site:
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Mehmet Aktas
Galip Aydin
Harshawardhan Gadgil
Ahmet Sayar
www.crisisgrid.org
This work was supported by NASA AIST as part
of “SERVOGrid: Complexity Computational
Environment”
Geographical Information Systems and
Grid Applications
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Pattern Informatics
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Regularized Dynamic Annealing Hidden Markov Method (RDAHMM)
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Time series analysis code by Dr. Robert Granat (JPL).
Can be applied to GPS and seismic archives.
Can be applied to real-time data.
Interdependent Energy Infrastructure Simulation System (IEISS)
GeoFEST
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Earthquake forecasting code developed by Prof. John Rundle (UC Davis) and
collaborators.
Uses seismic archives.
Finite element method code developed by Dr. Jay Parker (JPL) and Prof. Greg
Lyzenga (JPL/Harvey Mudd College)
Uses fault models as input.
Virtual California
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Prof. Rundle’s UC-Davis group
Used for forecasting
Uses fault and fault friction input
GIS Data Grid Work at CGL
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We decided that the Data Grid components of SERVO is best implemented
using standard GIS services.
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Use Open Geospatial Consortium standards
Provide downloadable GIS software to the community as a side effect of SERVO
research.
We implemented two cornerstone standards as Web Services (WS-I+
approach)
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Web Feature Service (WFS): data service for storing abstract map features
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Web Map Service (WMS): generate interactive maps from WFS’s and other
WMS’s.
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Can be used to set up problems by extracting features (faults, seismic events,
etc) from user GUIs to drive problems such as the PI code and (in near future)
GeoFEST, VC.
We also built a GIS compatible UDDI and WS-Context
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Supports queries
Faults, GPS, seismic records
Browse capabilities files.
We are currently working on these steps
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Improving WFS performance
Integrating WMS with video streaming technologies.
Implementing Sensor Web Enablement for streaming, real-time data.
Automating Pattern
Informatics
Pattern Informatics (PI)
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PI is a technique developed at University of California, Davis for
analyzing earthquake seismic records to forecast regions with
high future seismic activity.
 They have correctly forecasted the locations of 15 of last 16
earthquakes with magnitude > 5.0 in California.
See Tiampo, K. F., Rundle, J. B., McGinnis, S. A., & Klein, W.
Pattern dynamics and forecast methods in seismically active
regions. Pure Ap. Geophys. 159, 2429-2467 (2002).
 http://citebase.eprints.org/cgibin/fulltext?format=application/pdf&identifier=oai%3AarXiv.org%
3Acond-mat%2F0102032
PI is being applied other regions of the world, and John has
gotten a lot of press.
 Google “John Rundle UC Davis Pattern Informatics”
Pattern Informatics in a Grid Environment
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PI in a Grid environment:
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Hotspot forecasts are made using publicly available seismic records.
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Code location is unimportant, can be a service through remote execution
Results need to be stored, shared, modified
Grid/Web Services can provide these capabilities
Problems:
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Southern California Earthquake Data Center
Advanced National Seismic System (ANSS) catalogs
How do we provide programming interfaces (not just user interfaces) to the above
catalogs?
How do we connect remote data sources directly to the PI code.
How do we automate this for the entire planet?
Solutions:
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Use GIS services to provide the input data, plot the output data
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Web Feature Service for data archives
Web Map Service for generating maps
Use HPSearch tool to tie together and manage the distributed data sources and
code.
Web Map
Client
WSDL
Aggregating
WMS
Stubs
Stubs
HTTP
SOAP
WSDL
WSDL
WFS
+
Seismic Rec.
WFS
+
State Bounds
“REST”
…
WMS
+
OnEarth
GIS Behind the Scenes
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The web features are served up by a Web Feature Service.
Web Map Service aggregates maps
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We re-implement Open Geospatial Consortium standards using Web
Service Standards.
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http://grids.ucs.indiana.edu/ptliupages/publications/acm-gis-sayar.pdf.
http://grids.ucs.indiana.edu/ptliupages/publications/Geoinformatics05_asayar.pd
f.
More WFS Info:
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SOAP messages, WSDL service definitions.
Will allow us to separate messages from HTTP transport layer in future.
More WMS Info:
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NASA OnEarth + our own renderings.
http://grids.ucs.indiana.edu/ptliupages/publications/gwpap243.pdf
More general info, software, demos: http://www.crisisgrid.org
Tying It All Together: HPSearch
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HPSearch is an engine for orchestrating distributed Web Service
interactions
 It uses an event system and supports both file transfers and data
streams.
