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The End-User Access to Multiple Sources-EAMS system-integrates given information sources into a knowledge management system. It relates the world of documents with the database world using an ontology. The focus of developing the EAMS system is on the acquisition and maintenance of knowledge. Hence, in both worlds, machine learning is applied. In the document world, a learning search engine adapts to user behaviour by analysing the click-through-data. This eases the personalization of selecting appropriate documents for users and does not require further maintenance. In the database world, knowledge discovery in databases (KDD) bridges the gap between the fine granularity of relational databases and the actual information needs of users. KDD extracts knowledge from data and, therefore, allows the knowledge management system to make good use of already existing company data-without further acquisition or maintenance. A graphical user interface provides users with a uniform access to document collections on the Internet (Intranet) as well as to relational databases. Since the ontology generates the items in the user interface, a change in the ontology automatically changes the user interface without further efforts.The EAMS system has been applied to customer relationship management in the insurance domain. Questions to be answered by the system concern customer acquisition (e.g. direct marketing), customer up-and cross-selling (e.g. which products sell well together), and customer retention (here, which customers are likely to leave the insurance company or ask for a return of a capital life insurance). Documents about other insurance companies and demographic data published on the Internet contribute to the answers, as do the results of data analysis of the company's contracts.Knowledge management supplies information organization-wide to very different users. If
The End-User Access to Multiple Sources-EAMS system-integrates given information sources into a knowledge management system. It relates the world of documents with the database world using an ontology. The focus of developing the EAMS system is on the acquisition and maintenance of knowledge. Hence, in both worlds, machine learning is applied. In the document world, a learning search engine adapts to user behaviour by analysing the click-through-data. This eases the personalization of selecting appropriate documents for users and does not require further maintenance. In the database world, knowledge discovery in databases (KDD) bridges the gap between the fine granularity of relational databases and the actual information needs of users. KDD extracts knowledge from data and, therefore, allows the knowledge management system to make good use of already existing company data-without further acquisition or maintenance. A graphical user interface provides users with a uniform access to document collections on the Internet (Intranet) as well as to relational databases. Since the ontology generates the items in the user interface, a change in the ontology automatically changes the user interface without further efforts.The EAMS system has been applied to customer relationship management in the insurance domain. Questions to be answered by the system concern customer acquisition (e.g. direct marketing), customer up-and cross-selling (e.g. which products sell well together), and customer retention (here, which customers are likely to leave the insurance company or ask for a return of a capital life insurance). Documents about other insurance companies and demographic data published on the Internet contribute to the answers, as do the results of data analysis of the company's contracts.Knowledge management supplies information organization-wide to very different users. If
Semantic-Web style metadata for advanced context representation and domain knowledge are likely to play a more and more important role within access control models and languages. This paper outlines how context metadata can be referred to in semantics-aware access control policies and discusses the main open issues in designing, producing, and maintaining metadata for security. Abstract INTRODUCTIONIt is widely recognized that a well-understood model and a highly expressive language for access control are of paramount importance in today's global network environment. A common syntax and semantics for specifying and enforcing access control policies makes it possible to express and exchange the conditions under which distributed resources and services can be used in an open environment. Sharing and composing access control policies enables cooperation and federation of distributed services, as required by emerging Web-based computation paradigms. In this paper, we present our recent research work [2], dealing with three key aspects of knowledge representation involved in this new generation of access control languages:Resource representation. Writing access control policies where resources to be protected are pointed at via data identifiers and access conditions are evaluated against their attribute values is not sufficient anymore. Rather, it is important to be able to specify access control requirements about resources in terms of available metadata describing them.Context representation. Distributed environments have increased the amount of context information available at policy evaluation time (e.g., location-based one), and this information is achieving a more and more important role.Subject identity. Evaluating conditions on the subject requesting access to a resource often means accessing personal information either pre- 178 DATA AND APPLICATIONS SECURITY XVIIIsented by the requestor as a part of the authentication process or available elsewhere. Identifying subjects raises a number of privacy issues, since electronic transactions (e.g., purchases) require disclosure of a far greater quantity of information than their physical counterparts. A number of alternatives to strong identities are coming of age, all of them involving advanced metadata. Recent research work by our group [3] is based on the idea that reputations are a resource that can be computed on the basis of the views of a user community about a pseudonym; also, reputations can be stored, maintained, and certified.For metadata to play the fundamental role outlined above, several research problems need to be solved. To begin with, description metadata must be authenticated and aggregated before their content can be used for policy evaluation, and the need to determine metadata trustworthiness becomes important. A number of XML-based standards [18] are available that describe resources (including users) and services as well as circumstances and the environment where the transaction takes place. Promising approaches have started to emerge which ...
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