Extending Mining Applications Extending Mining Applications towards Web Technology in towards Web Technology in Forest IndustryForest Industry

Research&Development AgendaResearch&Development Agenda

Elena Irina NeagaForac Research ConsortiumLaval University, Québec CityCanadaE-mail: Irina.Elena-Neaga@forac.ulaval.ca

OutlineOutline

• Factors that affect the adoption of DW&DM• Customer Relationship Management• Supply Chain Management• Demand Chain Management• Workflow Concept • Web and Text Mining• Semantic Web• Standardization and Integration Issues• Research Contributions• Future research directions, applications and

challenges

Factors that affect the adoption Factors that affect the adoption of DW&DMof DW&DM

The investments on DW&DM technologies are very expensive and the failure is pretty high compared with other IT systems.There is little research regarding the managerial, tactical and strategic aspects of the adoption of DW&DM.The adoption may result based on the real needs of companies and not from the perspectives and conclusions of the research.Pro-active and deep analysis of business problems which require the application of DM.The statistical and optimization methods may be enough.The potential advantages should need to be predicted before obtaining the results.

CRMCRM

CRM is the process by which forest companies manages their interactions with customers in the same way as other industrial enterprises.

Dedicated CRM systems for forest industry, associated tools and methodologies are integrated applications that implement an interface between a specific company and its customers.

E-CRM systems overlap B2C interfaces in E-Commerce sites.

CRMCRM (continued)(continued)

CRM is also the core activity of e-business, and in the framework of forest products enterprises it could be integrated with SCM and ERP systems as well as other e-marketing applications which may use market basket analysis.

Several companies complement their CRM and ERP applications with other Business and Market Intelligence systems.

CRMCRM (continued)(continued)

A CRM system applying DM might be composed of the following sub-systems:

•Customer Profiling which is the system that implements the process of discovery of patterns within customer databases which provides new

information and knowledge. This system is mainly divided into customer acquisition and customer retention which may also be defined as

customer loyalty. •Customer Profitability uses DM in order to understand, optimize and

improve it. Customer profitability is also logically linked to customer loyalty. •Customer Segmentation applies DM in order to discover discrete segments in a customer database.

•Predicting Customer Behaviour includes churning which represents the process of customer moving from one company to another.

Engineering Viewpoint Business Viewpoint

Operational Design Strategic

Inventory and Control Production Planning and Scheduling

Integrated Operational Systems Information Sharing, Coordination and Monitoring

Relationship Development Competitive Advantage

Supply ChainSupply Chain

Demand Chain ManagementDemand Chain Management DCM may be defined as the extension of the operations

from a single business unit or a company to the whole chain.

DCM is a set of practices aimed at managing and coordinating the whole demand chain, starting from the end customer and working backward to raw material and suppliers.

The main objectives: the development of a synergy along the whole demand

chain. the definition of a focus on specific customer segments

and meeting their needs. DM may provide an alternative or a refined solution to the

forecasting demand using Bayesian time series [Spedding, Chan, 2000],[Cheung et al., 2001].

Workflow ConceptWorkflow Concept It defines a comprehensive

approach for coordinated execution of multiple tasks or activities.

Business and production processes modeling and management.

Generally WfMSs are for business processes as DBMS for data.

Support e-business applications and enterprise integration, collaboration and coordination.

Existing Workflow standards defined by WfMC and W3C.

Workflow Mining is the discovery processing applied to Workflow systems.

Standardisation IssuesStandardisation Issues

Existing standards: Predictive Model

Markup Language (PMML)

XML and XMI SQL/MM Part 6:DM Java Data Mining

(JDM) OMG Common

Warehouse Metadata (CWM) for DM

Related Standards:

Semantic Web Standards (RDF, RDFS, OWL, etc.)

Web services (SOAP/XML, WSDL, UDDI, etc.)

Grid services (WSRF, OGSI, etc.)