 Legacy name
HPSearch flows can be scripted with JavaScript
 HPSearch engine binds the flow to a particular set of remote
services and executes the script.
HPSearch engines are Web Services, can be distributed
interoperate for load balancing.
 Boss/Worker model
ProxyWebService: a wrapper class that adds notification and
streaming support to a Web Service.
More info: http://www.hpsearch.org
Data can be stored and
retrieved from the 3rd part
repository (Context Service)
WS Context
WFS
(Tambora)
(Gridfarm001)
NaradaBroker network:
Used by HPSearch
engines as well as for
data transfer
WMS
Data Filter
HPSearch
(Danube)
(TRex)
Virtual
Data
flow
WMS submits script
execution request
(URI of script,
parameters)
HPSearch hosts an
AXIS service for
remote deployment of
scripts
PI Code Runner
(Danube)
 Accumulate Data
 Run PI Code
 Create Graph
 Convert RAW -> GML
HPSearch
(Danube)
GML
(Danube)
Actual Data flow
HPSearch controls the Web services
Final Output pulled by the WMS
HPSearch Engines
communicate using NB
Messaging
infrastructure
IEISS GUI FOR OVERLAYING
OUTAGE AREA ON A MAP
IEISS Summary
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IEISS simulates power outages resulting from
damage to electrical and natural gas grids.
GIS Grid integration is similar to earlier PI
application.
Primary differences:
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Better support for dynamic GIS service discovery.
Better integration of distributed state monitoring
(WS-Context).
Google map clients as well as modified PI clients.
4-5
- WFS
publishes
the
as
GML
document
into a topic
WFS
1-2-3
6
- User
and
- WMS
invokes
WMS
Client
publish
IEISS
->results
through
WMS
their
Server
WSDL
WMSFeatureCollection
->
Client
URL
UDDI
to
interface
->
the
WFS
UDDI
for the
Registry
obtained
(“/NISAC/WFS”)
in a pub/sub
based
messaging
WFS session
-> WMS in
Server
geospatial features,
and WMS
Client
starts system.
a workflow
the
(creates
map overlay) and IEISS receive this GML document. WMS Server ->
Context aService.
WMS Client (displays it)
7 - On receiving invocation message, IEISS updates the shared state data
to be “IEISS_IS_IN_PROGRES”. IEISS runs and produces an ESRI
Shape file and then invokes shp2gml tool to convert produced Shape
file to GML format. After the conversion IEISS updates shared session
state to be “IEISS_COMPLETED”. As the state changes, the Context
Service notifies all interested workflow entities such as WMS Client.
9-10
- WFS-L
publishes
the IEISSWMS
output
as amakes
GML FeatureCollection
8 – On
receiving
the notification,
Client
a request to the
document
topicoutput
‘NISAC/WFS-L’. WMS Server is subscribed to
WFS-L for to
theNB
IEISS
this topic and receives the GML file then converts it to map overlay,
and the Client displays the new model on the map.
(Next set shows nonslideshow version)
IEISS Step by Step (Note Fig starts as 0)
1.
2.
3.
4.
5.
6.
WFS and WMS publish their WSDL URL to the UDDI Registry.
User starts the WMS Client on a web browser; the WMS Client displays the
available features. User submits a request to the WMS Server by selecting
desired features and an area on the map.
WMS Server dynamically discovers available WFSs that provide requested
features through UDDI Registry and obtains their physical locations (WSDL
address).
WMS Server forwards user’s request to the WFS.
WFS decodes the request, queries the database for the features and
receives the response.
WFS creates a GML FeatureCollection document from the database
response and publishes this document to NaradaBrokering topic
‘/NISAC/WFS’; WMS Server and IEISS receive this GML document.
WMS Server creates a map overlay from the received GML document and
sends it to WMS Client which in turn displays it to the user.
After receiving the GML document IEISS NB Subscriber invokes
gml2model tool; this tool converts GML to XML Model format to be
processed by IEISS
IEISS Steps Continued
7.
8.
9.
10.
11.
User invokes IEISS through WMS Client interface for the obtained geospatial
features, and WMS Client starts a workflow session in the Context Service. On
receiving invocation message, IEISS updates the shared state data for the
workflow session to be “IEISS_IS_IN_PROGRES” on the Context Service. Both
IEISS and WMS Client communicate with Context Service via asynchronous
function calls by utilizing Context Respond Handler Service. IEISS runs and
produces an ESRI Shape file that has the outage areas for the given region.
IEISS invokes shp2gml tool to convert produced Shape file to GML format [Fig.3].
