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Knowledge Discovery Process Models: From Traditional to Agile Modeling

Copyright © 2012. 29 pages.
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DOI: 10.4018/978-1-61350-050-7.ch004
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MLA

Alnoukari, Mouhib and Asim El Sheikh. "Knowledge Discovery Process Models: From Traditional to Agile Modeling." Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplinary Applications. IGI Global, 2012. 72-100. Web. 1 Nov. 2014. doi:10.4018/978-1-61350-050-7.ch004

APA

Alnoukari, M., & El Sheikh, A. (2012). Knowledge Discovery Process Models: From Traditional to Agile Modeling. In A. Rahman El Sheikh, & M. Alnoukari (Eds.) Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplinary Applications (pp. 72-100). Hershey, PA: Business Science Reference. doi:10.4018/978-1-61350-050-7.ch004

Chicago

Alnoukari, Mouhib and Asim El Sheikh. "Knowledge Discovery Process Models: From Traditional to Agile Modeling." In Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplinary Applications, ed. Asim Abdel Rahman El Sheikh and Mouhib Alnoukari, 72-100 (2012), accessed November 01, 2014. doi:10.4018/978-1-61350-050-7.ch004

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Abstract

Knowledge Discovery (KD) process model was first discussed in 1989. Different models were suggested starting with Fayyad’s et al (1996) process model. The common factor of all data-driven discovery process is that knowledge is the final outcome of this process. In this chapter, the authors will analyze most of the KD process models suggested in the literature. The chapter will have a detailed discussion on the KD process models that have innovative life cycle steps. It will propose a categorization of the existing KD models. The chapter deeply analyzes the strengths and weaknesses of the leading KD process models, with the supported commercial systems and reported applications, and their matrix characteristics.
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Knowledge Discovery Process Modeling Categorization

The following are the proposed categories for Knowledge Discovery Process (KDP) modeling:

  • 1.

    Traditional KDP Approach. This approach is widely used by most of KDP modeling innovators. Starting with Fayyad’s et al. (1996) KDD process modeling, many of KDP modeling used the same process flow including most of the following steps: business understanding, data understanding, data processing, data mining/modeling, model evaluation, and deployment/visualization.

  • 2.

    Ontology-based KDP Approach. This approach is the integration of ontology engineering and traditional KDP approach steps. Three directions were identified in this approach: Ontology for KDP, KDP for Ontology, and the integration of both previous directions (Gottgtroy 2007).

  • 3.

    Web-based KDP Approach. This approach mainly deals with web log analysis. It is mainly similar to traditional KDP approach, but it has some unique steps to deal with log web data, see (Pabarskaite and Raudys 2007) and (Buchner et al. 1999).

  • 4.

    Agile-based KDP Approach. This approach is the integration between agile methodologies and KDP traditional methodologies (Alnoukari et al. 2008).

The Leading Kdp Models

The following leading KDP models have been chosen by the authors based on their innovation steps, and their applications in both academia and industry:

  • 1.

    Knowledge Discovery in Databases (KDD) Process by Fayyad et al. (1996).

  • 2.

    Information Flow in a Data Mining Life Cycle by Ganesh et al. (1996).

  • 3.

    SEMMA by SAS Institute (1997).

  • 4.

    Refined KDD paradigm by Collier et al. (1998).

  • 5.

    Knowledge Discovery Life Cycle (KDLC) Model by Lee and Kerschberg (1998).

  • 6.

    CRoss-Industry-Standard Process for Data Mining (CRISP-DM) by CRISP-DM (2000).

  • 7.

    Generic Data Mining Life Cycle by (DMLC) by Hofmann (2003).

  • 8.

    Ontology Driven Knowledge Discovery Process (ODKD) by Gottgtroy (2007).

  • 9.

    Adaptive Software Development-Data Mining (ASD-DM) Process Model by Alnoukari et al. (2008).

