A Cloudy View on Computing workshop and CReSIS Field Data Accessibility Jerome Mitchell1, Jun Wang1, Geoffrey Fox1, Linda Hayden2 Indiana University1, Elizabeth City State University2

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A Cloudy View on Computing workshop and CReSIS
Field Data Accessibility Jerome Mitchell1,
Jun Wang1, Geoffrey Fox1, Linda Hayden2 Indiana
University1, Elizabeth City State University2

• Workshop
• Details
• Who Association of Computer/Information
  Sciences and Engineering Departments at Minority
  Institutions (ADMI) faculty/students
• Where Elizabeth City State University (ECSU)
• When June 7 - July 5 2011
• What A Teach-One-Teach-Many approach to cloud
  computing
• Purpose
• Introduce ADMI to the basics of the emerging
  Cloud Computing paradigm
• Understand the computer systems constraints,
  tradeoffs, and techniques of setting up and using
  cloud
• Understand how different algorithms can be
  implemented and executed on cloud frameworks
• Evaluating the performance and identifying
  bottlenecks when mapping applications to the
  clouds

• Compute Resources
• FutureGrid
• Virtual machines virtual networking to create
  sandboxed modules
• Virtual Grid appliances self-contained,
  pre-packaged execution environments
• Group VPNs simple management of virtual clusters
  by students and educators

Cloud GIS Distribution Service

• Google Earth Example
• 2009 Antarctica Season

• SpatiaLite Database
• Spatial extension to manages both vector and
  raster data and supports a rich set of GIS
  analysis functions through SQL.
• The data can be directly accessed through GIS
  software and MATLAB
• SpatiaLite Database Example
• 2009 Antarctic flight path data
• 4 million entries - originally stored as 828
  separate files and imported into one SpatiaLite
  database file

Overview of 2009 Flight Paths
Data Access for Single Frame

• CReSIS Field Data Accessibility
• Current CReSIS Data Organization
• CReSISs data products website lists
• direct download links for individual files
• The data are organized by season
• Seasons are broken into data segments
• Data segments are arranged into frames
• Associated data for each frame are stored in
  different file formats
• CSV (flight path)
• MAT (depth sounder data)
• PDFs (image products)
• File-based data system has no spatial data access
  support
• Spatial Data Accessibility Project
• Two main components Cloud distribution service
  and special service for PolarGrid field crew.
• Data is supported among multiple spatial
  databases.

2009 Antarctica Season Vector Data
Visual Crossover Analysis for Quality Control
(development project)
Flight path data stored as YYYYMMDD_segID_frameID.
txt SQLite command to create the segs
table CREATE TABLE segs ( UTCTime Number,
Thickness Number, Elevation Number, FrameID
VARCHAR(12), Surface Number, Bottom Number,
QualityLevel Integer) SELECT AddGeometryColumn
('segs','geometry',4326,'POINT',2) note
geometry 2 -gt xy, (longitude, latitude), 4326 -gt
WGS84 coordinate system
SpatiaLite MATLAB Direct Access Mksqlite
package a MEX-DLL to access SQLite databases
from MATLAB http//mksqlite.berlios.de/ Add this
flag to build.m to enable SQLite to load
SpatiaLite as an extension -DSQLITE_ENABLE_LOAD_
EXTENSION1 Testing in MATLAB dbid
mksqlite(0,'open', test.sqlite' ) sql 'SELECT
load_extension(''', path_to_spatialite,
''')' mksqlite(dbid, sql) load
extension mksqlite(dbid, 'SELECT
sqlite_version()') mksqlite(dbid, 'SELECT
spatialite_version()') mksqlite(dbid, 'SELECT
X(geometry) as lon, Y(geometry) as lat from segs
where FrameID2009101601001') mksqlite(dbid,
'close')
References PolarGrid Data Products
https//www.cresis.ku.edu/data SpatiaLite
http//www.gaia-gis.it/spatialite/ Quantum GIS
http//www.qgis.org/