After the conversion IEISS updates shared session state to be
“IEISS_COMPLETED”. As the state changes, the Context Service notifies all
interested workflow entities such as WMS Client. To notify WMS-Client, the
Context Service publishes the updates to a NB topic
(/NISAC/Context://IEISS/SessionStatus) from which the WMS-Client receives
notifications.
WMS makes a request to the WFS-L for the IEISS output.
WFS-L publishes the IEISS output as a GML FeatureCollection document to NB
topic ‘NISAC/WFS-L’.
WMS Server is subscribed to this topic and receives the GML file then converts it
to map overlay,
WMS Client displays the new model on the map.
Electric Power and Natural Gas data
Zoom-in
Zoom-out
FeatureInfo mode
Measure distance mode
Clear Distance
Drag and Drop mode
Refresh to initial map
Overlaid Outage Area - I
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Basic Steps:
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Select Energy Power
AND Natural Gas Data
and Update Layer List
rendered on the map
Click on “Overlay
Outage” button
See the outage area on
the map
Overlaid Outage Area - II
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Basic Steps:
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Select Energy Power Data
and Update Layer List
rendered on the map
Click on “Overlay Outage”
button
Use zoom-in mapping tool
below to get same outage
area in more detail
See the outage area on the
map
Overlaid Outage Area - III
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Basic Steps:
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Select Energy Power and
Natural Gas Data and
Update Layer List rendered
on the map
Select St. Petersburg from
the “Area of Interest”
dropdown list.
Click on “Overlay Outage”
button.
See the outage area on the
map
Getting Info about specific EP Data by clicking
on the map
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Basic Steps:
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Select Energy Power Data and
Update Layer List rendered on
the map
Select (i) from the mapping
tools below.
Click on any feature data on
the map.
See the information for
selected feature in pop-up
window
Google Hybrid Map and
Feature Information call to WMS
Natural Gas Layer
Electric Power Layer
Support for Real Time
Applications
RDAHMM: GPS Time Series Segmentation
Slide Courtesy of Robert Granat, JPL
GPS displacement (3D)
length two years.
Divided automatically
by HMM into 7 classes.
Features:
• Dip due to aquifer
drainage (days 120250)
• Hector Mine
earthquake (day 626)
• Noisy period at
end of time series
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Complex data with subtle signals is difficult for humans to
analyze, leading to gaps in analysis
HMM segmentation provides an automatic way to focus attention
on the most interesting parts of the time series
Towards Real-Time RDAHMM
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A real-time version of RDHAMM could potentially be
used to detect state change events in live data from
a GPS station.
SCIGN maintains 125+ GPS stations, so trivially
parallel RDAHHM clones can monitor state changes
in the entire network.
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HPSearch can help
But first we must get the data to RDAHMM.
NaradaBrokering: Message Transport for
Distributed Services
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NB is a distributed messaging
software system.
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NB system virtualizes transport
links between components.
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http://www.naradabrokering.or
g
Supports TCP/IP, parallel
TCP/IP, UDP, SSL.
See e.g.
http://grids.ucs.indiana.edu/ptli
upages/publications/AllHands2
005NB-Paper.pdf for transAtlantic parallel tcp/ip timings.
SOPAC GPS Services
GIS and Collaboration Tools
e-Annotation
Player
Archieved
stream list
Archived stream
player
Real time
stream list
Annotation/WB
player
Real time stream
player
e-Annotation
Whiteboard
GIS and Collaboration
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The previous slide illustrates an initial interface for capturing,
annotating, and storing/replaying video streams.
Still images can be captured and annotated on shared white
board.
Annotations are stored along with rest of system.
Collaborative and Synchronous
Annotation & Discussion
Collaborative Communication
e
tiv n
ora icatio
b
lla n
Co mmu
Co
Streaming
Servers
Master/Coach
broker
broker
archive
NaradaBrokering
Student
broker
broker
broker
e-Annotation
Portal Server
broker
Student
Ar
Collaborative Communication
Collaborative e-Annotation Player
Student
ch
iv
e/R
etr
iev
e
G
L
O
B
A
L
M
M
C
S
m
trea
TV
Live
stre
am
Capture Device
Archived Real Time (Live) Stream
From TV and Capture Devices
Collaborative e-Annotation Whiteboard
Archived Streams
Stream Annotation Snapshots
Instant Messenger
Real Time (Live) Stream Player
s
TV
Storage Servers
Challenges for Geographical Information
System Grids
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Must address performance issues.
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Related workshop at GGF 15.
HTTP is not an adequate transport mechanism for moving
data around.
XML representations, compression, etc.
Well established techniques from real-time
collaboration can be applied to sensors
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Stream archiving and playback, session management,
software multicasting.
Applies to both data streams (GPS) and maps (streaming
video).