focus study: mining on the grid with adam
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Focus Study: Mining on the Grid with ADaM. Sara Graves Sandra Redman Information Technology and Systems Center and Information Technology Research Center University of Alabama in Huntsville National Space Science and Technology Center 256-961-7806 [email protected] - PowerPoint PPT PresentationTRANSCRIPT
Focus Study:Mining on the Grid with
ADaMSara Graves
Sandra RedmanInformation Technology and Systems Center
andInformation Technology Research Center
University of Alabama in HuntsvilleNational Space Science and Technology
Center256-961-7806
[email protected]@itsc.uah.edu
www.itsc.uah.edu
Data Mining
• Automated discovery of patterns, anomalies from vast observational data sets
• Derived knowledge for decision making, predictions and disaster response
http://datamining.itsc.uah.edu
Creating a Successful Environment for Data Mining
Creating a Successful Environment for Data Mining
Provide scientists with the capabilities to allow the flexibility of creative scientific analysis
Provide data mining benefits of Automation of the analysis process Reducing data volume
Provide a framework to allow a well defined structure to the entire process
Provide a suite of mining algorithms for creative analysis that can adapt to new hypotheses
Provide capabilities to add science algorithms to the environment
Exploit emerging technologies in computational and data grids, high-performance networks, and collaborative environments
• Develop and document common/standard interfaces for interoperability of data and services
• Design new data models for handling
• real-time/streaming input
• data fusion/integration
• Design and develop distributed standardized catalog capabilities
• Develop advanced resource allocation and load balancing techniques
• Exploit the grid concept for enhanced data mining functionality
• Develop more intelligent and intuitive user interfaces
• Integrate with collaborative environments
• Develop ontologies of scientific data, processes and data mining techniques for multiple domains
• Support language and system independent components
• Incorporate data mining into science and engineering curricula
Challenges for Next-generation MiningChallenges for Next-generation Mining
Algorithm Development and Mining System (ADaM) - System Overview
Consists of over 100 interoperable mining and image processing components
Each component is provided with a C++ application programming interface (API), an executable in support of scripting tools (e.g. Perl, Python, Tcl, Shell)
ADaM components are lightweight and autonomous, and have been used successfully in a grid environment (NASA IPG, TeraGrid, lab)
ADaM has several translation components that provide data level interoperability with other mining systems (such as WEKA and Orange), and point tools (such as libSVM and svmLight)
Web service interfaces in development Executes in multiple environments (e.g. workstation,
cluster, grid, on-board, etc.) NMI Integration Testbed test cases
MEADModeling Environment for Atmospheric
Discovery
One of the NSF PACI Alliance research Expeditions
Expeditions ensure intense collaboration among technology developers and application scientists and focus on the deployment of infrastructure that supports computational science and engineering and science in a variety of disciplines
MEAD’s focus is on retrospective analysis of hurricanes and severe storms using the TeraGrid, integrating computation, grid workflow management, data management, model coupling, data analysis/mining, and visualization
MEAD Mining Example:Mesocyclone Detection Algorithm
Science Objective:– To investigate different thunderstorm cell
interactions favorable for subsequent tornado (mesocyclone) formation
Goals:– Develop a mesocyclone detection algorithm (in both
2D and 3D)– Develop an algorithm to track the temporal evolution
of the mesocyclone features– Investigate the use of clustering techniques to:
Summarize differences in simulation runs Provide an overview of all the simulations
Approach
Mining Approach– Use idealized WRF model simulations with
different initial conditions– Create a large parameter space of thunderstorm
cell interaction and storm behavior– Mine this search space for patterns and trends
Grid Approach– Application scripts developed in Python and tested
on linux; modified for Globus environment by writing a simple Globus RSL file
– Application scripts constructed to run each combination of tools in parallel on a different node on the grid
Example MEAD Workflow
Initial Data and
Parameters
Initial Data and
Parameters
