distributed data mining research using grids and web - iceage

Session 28: Session 28: Distributed Data Mining Distributed Data Mining

Research using Grids and Web Research using Grids and Web ServicesServices

Author/Presenter: Peter BrezanyAuthor/Presenter: Peter Brezany

University of Vienna, AustriaUniversity of Vienna, Austria

11 July

Motivation

Balatonfüred,Hungary - 6th-18th July 2008 2

Business

Medicine

Scientificexperiments

Simulations

Earth observations

Data and data exploration cloud

Data and data exploration cloud

Outline

Motivation

Selected projects ← Data mining model Towards high productivity analytics Parallel and distributed data mining

and OLAP in GridMiner/ADMIRE projects

Future developments

Selected Projects


A Long-Term Biodiversity, Ecosystem and Awareness Research Network – ALTER-Net


Waste

Air

Soil

Water

Emmision

Bio-diversity

Forests

DistributedData

DistributedData Mining

Flow Analysis

Geo-Statistic

Reporting

PopularPresen-tation

PredictionModels

DistributedApplications

…

Statistic

Common Ontology

Author: Kathi Schleidt


China-Austria Data Grid (CADGrid)

Main Idea: Medical Meridian Measurement Grid (M3G) for On-Line Diagnosis

Diabetic domain is the first domain highly profiting of the project results

Motivation

Meridian-Theory is an important part of Traditional Chinese Medicine (TCM)

Clinical practices of TCM (esp. acupuncture) have been guided by meridian theory for thousands of years

More than 4000 years of experience Knowledge that we should not only use

but also support by modern high-tech measurement and IT technologies

3-Dec-07 CADGrid 7


Meridian-Theory Basics (1)

According to TCM our human body has 14 acupuncture meridians

Secret to our biological and medical knowledge

Each meridian has its main points called source points


Meridian-Theory Basics (2)

Using data mining techniques, correlations between these points can be identified e.g. start-end point correlationsymmetric point correlation

If there was a pain on one place along the meridian, a good effect can be achieved by treating another place on the same line

Meridian-theory Basics (3)

Meridians can transport physical, medical, biological material

and information The characteristics (weaker or stronger

output, time delay, …) gained by the analysis of electro-signals sensed from meridians have a strong relationship with the human body organs (heart, lung, brain,…)

10

Meridian Measurement Methods

1 Active

2 Passive

11

Active Measurement

12

Data-file

Down-flow-key-point

Up-flow-key-point

Human-body-meridianSend

MeasureRecord

Record

Up-flow point: lower electrical potentialDown-flow point: higher electrical potentialFingers and toes: zero potential

Passive Measurement

13

Data-file

MeasureRecord

to the ground of the instrument


Application 1

Non-invasive Glucose Measurement (NIGM)

Meridian Measurement Instrument


The First Prototype


NIGM Workflow


M3G Services for DiabeticsNIGM-Service – Model Setup


M3G Services for DiabeticsNIGM-Service – Use Model


M3G Services for DiabeticsNIGM-Service – Maintain Model


CADGrid Framework

Intelligence Base

21

Future Work

Extension to other domains Brain Informatics

domain

22


BusinessUnderstanding

DataUnderstanding

DataPreparation

Modelling

Evaluation

DeploymentData

CRISP-DM

Towards High Productivity Analytics


A Project Sponsored by

Motivation:

High Productivity Analytics

Our definition:

„A high productive analytics system is one that delivers a high level of performance, guarantees a high level of accuracy of analytics models and other results extracted from analyzed data sets while scoring equally on other aspects, like usability, robustness, system management, and ease of programming.“


High Productivity Analytics Research Agenda

High performance services developed by high productivity languages and tools

Efficient workflow management (building and execution)

Advanced GUI

Illustration on the GridMiner system


GridMiner Data Mining Model


Data

Business understanding

Dataunderstanding

DataPreparation

Modeling

Evaluation

Deployment

CRISP-DM, SPSS

ServiceProvider

ServiceProvider

ServiceProvider

Data provider

Gri

dM

iner User

Virtual Organization

GridMiner Conceptual Architecture


DataWarehouse

Knowledge

Cleaning andIntegration

Selection andTransformation

Data Mining

Evaluation andPresentation

OLAP

Online Analytical Mining

OLAP Queries

Data and functional resources can be geogra-phically distributed – focus on virtualizationand large-scale data mining.

