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    Knowledge Management

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    adage that says knowledge is power‚ and this is reflected in the way organisations now view knowledge. Knowledge has become one of the key resources organisations utilise in their business strategies hence the need to manage it. The field of Knowledge Management is considered fairly new and it has generated a lot of interest within the business circles with individual companies investing a lot of resources in its development and establishment within their organisations. This paper is focusing on the

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    knowledge management

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    detailed coverage of knowledge management concepts and methodologies which includes knowledge creation‚ knowledge architecture‚ and knowledge codification. The knowledge management tools and knowledge portals as well as the notions of knowledge transfer in the E-world are discussed. Aims The aims of this unit include the broad understanding of the following areas of Knowledge Management Systems: • Knowledge Management Systems Life Cycle. • Knowledge Creation and Knowledge Architecture. • Capturing

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    B.UILDING I.NFORMATION M.ODELLING INTRODUCTION: The construction industry in the 21st century is facing a huge challenge. Building Information Modeling(BIM) is emerging as a technological‚ procedural and strategic new approach to the fields of Architecture‚ Engineering and Construction providing a way for iterating‚ documenting and managing a design through most of its life-cycle from conceptual design‚ design development to construction through operations and management. The bulk of the time

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    Knowledge Is Power

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    Knowledge is Powerful than Money Respected president‚ honorable faculty staff and my fellow students A.O.A. Knowledge is powerful than wealth is our today’s topic of discussion. When we say that knowledge is power we mean that knowledge is the only source of strength in the world. The idea that wealth is power is also quite common‚ it is believed to be true by a number of people but that is wrong; wealth is not a permanent thing. A man may be wealthy today but he may become very

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    Data Mining

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    Data Mining Abdullah Alshawdhabi Coleman University Simply stated data mining refers to extracting or mining knowledge from large amounts of it. The term is actually a misnomer. Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus‚ data mining should have been more appropriately named “knowledge mining from data‚” which is unfortunately somewhat long. Knowledge mining‚ a shorter term‚ may not

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    knowledge management

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    http://www.zsi.ii.pwr.wroc.pl Abstract. This paper attempts to address the problem of the automatic customer segmentation by processing data collected in Social Customer Relationship Management (Social CRM) systems using Kohonen networks. Presented segmentation approach comprises classic loyaltyprofitability link model that is explicit for CRM‚ and new social media components direct to Social CRM. The result of presented approach is an analysis tool with data visualization for managers which significantly

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    Knowledge Management

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    Knowledge Management on Air Quality Case Studies Knowledge Management on Air Quality Case Studies © 2010 Asian Development Bank and Clean Air Initiative for Asian Cities Center (CAI–Asia Center) All rights reserved. Published 2010. ISBN 978-92-9092Publication Stock No. RPT102406 Asian Development Bank and CAI–Asia Center. Knowledge management on air quality: Case studies. Mandaluyong City‚ Philippines: Asian Development Bank and CAI–Asia Center‚ 2010. 1. Knowledge management.

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    Knowledge Management

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    Table of Contents Declaration 0 Question 1a – What is Knowledge Management? 3 DEFINITION 1: 3 DEFINITION 2: 4 DEFINITION 3: 4 DEFINITION 4: 5 Question 1b – What is Knowledge Management? 6 Question 2 – Knowledge Management Tools and Techniques 7 Organisational Structure: 7 Company Culture: 7 External Partnership: 7 Revenue: 8 2. Selection of tools. 8 What they have: 8 What they need: 8 Tools I select: 9 Mind Mapping 9 Database Management System 9 Social Network and Blogs 9 3.

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    Data Quality

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    PRINCIPLES OF DATA QUALITY Arthur D. Chapman1 Although most data gathering disciples treat error as an embarrassing issue to be expunged‚ the error inherent in [spatial] data deserves closer attention and public understanding …because error provides a critical component in judging fitness for use. (Chrisman 1991). Australian Biodiversity Information Services PO Box 7491‚ Toowoomba South‚ Qld‚ Australia email: papers.digit@gbif.org 1 © 2005‚ Global Biodiversity Information Facility Material

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    represents my own work and that it has not been previously submitted for a degree at this or any other university. Martin Above Mwape Supervisor: Mr S Chikuba. Signature …………………… Date…………Signature/date…………………… CERTIFICATE OF APPROVAL This report has been approved as partial fulfilment of the requirement for the award of Bsc Economics of Chreso University Supervisor: Mr S. Chikuba Signature: ………………………….. Date ……………………………….. ABSTRACT

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