By Ian Watson
The wholesale seize and distribution of data over the past thirty years has created an exceptional desire for businesses to control their wisdom resources. wisdom administration (KM) addresses this desire via aiding a company to leverage its details assets and data resources by way of "remembering" and utilising its adventure. KM consists of the purchase, garage, retrieval, software, new release, and assessment of the data resources of a company in a managed manner. this day, agencies are making use of KM all through their structures, from details administration to advertising to human assets. employing wisdom administration: options for construction company stories examines why case-based reasoning (CBR) is so like minded for KM. CBR can be utilized to conform suggestions initially designed to unravel difficulties long ago, to handle new difficulties confronted through the association. This e-book truly demonstrates how CBR could be effectively utilized to KM difficulties by way of featuring numerous in-depth case-studies. Ian Watson, a widely known researcher in case-based reasoning and writer of the introductory ebook, utilizing CBR: strategies for firm structures has written this ebook particularly for IT managers and information administration process builders. * presents 7 real-world functions of information administration platforms that use case-based reasoning options. * provides the technical details had to enforce a data administration approach. * bargains insights into the advance of industrial KM CBR purposes * contains details on CBR software program proprietors, CBR experts and cost further resellers
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Additional info for Applying Knowledge Management: Techniques for Building Corporate Memories
If ignored, the case base’s value will degrade with time. 10 Conclusion By now, you should recognize that we all use CBR all the time to solve problems in our daily lives.
For example, they can be cases in the legal sense, they can be case histories of patients in the medical sense, details of bank loans, or descriptions of equipment troubleshooting situations. Perhaps you see a pattern emerging. Each of these—a legal case, a medical case history, a bank loan—and the troubleshooting record comprise: ■ ■ a description (the legal problem, the patient’s symptoms, the details of the loan, and the equipment’s problem); and the respective outcome or solution (the verdict or ruling, the treatment, the outcome of the loan, and the technical fix).
This now more clearly shows that T is closer to B than to A. You can see how adding background knowledge to our nearest neighbor formula in the form of relative importance or weightings on each attribute dramatically altered which case was retrieved as a best match. Our example is very simple. In reality, cases may have ten, twenty, or more attributes, each with its own weighting. Instead of using a two-dimensional space, as in our example, cases are plotted into a N-dimensional space. Moreover, we are not restricted to simple numerical comparisons of similarity.