Applying a disciplined approach to the development of a context-aware communication application

Pervasive computing applications must be engineered to provide unprecedented levels of flexibility in order to reconfigure and adapt in response to changes in computing resources and user requirements. To meet these challenges, appropriate software engineering abstractions and infrastructure are required as a platform on which to build adaptive applications. In this paper, we demonstrate the use of a disciplined, model-based approach to engineer a context-aware Session Initiation Protocol (SIP) based communication application. This disciplined approach builds on our previously developed conceptual models and infrastructural components, which enable the description, acquisition, management and exploitation of arbitrary types of context and user preference information to enable adaptation to context changes

Intergroup processes in intragroup contexts: The roles of group status, group identification and communication in diverse work teams

The present research examines employee identification and communication in organisations. In Study 1, 2229 soldiers from a military organisation completed measures of perceived status and strength of identification with their unit, employment category and their brigade. As predicted, the status of a key organisational group influenced reactions to different organisational groups: full-time soldiers evaluated their work unit and the organisation as being lower in status and identified less strongly with both of these groups than part-time soldiers. The second study extended these findings to a different research context: a large psychiatric hospital undergoing downsizing and restructuring. Surprisingly, there were no differences in survivors' and victims' levels of identification with organisational groups. Instead, and consistent with Study 1, there was evidence to suggest that employees adjusted their patterns of identification and perceptions of group status through a compensatory mechanism that maximised opportunities for selfenhancement and positive distinctiveness. In the third study, employees from a public hospital (N = 142) rated communication from double ingroup members (same work unit/same occupational group) more favourably than communication from partial group members (same work unit/different occupational group). These results are considered in terms of their practical implications for identity management in organisations.

The role of different sources of communication and trust in reducing change-related uncertainties during organisational change

The current paper presents a qualitadve study of the role of different sources of communicadon in reducing change-related uncertaintj' experienced by employees during organisadonal change. The paper examines the role of trust in influencing how employees' appraise informadon from different sources within organisadons. Interviews with 19 employees from a range of organisadons idendfy the different types of change-related uncertaindes experienced during change. In addidon, the different sources of communicadon utilised by employees are idendfied and the role each source plays in reducing the different t}'pes of uncertaint}' invesdgated. From employee responses it is evident that t)'pically supervisors are the best source of change informadon, while communicadon from senior management usually focuses on strategic issues. Employees indicate that communicadon with coworkers operates as a support mechanism providing an avenue to share grievances arising from the change. Finally, trust is idendfied as playing an important role in influencing who employees go to for informadon when experiencing uncertainty'. ImpUcadons for change management research in addidon to pracdcal implicadons are discussed.

Towards a standards-based autonomic context management system

Pervasive computing applications must be sufficiently autonomous to adapt their behaviour to changes in computing resources and user requirements. This capability is known as context-awareness. In some cases, context-aware applications must be implemented as autonomic systems which are capable of dynamically discovering and replacing context sources (sensors) at run-time. Unlike other types of application autonomy, this kind of dynamic reconfiguration has not been sufficiently investigated yet by the research community. However, application-level context models are becoming common, in order to ease programming of context-aware applications and support evolution by decoupling applications from context sources. We can leverage these context models to develop general (i.e., application-independent) solutions for dynamic, run-time discovery of context sources (i.e., context management). This paper presents a model and architecture for a reconfigurable context management system that supports interoperability by building on emerging standards for sensor description and classification.

On aggregation issues in spatial data management

Large amounts of information can be overwhelming and costly to process, especially when transmitting data over a network. A typical modern Geographical Information System (GIS) brings all types of data together based on the geographic component of the data and provides simple point-and-click query capabilities as well as complex analysis tools. Querying a Geographical Information System, however, can be prohibitively expensive due to the large amounts of data which may need to be processed. Since the use of GIS technology has grown dramatically in the past few years, there is now a need more than ever, to provide users with the fastest and least expensive query capabilities, especially since an approximated 80 % of data stored in corporate databases has a geographical component. However, not every application requires the same, high quality data for its processing. In this paper we address the issues of reducing the cost and response time of GIS queries by preaggregating data by compromising the data accuracy and precision. We present computational issues in generation of multi-level resolutions of spatial data and show that the problem of finding the best approximation for the given region and a real value function on this region, under a predictable error, in general is "NP-complete.