Integrating Geographical Information Systems and Grid Applications

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Integrating Geographical Information Systems and
Grid Applications

• Marlon Pierce
• Contributions Ahmet Sayar, Galip Aydin, Mehmet
  Aktas, Harshawardhan Gadgil
• Community Grids Lab
• Indiana University

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Acknowledgements

• The real work was done by (in alphabetical
  order).
• Mehmet Aktas
• Galip Aydin
• Harshawardhan Gadgil
• Ahmet Sayar
• Project web site
• www.crisisgrid.org
• This work was supported by NASA AIST as part of
  SERVOGrid Complexity Computational Environment

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Geographical Information Systems and Grid
Applications

• Pattern Informatics
• Earthquake forecasting code developed by Prof.
  John Rundle (UC Davis) and collaborators.
• Uses seismic archives.
• Regularized Dynamic Annealing Hidden Markov
  Method (RDAHMM)
• 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
• 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
• Prof. Rundles UC-Davis group
• Used for forecasting
• Uses fault and fault friction input

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GIS Data Grid Work at CGL

• We decided that the Data Grid components of SERVO
  is best implemented using standard GIS services.
• 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)
• Web Feature Service (WFS) data service for
  storing abstract map features
• Supports queries
• Faults, GPS, seismic records
• Web Map Service (WMS) generate interactive maps
  from WFSs and other WMSs.
• 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
• Browse capabilities files.
• We are currently working on these steps
• Improving WFS performance
• Integrating WMS with video streaming
  technologies.
• Implementing Sensor Web Enablement for streaming,
  real-time data.

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Automating Pattern Informatics
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Pattern Informatics (PI)

• 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 gt 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/cgi-bin/fulltext?forma
  tapplication/pdfidentifieroai3AarXiv.org3Acon
  d-mat2F0102032
• PI is being applied other regions of the world,
  and John has gotten a lot of press.
• Google John Rundle UC Davis Pattern Informatics

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Pattern Informatics in a Grid Environment

• PI in a Grid environment
• Hotspot forecasts are made using publicly
  available seismic records.
• Southern California Earthquake Data Center
• Advanced National Seismic System (ANSS) catalogs
• 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
• 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
• Use GIS services to provide the input data, plot
  the output data
• 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.

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Web Map Client
WSDL
Aggregating WMS
Stubs
Stubs
HTTP
SOAP
WSDL
WSDL
REST
WFS Seismic Rec.
WFS State Bounds
WMS OnEarth

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GIS Behind the Scenes

• The web features are served up by a Web Feature
  Service.
• Web Map Service aggregates maps
• NASA OnEarth our own renderings.
• We re-implement Open Geospatial Consortium
  standards using Web Service Standards.
• SOAP messages, WSDL service definitions.
• Will allow us to separate messages from HTTP
  transport layer in future.
• More WMS Info
• http//grids.ucs.indiana.edu/ptliupages/publicatio
  ns/acm-gis-sayar.pdf.
• http//grids.ucs.indiana.edu/ptliupages/publicatio
  ns/Geoinformatics05_asayar.pdf.
• More WFS Info
• http//grids.ucs.indiana.edu/ptliupages/publicatio
  ns/gwpap243.pdf
• More general info, software, demos
  http//www.crisisgrid.org

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Tying It All Together HPSearch

• 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

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WS Context (Tambora)
Data can be stored and retrieved from the 3rd
part repository (Context Service)
WFS (Gridfarm001)
NaradaBroker network Used by HPSearch engines
as well as for data transfer
WMS
Data Filter (Danube)
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 -gt GML

GML (Danube)
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IEISS GUI FOR OVERLAYING OUTAGE AREA ON A MAP
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IEISS Summary

• IEISS simulates power outages resulting from
  damage to electrical and natural gas grids.
• GIS Grid integration is similar to earlier PI
  application.
• Primary differences
• Better support for dynamic GIS service discovery.
• Better integration of distributed state
  monitoring (WS-Context).
• Google map clients as well as modified PI clients.

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IEISS Step by Step (Note Fig starts as 0)

• 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 users 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

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IEISS Steps Continued

• 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/Sess
  ionStatus) 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.

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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
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Overlaid Outage Area - I

• Basic Steps
• 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

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Overlaid Outage Area - II

• Basic Steps
• 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

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Overlaid Outage Area - III

• Basic Steps
• 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

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Getting Info about specific EP Data by clicking
on the map

• Basic Steps
• 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

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Google Hybrid Map and Feature Information call
to WMS
Natural Gas Layer
Electric Power Layer
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Support for Real Time Applications
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RDAHMM GPS Time Series SegmentationSlide
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 120-250)
• Hector Mine earthquake (day 626)
• Noisy period at end of time series

• 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

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Towards Real-Time RDAHMM

• 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.
• HPSearch can help
• But first we must get the data to RDAHMM.

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NaradaBrokering Message Transport for
Distributed Services

• NB is a distributed messaging software system.
• http//www.naradabrokering.org
• NB system virtualizes transport links between
  components.
• Supports TCP/IP, parallel TCP/IP, UDP, SSL.
• See e.g. http//grids.ucs.indiana.edu/ptliupages/p
  ublications/AllHands2005NB-Paper.pdf for
  trans-Atlantic parallel tcp/ip timings.

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SOPAC GPS Services
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GIS and Collaboration Tools
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GIS and Collaboration

• 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.

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Challenges for Geographical Information System
Grids

• Must address performance issues.
• 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
• Stream archiving and playback, session
  management, software multicasting.
• Applies to both data streams (GPS) and maps
  (streaming video).