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Advanced Topics in Data Mining and Research DirectionsAdvanced Topics in Data Mining and Research Directions

CSE5610 Intelligent Software Systems

Semester 1, 2006

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Outline

• Mining Different Data Types

– Spatial, Temporal, Time Series, Data Streams, Multimedia, XML, Web, Text etc.

• Distributed Data Mining (DDM)

• Mobile & Ubiquitous Data Mining (UDM)

• Data Mining E-Services

• Anytime, Anywhere Data Mining E-Services

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Generations of Data Mining

• Four Generations of Data Mining Systems – Robert Grossman

• First Generation

– Stand Alone, Centralised, Single Algorithm

• Second Generation

– Integration with databases, support for high-dimensionality, complex data types

• Third Generation

– Distribution and Heterogeniety

• Fourth Generation

– Support for mining embedded, mobile and ubiquitous data sources

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Distributed Data Mining

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Distributed Data Mining

• Inherently distributed data

• MNC + Global Markets

• => Physical/geographical separation of users from the data sources

• Traditional data mining model involving the co-location of users, data and computational resources is inadequate

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Distributed Data Mining (DDM)

• The inherent distribution of data and other resources as a result of organisations being distributed.

• The large volumes of data, the transfer of which results in exorbitant communication costs.

• The need to mine heterogeneous data, the integration of which is both non-trivial and expensive.

• The performance and scalability bottle necks of data mining.

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Distributed Data Mining (DDM)

• DDM = Data Mining (DM) + Knowledge Integration (KI)

• DM - Performing traditional knowledge discovery at each distributed data site.

• KI - Merging the results generated from the individual sites into a body of cohesive and unified knowledge.

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Parallel Data Mining (PDM)

• Principal distinction between DDM & Parallel DM– parallel mining involves parallel processors

with or without shared memory

• Parallel data mining also includes development of parallel versions of traditional data mining techniques.

• Can be integration – DecisionCentre

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DDM – Algorithms & Architectures

• Research in distributed data mining can be divided into two broad categories [Fu01]:

• Data Mining Algorithms. – focus on efficient techniques for knowledge

integration.

• Distributed Data Mining Architectures.– focus on development of distributed data mining

architectures

– emphasizes the processes and technologies that support construction of software systems to perform distributed data mining

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Taxonomy of DDM Architectures

Distributed DataMining Systems

Client-Server Agents

Stationary Mobile

Architectures

Self-directedmigration

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Classification – DDM Systems

DDM Architectural Models DDM Systems

Client-server DecisionCentre [CDG99], IntelliMiner [PaS99, PaS01], InterAct [PaD02]

Agents Mobile Agent Stationary Agent

JAM [SPT97], Infosleuth [UMG98, MUU99], BODHI [KPH99], Papyrus [Ram98], PADMA [KHS97a, KHS97b]

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Client-Server DDM

PC Workstation Laptop

Data Mining Sever

DataServer 2

DataTransfer

UserData Mining

Request

DataMiningResults

DataServer 1

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Mobile Agent Model for DDM

PC Workstation

Task Controlling Agent

USERS

Agent SystemData MiningResult Agent

Data MiningResult Agent

DirectoryService

KnowledgeIntegration Agent

Data Resource Agents

DataServer 1

DataServer 1

Laptop

Data Mining Agents

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Hybrid Model for DDM

DDM Server

Agent Centre

DataSource 1

DataSource2

DataSource n

ClientServer

AgentAgent

Optimiser

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Ubiquitous Data Mining

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Ubiquitous Data Mining (UDM)

• Mining data in a resource-constrained environment to support the time critical information needs of mobile users

• Typical Characteristics– Mobile User – frequent disconnections– Handheld Device -

> Resource constraints – memory, battery, processor, screen real-estate

– Time critical– Real-time & On-line – Data Streams

• Example Scenarios

• Many Challenges

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Current Research

• Kargupta’s Group– MobiMine

• @CSSE, Monash Univ.– AgentUDM

– Adapative, Cost-efficient & Light-weight data mining techniques for data streams

> Mohamed Medhat > LWC, LWF & LWClass

> Watch this space!!!

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Data Mining E-Services

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Data Mining E-Services

• “…data analysis and mining functions themselves will be offered as business intelligence e-services that accept operational data from clients and return models or rules”

Umesh Dayal, 2001

•Why? – Knowledge is a key resource – Cost of data mining infrastructure

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Data Mining E-Services

• Current Commercial Landscape– Several ASPs -> DigiMine, Information Discovery,

WhiteCross Systems, ListAnalyst.com etc. etc.

– Mode of Operation

• Hybrid Model & Data Mining ASPs– Optimise Response Time

> Leads to improved throughput

– QoS Estimation

– Location Preferences of Clients

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Data Mining E-Services

• Current Commercial Landscape– Several ASPs -> DigiMine, Information Discovery,

WhiteCross Systems, ListAnalyst.com etc. etc.

– Mode of Operation

• Hybrid Model & Data Mining ASPs– Optimise Response Time

> Leads to improved throughput

– QoS Estimation

– Location Preferences of Clients

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Anytime, Anywhere Data Mining E-Services

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My Thoughts

• Data is a commodity, Analysis is a service

• Access anytime, anywhere• By anyone…

– From large corporations to small business to individuals

• From home buyers to mobile salespersons to grocery shoppers…

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My Thoughts

• A preliminary model for delivery– Datacentric Grids

High Performance Servers

MiningAlgorithms

ModelRepository

Mobile AgentManagement

System

Model Query

Compute NewModel Request

+Remote User

Data

Compute NewModel Request

+User Data

Compute NewModelRequest

Compute NewModelRequest + UserComputation

Data Repository

Data1

Data2

Datan

PrivateDatacentric

Grid

Compute NewModel Request+ User Data +

UserComputation

Datacentric Grid Management Module

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References

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References

• http://www.csse.monash.edu.au/projects/MobileComponents/projects/dame/

• http://www.csse.monash.edu.au/~shonali/research.html

• http://www.csee.umbc.edu/~hillol/DDMBIB/

• http://www.csee.umbc.edu/~hillol/diadic.html

• http://www.csse.monash.edu.au/~mgaber/main.html