Motivation for large-scale data mining


accu

rac

y

sampled data size

100%

available data size

(qo,mo)

(qo,mo)

qi - data quality

mi - modeling method

(q0,m0)

Assumed

(qo,mo)

(qi,mi)

Service Parallelism Levels


Inter-Service &Intra-ServiceParallelism

Hybrid Programming Model

SPMD – Single Program Multiple Data (used for programming multiprocessor architectures)

+ SSMD – Single Service Multiple Data

(introduced by us for programming service-oriented architectures)


1. Construction of Decision Trees - SPRINT – Scalable PaRallelizable INduction of decision Tree


categoric

al

continuous

class

Splitting Attributes

The splitting attribute at a node is determined by the Gini index.

Out-of-Core Algorithm

Phase 1 - Preparation


Phase 2 - Execution


2. Construction of Neural Networks


Error Back-Propagation

Inputlayer

Outputlayer

Hidden layer

+-

Desiredoutput

Σ

Sum Limiter-sigmoid funct.

Weightedinputs

Outputs

Node

Parallel Algorithm

Challenges Training real NN is extremely computationally

intensive. Many NN practical applications (e.g., speech

and face recognition) involve the large number of adjustable parameters and training patterns to achieve the needed accuracy.

Solution Parallel training algorithms Development of services running in high

performance hardware and software environments


Programming Environment: Titanium

The goals: performance, safety, and expressiveness.

A language that gives its users access to modern program structuring through the use of object-oriented technology, that enables its users to write explicitly parallel code.

Based on a parallel SPMD model of computation with a global address space.

Titanium uses Java as its base, not a strict extension of Java.

Compiler: Titanium C + communicationBalatonfüred,Hungary - 6th-18th July 2008 37

Overview of Distributed Solution


Master

Sub-master 0

Sub-master 1

Slave0

Slave1

Slave2

Slave0

Slave1

Training Datafor

Sub-master 1

Data DistributionScheme 1

Data DistributionScheme 2

Training Datafor

Sub-master 0

The Parallel Implementation


VGE Client

VGE Server

VGE – Vienna Grid Environment

The Distributed & Parallel Implementation


VGE Client

VGE Server

3. On-Line Analytical Processing (OLAP)


a three-dimensional data cube

Distributed OLAP – Aggregation of Compute and Storage Resources


Tuple Stream

OLAP Service


Virtual Cube

Sub Cube

Sub Cube

Slave 1

Slave 3

Master

Data

Sub Cube

Slave 2

Indexes

Index Service

query

answerXML

Workflow Composition Approaches


AnalyticalServices

AnalyticalServices

AnalyticalServices

AnalyticalServices

WorkflowEngine

WorkflowEngine

AnalyticalServices

AnalyticalServices

WorkflowDescriptionWorkflow

Description

Manual Composition

WorkflowEditor

WorkflowEditor

AnalyticalServices

AnalyticalServices

AnalyticalServices

AnalyticalServices

WorkflowComposerWorkflowComposer

Passive Approach

WorkflowEngine

WorkflowEngine

AnalyticalServices

AnalyticalServices

WorkflowDescriptionWorkflow

Description

KBKB

Automated Composition

ReasonerReasoner

ResourcesMonitoringResourcesMonitoring

Active Approach

WorkflowComposerWorkflowComposer

WorkflowEngine

WorkflowEngine

KBKB

AnalyticalServices

AnalyticalServices

AnalyticalServices

AnalyticalServices

AnalyticalServices

AnalyticalServices

ReasonerReasoner

ResourcesMonitoringResourcesMonitoring

GridMiner Workflow Composition Editor

Computational Grid

Data Grid

Data Minig Grid

Semantic Grid – 1st Generation

Current Grids

Next-GenerationGrid

Evolution of the Web

KnowledgeTechnologies

Evolution of HPCNMobileServices

Towards Next-Generation Grids

distributed data mining research using grids and web - iceage

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