Titlebook: Internet Webcasting; Generating and Match Matthias Eichstädt Textbook 1999 Springer Fachmedien Wiesbaden 1999 Business.Deployment.Evaluatio

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978-3-8244-2125-1Springer Fachmedien Wiesbaden 1999

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https://doi.org/10.1007/978-3-663-10402-5Business; Deployment; Evaluation; Information; Internet; Intranet; Matching; Project; Randelementmethode; Rat

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Ingrained

The IBM Grand Central Station Project,This chapter presents an architectural overview of the Grand Central Station (GCS) project at the IBM Almaden Research Center. This architecture provides a specific context for the subsequent discussions of the profile matching algorithms.

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Internal Profile Representation,The foundation of the presented profile matching algorithms is a compact data structure for organizing a large collection of profiles called profile index (PI for brevity). This chapter introduces the design objectives and the data structures to represent a profile index.

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Parallel Profile Matching,In this chapter we present a fast parallel profile matching algorithm, which has been built upon our sequential matching algorithm. It automatically partitions the profile database to balance the load and to minimize the interaction among processors. We also introduce a dynamic load balancing mechanism that handles profile updates.

团结

Automatic Profile Generation,In this chapter we introduce novel techniques to generate user profiles automatically from monitoring the users’ behavior. We present two applications, an active email system and a web based profile generator, that utilize different aspects of our techniques.

Colonnade

Quantitative Evaluation,The profile matching algorithms presented in chapter 5 and 6 along with the rest of the GCS system have been implemented using the Java programming language [JAV]. This chapter presents the performance evaluation of our profile matching based on the current implementation.

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. Push technology stems from a very simple idea. Rather than requiring users to explicitly request the information that they need, data can be sent to users without having them specifically ask for it. A push system automatically delivers information content such as classified ads or shopping items