Efficiently computing weighted proximity relationships in spatial databases

Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate the problem of evaluating the top k distinguished “features” for a “cluster” based on weighted proximity relationships between the cluster and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. Combining a standard multi-step paradigm with new lower and upper proximity bounds, we presented an efficient algorithm to solve the problem. The algorithm is implemented in several different modes. Our experiment results not only give a comparison among them but also illustrate the efficiency of the algorithm.

Gender, computing and the organisation of working time: public/private comparisons in the Australian context

Professional computing employment in Australia, as in most advanced economies, is highly sex segregated, reflecting well-rehearsed ideas about the masculinity of technology and computing culture. In this paper we are concerned with the processes of work organisation that sustain and reproduce this gendered occupational distribution, focusing in particular on differences and similarities in working-time arrangements between public and private sectors in the Australian context. While information technology companies are often highly competitive workplaces with individualised working arrangements, computing professionals work in a wide range of organisations with different regulatory histories and practices. Our goal is to investigate the implications of these variations for gender equity outcomes, using the public/private divide as indicative of different regulatory frameworks. We draw on Australian census data and a series of organisational case studies to compare working-time arrangements in professional computing employment across sectors, and to examine the various ways employees adapt and respond. Our analysis identifies a stronger ‘long hours culture’ in the private sector, but also underlines the rarity of part-time work in both sectors, and suggests that men and women tend to respond in different ways to these constraints. Although the findings highlight the importance of regulatory frameworks, the organisation of working time across sectors appears to be sustaining rather than challenging gender inequalities in computing employment.

Active node supporting context-aware vertical handover in pervasive computing environment with redundant positioning

A major requirement for pervasive systems is to integrate context-awareness to support heterogeneous networks and device technologies and at the same time support application adaptations to suit user activities. However, current infrastructures for pervasive systems are based on centralized architectures which are focused on context support for service adaptations in response to changes in the computing environment or user mobility. In this paper, we propose a hierarchical architecture based on active nodes, which maximizes the computational capabilities of various nodes within the pervasive computing environment, while efficiently gathering and evaluating context information from the user's working environment. The migratable active node architecture employs various decision making processes for evaluating a rich set of context information in order to dynamically allocate active nodes in the working environment, perform application adaptations and predict user mobility. The active node also utilizes the Redundant Positioning System to accurately manage user's mobility. This paper demonstrates the active node capabilities through context-aware vertical handover applications.