Learning across business sectors: context, embeddedness and conceptual chasms

While the construction industry is frequently encouraged to learn from other business sectors, the difficulties of transferring knowledge from one context to another are rarely acknowledged. The problematic nature of knowledge is addressed with particular emphasis on the concept of contextual embeddedness. From this point of view, the process of 'knowledge transfer' depends upon a prolonged process of socialization between actors from both the 'receiving' and 'sending' contexts networking. It is contended that a significant conceptual chasm exists between the exhortations of industry leaders to learn from other sectors and the theoretical complexities associated with knowledge transfer. An ongoing research project is described that seeks to facilitate knowledge sharing between construction and aerospace. A novel approach to knowledge sharing based upon soft systems methodology (SSM) (Mode 2) is described and justified. Initial findings from the first cycle of the research are discussed and used to highlight the importance of context in the implementation of supply chain management.

A study of cursor trajectories of motion-impaired users

This paper describes a study of the cursor trajectories of motion-impaired users in "point and click" interactions. A characteristic of cursor movement is proposed that aims to capture the spatial distribution of cursor movement about a target. This characteristic indicates that users often exhibit increased cursor movement in the vicinity of the target, have more difficulty performing the "clicking" part of the interaction as compared to the navigation part, and tend to navigate directly toward the target during the middle portion of the cursor trajectory. The implications of these characteristic behaviours on interface design are discussed.

Pocket data mining: towards collaborative data mining in mobile computing environments

Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. This paper proposes a new architecture that we have prototyped for realizing the significant applications in this area. We have proposed using mobile software agents in this application for several reasons. Most importantly the autonomic intelligent behaviour of the agent technology has been the driving force for using it in this application. Other efficiency reasons are discussed in details in this paper. Experimental results showing the feasibility of the proposed architecture are presented and discussed.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Review of theories for blocking

A typical feature of the atmospheric circulation at middle and high latitudes is a tendency to fluctuate between two rather extreme circulation patterns. This behaviour of the atmosphere is most common at the Northern Hemisphere during the winter and has been known among the meteorologists for a considerable time (e.g. Garriott (1904)). One of these two states is identified by a predominantly zonal circulation or a so-called high-index circulation, the other state by a meridional or a low-index circulation. The meridional circulation is often broken up in a characteristic atmospheric pattern of cut-off lows and highs. These features usually have a time scale of several days during which they affect the weather in a very dominating way. The transition from the zonal to the meridional or cellular circulation is very characteristic and follows a very typical chain of events.