Research ContributionResearch Contribution

PhD thesis: Framework for Distributed Knowledge Discovery Systems Embedded in Extended Enterprise, Loughborough University @ 2003, Loughborough, United Kingdom.

Standard integration of KD & DM systems in an extended manufacturing enterprise.

A unified object-oriented framework for the development of distributed KD/DM systems.

UNIFIED FRAMEWORK

KD/DM Products Systems

Systems for CRM SCM ERP

Modeling a Generic DM ApplicationModeling a Generic DM ApplicationClass DiagramClass Diagram

Association

global

Sequential Patterns

FuzzyLogic

Other Algorithms

OLAP

Classification

global

Cleaning Profiling Integration Selection Transformation

Data Pre_processing_1

ApplicationSpecification

CRMSCMERPMarket AnalysisProduct Life CycleProduction_Inventory

1

0..1

1

0..1

Data Pre_processing_2

CleaningProfilingIntegrationSelectionTransforamtion

opname()

00

0..* 0..n0..* 0..n

Statistical Analysis

Systems_Implementing_Algorithms

Subject-oriented

Data Mining

AssociationRuleClassificationClusteringStatistics

opname()

<<Data_Mining()>>

MyCorbaInterface

PolyAnalystDedicated_Systems_for

_financial_market

Visualization

Applying OMG’s CWM-DMApplying OMG’s CWM-DMMain DiagramMain Diagram

StatisticsMiningModel

StatisticsMiningModel

MiningProductModelResult

product_type : Stringproduct_model : MiningModelResult

MiningProductModelResult()

MiningManufacturingModelResult

MiningManufacturingModelResult()

+theMiningProductModelResult

+theMiningManufacturingModelResult

SequentialMiningModel

ApplicationAttribute

SequentilaMiningModel()

AssociationRuleMiningModel

AssociationRuleMiningModel

ClusteringMiningModel

ClusteringMiningModel

SupervisedMiningModel

target : ApplicationAttribute

SupervisedMiningModel()

MiningSettings

function : Stringalgorithm : String/ attributeUsage : AttributeUsageRelation/ dataSpecification : MiningDataSpecification

MiningModelResult

type : String/ model : MiningModel

MiningModelResult()

+theMiningProductModelResult

+theMiningManufacturingModelResult

ApplicationAttribute

usageType : AttributeUsageattributeType : AttributeType

ApplicationAttribute()

1+theApplicationAttribute+target 1

MiningModel

function : Stringalgorithm : String/ settings : MiningSettings/ inputSpec : ApplicationSpec.

MiningModel()

1

0..1

+miningModel1

+settings0..1

+theMiningModel

MarketAnalysis

MarketAnalysis()

ERP

ERP()

CRM

CRM()

DSS

DSS()

SCM

SCM()

ApplicationInputSpecification

/ miningModel : MiningModel/ inputAttribute : ApplicationAttribute

ApplicationInputSpecification()

+theApplicationAttribute

1..*0..1

+theApplicationInputSpecification

1..*0..1 Production_Inventory

name

Production_Inventory()

Other_Applications

EE_ApplicationSpecification

1

1..*+theMarketAnalysis

1

1..*

+theCRM

+theDSS

+theSupplyChain

1 1

+theApplicationInputSpecification

1 1+theInventorySystem

+theOther_Applications

Applying OMG’s CWM-DMApplying OMG’s CWM-DMSettings Diagram

CostMatrix

CostMatrix()

RegressionSettings

RegressionSettings()

ClassificationSettings

ClassificationSettings()

+theCostMatrix+theClassificationSettings

MiningAttribute

MiningAttribute()PMML()

StatisticsSettings

StatisticsSettings()

ClusteringSettings

ClusteringSettings()

AssociationRuleSettings

SupervisedMiningSettings

SupervisedMiningSettings()

+theRegressionSettings

+theSupervisedMiningSettings

+theClassificationSettings

+theSupervisedMiningSettings

ManufacturingCompetence&Performance

MarketStrategies

MarketStrategies()

