Transcript mksqlite(dbid, `SELECT spatialite_version()`)

A Cloudy View on Computing
Workshop and CReSIS Field Data
Accessibility
Jerome E. Mitchell
Indiana University
A Cloudy View on Computing
Workshop
Workshop Details
• Who:
– > 10-12 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
• Preliminary discussion on workshop at ADMI 2011 Conference (April 14-16, 2011)
Workshop Purpose
• Introduce ADMI to the basics of the emerging Cloud
Computing paradigm
– Learn how it came about
– Understand its enabling technologies
– Understand the computer systems constraints, tradeoffs, and
techniques of setting up and using cloud
• Teach ADMI how to implement algorithms in the Cloud
– Gain competence in cloud programming models for distributed
processing of large datasets.
– Understand how different algorithms can be implemented and executed
on cloud frameworks
– Evaluating the performance and identifying bottlenecks when mapping
applications to the clouds
Workshop Components
What we are trying to answer?
What is Cloud
Computing?
Why Cloud
Computing?
What are its
challenges and
opportunities?
How does
Cloud
Computing
work?
Workshop Schedule
End of First Week
Now I
understand
Cloud Computing
Parallel
Processing
Programming Model
Now I
appreciate
why Cloud
Computing
is important
Functional
Programming
Used by
Map
/Reduce
End of Third Week
CGL’s
implementation
Parallelized by
Apache’s
implementation
Twister
Algorithm
Now I really
understand
Cloud
Computing!
Hadoop
End of Fifth Week
T
i
m
e
I
i
n
e
Experimenting With…
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
References
• D. Wolinsky, A. Prakash, and R. Figueiredo.
Experiences with Self-Organizing, Decentralized Grids
Using the Grid Appliance.
• J. Ekanayake, H. Li, B. Zhang, T. Guanarathne, S. Bae,
J. Qiu, and G. Fox. Twister: A runtime for iterative
MapReduce. In Proceeding of the 19th ACM
International Symposium on High Performance
Distributed Computing, pages 810 – 818. ACM, 2010.
• Apache Hadoop, Retrieved Aug 20, 2010, from ASF:
http://hadoop.apache.org/core/
• FutureGrid: futuregrid.org
CReSIS Field Data
Accessibility
Current CReSIS Data
Organization
• CReSIS’s 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
Field Data Spatial Access
Project
We are developing a solution based on
SpatiaLite database with neither server nor
Internet requirements, ideal for field use
SpatiaLite Database
• Spatialite
– Spatial extension to the light-weighted SQLite database
– 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
Field Data Access Service
2009 Antarctica Season Vector Data
Visual Crossover Analysis for Quality
Control (in development project)
SpatiaLite Database Example
• 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 -> xy, (longitude, latitude), 4326 -> 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_EXTENSION=1
• 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 FrameID=2009101601001');
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/