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Complete Chapter List

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Table of Contents
Foreword
Evon M. O. Abu-Taieh
Preface
Asim Abdel Rahman El Sheikh, Mouhib Alnoukari
Chapter 1
Mouhib Alnoukari, Humam Alhammami Alhawasli, Hatem Abd Alnafea, Amjad Jalal Zamreek
This chapter attempts to define the knowledge body of Business Intelligence. It provides an overview of the context we have been working in. The... Sample PDF
Business Intelligence: Body of Knowledge
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Chapter 2
Zaidoun Alzoabi
The term Agile Method of software development was coined in the 2001.This approach is characterized with creativity, flexibility, adaptability... Sample PDF
Agile Software: Body of Knowledge
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Chapter 3
Zaidoun Alzoabi
Agile methods are characterized with flexibility, reliance on tacit knowledge, and face to face communication in contrast to traditional methods... Sample PDF
Knowledge Management in Agile Methods Context: What Type of Knowledge Is Used by Agilests?
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Chapter 4
Mouhib Alnoukari, Asim El Sheikh
Knowledge Discovery (KD) process model was first discussed in 1989. Different models were suggested starting with Fayyad’s et al (1996) process... Sample PDF
Knowledge Discovery Process Models: From Traditional to Agile Modeling
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Chapter 5
Deanne Larson
Agile methodologies were introduced in 2001. Since this time, practitioners have tried to create and apply Agile methodologies to many delivery... Sample PDF
Agile Methodologies for Business Intelligence
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Chapter 6
Martin Molhanec, Vojtech Merunka
The business intelligence system gradually became of vital importance for many organizations nowadays. But unfortunately, the traditional static... Sample PDF
BORM: Agile Modelling for Business Intelligence
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Chapter 7
J. Fernández, E. Mayol, J.A. Pastor
In this chapter we present an overview of several methodological approaches used in business intelligence (BI) projects, as well as data warehouse... Sample PDF
Agile Approach to Business Intelligence as a Way to Success
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Chapter 8
Samir Hammami, Firas M. Alkhaldi
Over years, research in Management Information Systems (MIS) has resulted in significant implications for organizations in a wide variety of areas... Sample PDF
Enhancing BI Systems Application Through the Integration of IT Governance and Knowledge Capabilities of the Organization
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Chapter 9
Mouhib Alnoukari
The main contribution of this chapter is the demonstration that ASD-BI is adaptive to environment changes, enhances knowledge capturing and sharing... Sample PDF
ASD-BI: A Knowledge Discovery Process Modeling Based on Adaptive Software Development Agile Methodology
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Chapter 10
Jagdish Chand
In this chapter we will describe an advanced business intelligence system we built at Yahoo to measure the lift in brand awareness driven from the... Sample PDF
Measurement of Brand Lift from a Display Advertising Campaign
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Chapter 11
Zaidoun Alzoabi, Faek Diko, Saiid Hanna
BI is playing a major role in achieving competitive advantage in almost every sector of the market, and the higher education sector is no exception.... Sample PDF
Suggested Model for Business Intelligence in Higher Education
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Chapter 12
Muhammad Mazen Almustafa, Dania Alkhaldi
In this highly technology - dependent, knowledge- based economy, the causes for failure of most software development projects are related to rapid... Sample PDF
Business Intelligence and Agile Methodology for Risk Management in Knowledge-Based Organizations
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Chapter 13
Kamal Atieh, Elias Farzali
The need for such framework increase or decrease in each country depending on its status and on the relationship existed between business and IT... Sample PDF
Towards a Business Intelligence Governance Framework Within E-Government System
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Chapter 14
Constanta-Nicoleta Bodea
The chapter presents an ontology-based knowledge management system developed for a Romanian university. The starting point for the development... Sample PDF
Business Intelligence in Higher Education: An Ontological Approach
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Chapter 15
Haroon Altarawneh, Sattam Alamaro, Asim El Sheikh
Agile BI is as much about how BI applications are used and what they enable businesses to do, as it is about how the applications themselves are... Sample PDF
Web Engineering and Business Intelligence: Agile Web Engineering Development and Practice
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