ManufacturingStrategies

AttributeUsageRelation

AttributeUsageRelation() 1*

+Attribute

1

+AttributeUsage

*

AlgorithmSpecification

+theStatisticsSettings

+theClusteringSettings

+theAssociationRuleSettings

+theSupervisedMiningSettings

ExtendedEnterpriseStrategies

+theExtendedEnterpriseStrategies

BusinessProductSpecification(P3TQ)

productplacepricetimequantity

MiningSettings

function : Stringalgorithm : String/ attributeUsage : AttributeUsageRelation/ dataSpecification : MiningDataSpecification

1..*

1

+attributeUsage

1..*

+settings 1

+theMiningSettings

ManufacturingStrategicModel

Resources

Resources()

Processes

Processes()

Facilities

MiningDataSpecification

/ attribute : MiningAttribute

ManufacturingModel

GenericProductData

CreateProduct()GetProduct()

PDMModel

sales()retails()buyers()cost()

CompetitiveCapabilities

CoreCapabilities

+theManufacturingCompetence&Performance

Related ContributionsRelated Contributions Methodological and standard applications of

data, web and text mining systems.

Using OMG methodologies, architectures, models and midleware projects such as:

UML, CORBA, MDA and CWM.

Adhering to the existing reference architectures for enterprise integration and modeling, and ISO standards such as: CIM-OSA, ARIS, PERA, GERAM and RM-ODP.

Related ContributionsRelated Contributions (continued)(continued)

The specifications of the prototype system.

The definition of its capabilities and properties.

Development of some interfaces for legacy systems and databases.

KD/DM Interfaces

CWM/Java Corba/IDL

C/C++

Java/API

C++/API

ManufacturingModel/ API

ExtendedEnterprise Strategies/API

ProductModel/ API

KD/DM Core Package

Flat Files

J D BC

O D BC

An Interface between Forac Experimental Platform and KD&DM Using Agent Systems

Data

W arehouse

Knowledge, New Information

Mining Models

DATA

MINING

Exploration of the interconnections

between hypertext documents

Web StructureMining

Exploration of the interconnections

between hypertext documents

Web StructureMining

Exploration of data on The Use of the Web

User Accesses;Contents of Web log files;Other relevant data.

Web Usage Mining

Exploration of data on The Use of the Web

User Accesses;Contents of Web log files;Other relevant data.

Web Usage Mining

Exploration of the Content of the Web

Page Contents;Page links.

Web Content Mining

Exploration of the Content of the Web

Page Contents;Page links.

Web Content Mining

•It represents the mining processing applied to large volumes of unstructured text.

•The marketing information is available on the web as white papers, academic publications, trade journals, news, articles, reviews and even public opinions. Text mining could support the marketing professionals to efficiently use this information for finding knowledge and patterns.

Semantic Web TechnologiesSemantic Web Technologies

The current WWW is mainly syntactic-based where structure of the content is presented while content itself is only readable by humans.

Semantic Web is directed to create and manage the future Web or at least an extention which aims to include semantics to content.

Semantic Web Languages make the Web computer processable and computer understandable.

The Ontology Languages are directed to formalize the Web.

ReferencesReferences

• Aalst, W. and Hee, K. – Workflow Management Models, Methods, and Systems, London, New York: The MIT Press, Cooperative Information Systems, 2002.

• Cheun D. et al. – Advances in Knowledge Discovery and Data Mining, 5th Pacific-Asia Conference, PAKDD 2001, Hong Kong, China, Lecture Notes in AI, Berlin: Springer-Verlag, 2001.

• Hoover W.E. Jr. et al. – Managing the Demand-Supply Chain Value Innovations for Customer Satisfaction, New York: John Wiley & Sons, Inc. 2001.

• Marinescu D. – Internet-Based Workflow Management Toward a Semantic Web, New York: Wiley Series on Parallel and Distributed Computing, 2002.

• Schary and Skjott-Larsen – Managing the Global Supply Chain, Copenhagen: Munksgaard International Publishers Ltd., 1995.

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