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So far, most investments in EI have been driven by a focus on an increase in efficiency and topdown change of business processes in relatively static value chains. Typical deployment of EI has been based on the idea of an enterprise-wide "big bang" transition to a new "best way of working", pre-conceived largely by a corporate elite of engineers and analysts. The resulting system and the related procedures were supposed to enforce this way of working and make sure that the enterprise would reap the benefits (of efficiency) for some time to come, by discouraging subsequent unofficial forms of smaller-scale and/or bottom-up change. This approach was very much enterprise centric and typically weak in accommodating subsequent change. It is however no longer adequate, because enterprises increasingly need to rely on bottom-up initiative, emergence and flexibility, in order to remain competitive. Due to fierce global competition, enterprises can no longer survive with a focus on efficiency and producing more of the same (for a lower price). Instead, enterprises need to concentrate on value innovation and producing more of not the same (with higher margins). To this end enterprises operate increasingly in dynamic value networks.Therefore EI should be geared towards leveraging creativity, collaboration and change in more dynamic networks to release its full potential as an instrument for value creation. A new objective for EI should be: To stimulate value creation based on innovation and co-creation in a context of networked enterprises that is very much defined bottom-up, by creative, committed workers.

The EU IST integrated project Semantic Knowledge Technologies (SEKT) developed and exploited semantic knowledge technologies. Core to the SEKT project has been the creation of synergies by combining the three core research areas ontology management, machine learning and natural language processing.

In the process of knowledge discovery, workers examine available information in order to make sense of it. By sensemaking, we mean interacting with and operating on the information with a variety of information processing mechanisms [3,18]. Previously, we introduced a concept that uses the spreadsheet metaphor with cells containing visualizations of complex data. In this paper, we extend and apply a cognitive model called "visual sensemaking " to the Visualization Spreadsheet. We use the task of making sense of a large Web site as a concrete example through out the paper for demonstration. Using a variety of visualization techniques, such as the Disk Tree and Cone Tree, we show that the interactions of the Visualization Spreadsheet help users draw conclusions from the overall relationships of the entire information set.

Designing Information Technology to Support Distributed Cognition

In this paper an application of methodologies to automatically acquire semantic word classes and to use them in text information retrieval systems is described.

This paper presents two design patterns for the hypermedia domain: `Navigational Contexts' and `Information on Demand'. They are applied in two different aspects of hypermedia applications design: the design of healthy navigational structures and the design of understandable and usable hypermedia interfaces, respectively. These two patterns are part of an effort for developing a Pattern Language for that domain.

The confluence of computers, communications, and databases is quickly creating a distributed database where many applications require real-time access to both temporally accurate and multimedia data. This is particularly true in military and intelligence applications, but these required features are needed in many commercial applications as well. We are developing a distributed database, called BeeHive, which could offer features along different types of requirements: real-time, fault-tolerance, security, and quality-of service for audio and video. Support of these features and potential trade-offs between them could provide a significant improvement in performance and functionality over current distributed database and object management systems. In this paper, we present a high level design for BeeHive architecture and sketch the design of the BeeHive Object Model (BOM) which extends object-oriented data models by incorporating time and other features into objects, resulting in a highly reflective architecture.