Extending Mining Applications towards Web Technology in Forest Industry

1. Extending Mining Applications towards Web Technology in Forest Industry Research&Development Agenda Elena Irina Neaga Forac Research Consortium Laval University, Québec City Canada E-mail: Irina.Elena-Neaga@forac.ulaval.ca

2. Outline • 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

3. Factors that affect the adoption of 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.

4. CRM 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 B2Cinterfaces in E-Commerce sites.

5. CRM(continued) CRM is also the core activity of e-business, and in the framework of forest products enterprisesit 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.

6. CRM(continued) • A CRM systemapplying DM might be composed of the following sub-systems: • Customer Profilingwhich 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 intocustomer acquisitionandcustomer retentionwhich may also be defined ascustomer loyalty. • Customer Profitability uses DM in order to understand, optimize and improve it. Customer profitability is also logically linked tocustomer loyalty. • Customer Segmentationapplies DM in order to discover discrete segments in a customer database. • Predicting Customer Behaviour includeschurningwhich represents the process of customer moving from one company to another.

7. Supply Chain

8. Demand 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].

9. Workflow 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.

10. 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.) Standardisation Issues

11. Research 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.

12. KD/DM UNIFIED Products FRAMEWORK Systems Systems for CRM SCM ERP

13. Association Classification Data Statistical Pre_processing_1 Analysis global global Sequential Patterns Data <<Data_Mining()>> ApplicationSpecification Pre_processing_2 Data Mining CRM Cleaning AssociationRule SCM Systems_Implementing FuzzyLogic Profiling Classification ERP _Algorithms Integration Clustering Market Analysis 0..* 0..* 0..n 0..n Selection Statistics Product Life Cycle Transforamtion Production_Inventory opname() opname() Other Algorithms 0..1 0..1 Subject-oriented Visualization 0 0 PolyAnalyst Dedicated_Systems_for _financial_market Cleaning Profiling Integration Selection Transformation MyCorbaInterface OLAP 1 1 Modeling a Generic DM ApplicationClass Diagram

14. Applying OMG’s CWM-DMMain Diagram

15. Applying OMG’s CWM-DMSettings Diagram

16. Related 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.

17. Related Contributions(continued) • The specifications of the prototype system. • The definition of its capabilities and properties. • Development of some interfaces for legacy systems and databases.

18. D ata Knowledge, New Information W arehouse Mining Models DATA MINING An Interface between Forac Experimental Platform and KD&DM Using Agent Systems

19. Web Mining Approach Exploration of the interconnections between hypertext documents Web Structure 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

20. Text 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.

21. Semantic 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.

22. References • 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.

23. “The world is moving so fast these days that the man who says it can't be done is generally interrupted by someone doing it.” Elbert Hubbard ? Paul Cezanne - Foliage