Businesses currently face the daily challenge of managing content efficiently. These businesses are being flooded with information from web Content Management Systems (CMS) that present an all-too-simple picture. Instead, content management systems should solve the problem of turning content into information and information into knowledge.

Content Management Systems are not just a product or a technology. CMS is defined as a generic term which refers to a wide range of processes that underpin the “next-generation” of medium to large-scale websites. Content management is a process which deals with the creation, storage, modification, retrieval and display of data or content.
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Information and documentation services available on the Internet through web servers are growing in an exponential manner. The logical evolution of the Internet over the last 10 years has been producing a replacement of static web pages and documents by dynamically generated documents. This is due both to user interaction with work processes and flows defined by service creators and to the availability of growing information repositories. This has meant a progressive evolution from a concept of web page publishing which was quite simple in its origins to more complex and differentiated schemes relying on procedures and techniques based on information management. The increasing complexity of services and systems supporting them has made it necessary to formulate a theoretical and practical corpus capable of combining classical information management techniques within organizations with the particular features of the digital environment.
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This is a literature review on neural networks and related algorithms. It is aimed to get a general understanding on neural networks and find out the possible applications of these models in information retrieval (IR) systems.

Beginning with a preliminary definition and typical structure of neural networks, neural networks are studied with respect to their learning processes and architecture structures. A case study on some specific networks and related algorithms is followed. The applications of some neural network models and related algorithms in information retrieval systems are then investigated. Problems on applying neural network models into IR systems are finally summarized in the conclusion.
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This Wireless Networking (Macintosh) User Guide is Copyright by Deakin University All rights reserved. No part of this work covered by Deakin University’s copyright may be reproduced or copied in any form or by any means (graphic, electronic or mechanical, including photocopying, recording, taping or information retrieval systems) without the written permission of Deakin University.
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The purpose of these tutorials is not to teach you Microsoft Access, but rather to teach you some generic information systems concepts and skills using Access. Of course, as a side effect, you will learn a great deal about the software enough to write your own useful applications. However, keep in mind that Access is an enormously complex, nearly- industrial-strength software development environ- ment. The material here only scrapes the surface of Access development and database programming.
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