A framework for multi-user distributed virtual environments (DVEs) has been proposed. The proposed framework, incorporating two models (the functional model and the interconnection model), attempts to represent the common functionality, communication issues and requirements found in multi-user DVEs. The functional model concentrates on the DVE's functionality, while the interconnection model concentrates on how the components are interconnected to realize the required functionality. The models have been specified using the Unified Modeling Language (UML). An experimental case study demonstrates the applicability and generality of the proposed approach.

This paper describes a new effort estimation model based on use case reuse, called the use case reusability (UCR), intended for the projects that are reusing artifacts previously developed in past projects with similar scope. Analysis of the widely spread effort estimation techniques for software development projects shows that these techniques were primarily intended for the development of new software solutions. The baseline for the new effort estimation model is the use case points model. The UCR model introduces new classification of use cases based on their reusability, and it includes only those technical and environmental factors that according to the effort estimation experts have significant impact on effort for the target projects. This paper also presents a study which validates the usage of UCR model. The study is conducted within industry and academic environments using industry project teams and postgraduate students as subjects. The analysis results show that UCR model can be applied in different project environments and that according to the observed mean magnitude relative error, it produced very promising effort estimates.

The paper deals with prediction of user movement in the context of enhancing location-aware services. Location and movement information are based on the simplest mechanism provided by mobile networks -broadcasted cell identification. The proposed approach includes the following steps: collecting information about user movement, analysing and learning user movement patterns, and applying movement knowledge to movement prediction. Movement prediction system based on the neural networks used for movement regularity detection and movement prediction is presented. Service architecture and an example service concludes the paper.

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