Multiple WRF Models
(Weather)
Multiple ROMS Models
(Ocean)
Data Mining (ADaM)
Visualization
Inter-model communications
Initial Setup Model Execution Post Run Analysis
ModelResults
ModelResults
Grid environment supports the demanding computational, data storage and post analysis requirements
Using the TeraGrid
Excellent user documentation at http://www.teragrid.org/userinfo/
Account Management - Procedures vary per site– Get account at each site – Obtain certificate (from one of several sites, X.509 or KX.509)– Establish Distinguished Name in grid-mapfile at each site– Create certificate proxy (grid-proxy-int, MyProxy, kinit)
Programming Environment – Know your systems– Compilers (you have a number of choices)– Environment Variables (SoftEnv)– Message Passing (several flavors available)
Executing Jobs– Condor-G– Globus
WRF Initializations
• 230 WRF runs were made, + two control (single-cell)• Each corresponded to a particular arrangement of a pair of initial storm cells
• In figure at left:• Each square: 1 simulation• 1st storm in the middle;• 2nd at one of blue squares• Center cell strongerMatrix of WRF simulations
Slide Source: Brian Jewett
Example: Tracking Results
Mesocyclone Detection and Tracking Results
Features with time durations of a single time step are filtered out
Summary – Mesocyclone Detection Number of mesocyclones with higher duration tend
to be associated with initializations where the second cell is closer to the first
Mesocyclones found in the storm simulations are sensitive to the particular arrangement of a pair of initial storm cells (secondary storm placement at 45 degrees to the primary storm)
Clustering techniques are useful– Summarize differences in simulation runs– Provide an overview of all the simulations
Limitations of Clustering algorithms– Investigated K-Means, Dbscan, Maximin and Hiearchical
Clustering Algorithms– K-Means clustering quality is inferior but provides useful
cluster centers or profiles
LEAD Linked Environments for Atmospheric
Discovery
A cyberinfrastructure for mesoscale meteorology
– real-time, on-demand, and dynamically adaptive needs for mesoscale weather research
– High volume data sets and streams
– Computationally demanding numerical models and data assimilation systems
LEAD
NSF Information Technology Research (ITR) program
Multi-Disciplinary team contributing expertise in meteorological applications, analysis tools, forecast tools, data distribution and management, portal development, workflow orchestration, education and outreach
LEAD
An integrated framework for identifying, accessing, preparing, assimilating, predicting, managing, analyzing, mining, and visualizing meteorological data, independent of format and physical location
Dynamic workflow orchestration and data management are key elements
LEAD GWSTBsGrid and Web Services Testbeds
– Local User Environment – customized portal, control of information flows, collaboration tools, managing processes
– Productivity Environment – models, tools, and algorithms
– Data Services Environment – data transport, data formatting, and interoperability
– Distributed Technologies Environment – workflow infrastructure to autonomously acquire resources and adapt to changing plans
– Data Archive – recent and historical data, products, and tools
The Portal as a Grid Access Point
The Portal Server provides the users Grid Context.
SecuritySecurityData Management
Service
Data ManagementService
AccountingService
AccountingServiceLogging
Logging
Event ServiceEvent Service
PolicyPolicy
Administration& Monitoring
Administration& Monitoring
Grid OrchestrationGrid Orchestration
Registries andName binding
Registries andName binding
Reservations And Scheduling
Reservations And Scheduling
Open Grid Service Architecture Layer
Web Services Resource Framework – Web Services Notification
OGCE or GridSphere
Grid Portal Server
OGCE or GridSphere
Grid Portal Server
https
Physical Resource Layer
SOAP & WS-Security
Services Oriented Architecture
User interfaces with portal via browser Portal provides tools for users to build and
launch workflows Portlets (JSR-168) provide interface between
user and grid services Applications can be wrapped as services via a
Portal Factory Service Generator – Requires application, script to run it, input
parameters, output parameters– Write an AppService document and upload to
Portal Factory Service Generator (in portal)– Service is created as well as the portal client
interface Security model integral to design
Data Integration and Mining: From Global Information to Local Knowledge
Precision Agriculture
Emergency Response
Weather Prediction
Urban Environments
Bioinformatics