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

sgraves@itsc.uah.edusredman@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