Architectural design in object-z

We present a process for introducing an object-oriented architecture into an abstract functional specification written in Object-Z. Since the design is derived from the specification, correctness concerns are addressed as pan of the design process. We base our approach on refactoring rules that apply to class structure, and use the rules to implement design patterns. As a motivating example, we introduce a user-interface design that follows the model-view-controller paradigm into an existing specification.

Using the Amazon metric to construct an image database based on what people do, not what they say

Current image database metadata schemas require users to adopt a specific text-based vocabulary. Text-based metadata is good for searching but not for browsing. Existing image-based search facilities, on the other hand, are highly specialised and so suffer similar problems. Wexelblat's semantic dimensional spatial visualisation schemas go some way towards addressing this problem by making both searching and browsing more accessible to the user in a single interface. But the question of how and what initial metadata to enter a database remains. Different people see different things in an image and will organise a collection in equally diverse ways. However, we can find some similarity across groups of users regardless of their reasoning. For example, a search on Amazon.com returns other products also, based on an averaging of how users navigate the database. In this paper, we report on applying this concept to a set of images for which we have visualised them using traditional methods and the Amazon.com method. We report on the findings of this comparative investigation in a case study setting involving a group of randomly selected participants. We conclude with the recommendation that in combination, the traditional and averaging methods would provide an enhancement to current database visualisation, searching, and browsing facilities.

Reflective web interface agent

Web interface agent is used with web browsers to assist users in searching and interactions with the WWW. It is used for a variety of purposes, such as web-enabled remote control, web interactive visualization, and e-commerce activities. User may be aware or unaware of its existence. The intelligence of interface agent consists in its capability of learning and decision-making in performing interactive functions on behalf of a user. However, since web is an open system environment, the reasoning mechanism in an agent should be able to adapt changes and make decisions on exceptional situations, and therefore use meta knowledge. This paper proposes a framework of Reflective Web Interface Agent (RWIA) that is to provide causal connections between the application interfaces and the knowledge model of the interface agent. A prototype is also implemented for the purpose of demonstration.

Towards user profiling for web recommendation

Collaborative recommendation is one of widely used recommendation systems, which recommend items to visitor on a basis of referring other's preference that is similar to current user. User profiling technique upon Web transaction data is able to capture such informative knowledge of user task or interest. With the discovered usage pattern information, it is likely to recommend Web users more preferred content or customize the Web presentation to visitors via collaborative recommendation. In addition, it is helpful to identify the underlying relationships among Web users, items as well as latent tasks during Web mining period. In this paper, we propose a Web recommendation framework based on user profiling technique. In this approach, we employ Probabilistic Latent Semantic Analysis (PLSA) to model the co-occurrence activities and develop a modified k-means clustering algorithm to build user profiles as the representatives of usage patterns. Moreover, the hidden task model is derived by characterizing the meaningful latent factor space. With the discovered user profiles, we then choose the most matched profile, which possesses the closely similar preference to current user and make collaborative recommendation based on the corresponding page weights appeared in the selected user profile. The preliminary experimental results performed on real world data sets show that the proposed approach is capable of making recommendation accurately and efficiently.