Client-side mobile user profile for content management using data mining techniques

Mobile device can be used as a medium to send and receive the mobile internet content. However, there are several limitations using mobile internet. Content personalisation has been viewed as an important area when using mobile internet. In order for personalisation to be successful, understanding the user is important. In this paper, we explore the implementation of the user profile at client-side, which may be used whenever user connect to the mobile content provider. The client-side user profile can help to free the provider in performing analysis by using data mining technique at the mobile device. This research investigates the conceptual idea of using clustering and classification of user profile at the client-site mobile. In this paper, we applied K-means and compared several other classification algorithms like TwoStep, Kohenen and Anomaly to determine the boundaries of the important factors using information ranking separation

Mobile Information Systems In Australian Utility Companies

This research has investigated a broad range of issues associated with the use of mobile information systems in Australian utility companies. In particular, special consideration has been given to the existing infrastructure, business processes and information systems used in the participating organisations to support its field workers. This research has used a Technology, Organisation & People (TOP) multi-perspective model to conduct five case studies obtaining various technical, organisational and personal issues associated with the adoption of mobile information systems. These findings will help other large organisations to better manage their geographically distributed assets and workforce

An integrated mobile content recommendation system

Many features have been added to mobile devices to assist the user's information consumption. However, there are limitations due to information overload on the devices, hardware usability and capacity. As a result, content filtering in a mobile recommendation system plays a vital role in the solution to this problem. A system that utilises content filtering can recommend content which matches a user's needs based on user preferences with a higher accuracy rate. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. The problems can be viewed as first time connection and first content rating for non-interactive recommendation systems where information is insufficient to predict mobile content which will match with a user's needs. In addition, how to find relevant items for the content recommendation system which are related to a user's profile is also a concern. An integrated model that combines the user group identification and mobile content filtering for mobile content recommendation was proposed in this study in order to address the current limitations of the mobile content recommendation system. The model enhances the system by finding the relevant content items that match with a user's needs based on the user's profile. A prototype of the client-side user profile modelling is also developed to demonstrate the concept. The integrated model applies clustering techniques to determine groups of users. The content filtering implemented classification techniques to predict the top content items. After that, an adaptive association rules technique was performed to find relevant content items. These approaches can help to build the integrated model. Experimental results have demonstrated that the proposed integrated model performs better than the comparable techniques such as association rules and collaborative filtering. These techniques have been used in several recommendation systems. The integrated model performed better in terms of finding relevant content items which obtained higher accuracy rate of content prediction and predicted successful recommended relevant content measured by recommendation metrics. The model also performed better in terms of rules generation and content recommendation generation. Verification of the proposed model was based on real world practical data. A prototype mobile content recommendation system with client-side user profile has been developed to handle the revisiting user issue. In addition, context information, such as time-of-day and time-of-week, could also be used to enhance the system by recommending the related content to users during different time periods. Finally, it was shown that the proposed method implemented fewer rules to generate recommendation for mobile content users and it took less processing time. This seems to overcome the problems of first time connection and first content rating for non-interactive recommendation systems

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Adaptive interfaces for mobile preference-based searching

Today's mobile computing devices provide a convenient means to search for points-of-interest (POIs) such as restaurants and accommodation. Mobile Preference-Based Search Tools (PBSTs) allow users to identify POIs such as restaurants or accommodation most suited to their needs and constraints using a mobile device. These devices however, have several design constraints including limited screen space and hardware capabilities. Adaptive User Interfaces (AUIs) have been proposed to address these issues but have not been extensively applied to mobile PBSTs such as mobile tourist guides. In addition, AUIs possess several benefits and advantages over static (traditional) interfaces, which do not take a user's preferences, skill set and experience into account. Little research, however, has been conducted into identifying the potential benefits of AUIs for mobile preference-based searching (PBS). The aim of this research was to determine the extent to which an AUI could improve the effectiveness and user satisfaction of mobile PBS. A literature study was conducted to determine the benefits and limitations of existing mobile PBSTs and determine how these could be improved. The potential benefits of AUIs for mobile PBSTs and a mobile map-based visualisation system were identified. A suitable model for incorporating an AUI into a mobile PBST was identified. The requirements for a mobile PBST were combined with the potentially adaptable objects of a Mobile Map-based Visualisation (MMV) system to provide adaptation suggestions for POInter, an existing mobile tourist guide. A field study using POInter was conducted in order to measure the extent to which participants agreed with suggestions provided for adapting the information, interaction and visualisation aspects of the system. These results were used to derive adaptation requirements for A-POInter, an adaptive version of POInter. Using a model-based design approach, an AUI was designed and implemented for A-POInter. An extensive field study was then conducted to evaluate the usability of the adaptations provided by A-POInter. The quantitative and qualitative data collected from the evaluations allowed the usability of A-POInter to be determined. The results of the field study showed that the participants were highly satisfied with the usability and the usefulness of the adaptations provided by A-POInter. Conclusions and recommendations for future work based on the results of the research were then outlined to conclude the dissertation

Adaptive user interfaces for mobile map-based visualisation

Mobile devices today frequently serve as platforms for the visualisation of map-based data. Despite the obvious advantages, mobile map-based visualisation (MMV) systems are often difficult to design and use. Limited screen space, resource constraints and awkward interaction mechanisms are among the many problems with which designers and users have to contend. Adaptive user interfaces (AUIs), which adapt to the individual user, represent a possible means of addressing the problems of MMV. Adaptive MMV systems are, however, generally designed in an ad-hoc fashion, making the benefits achieved difficult to replicate. In addition, existing models for adaptive MMV systems are either conceptual in nature or only address a subset of the possible input variables and adaptation effects. The primary objective of this research was to develop and evaluate an adaptive MMV system using a model-based approach. The Proteus Model was proposed to support the design of MMV systems which adapt in terms of information, visualisation and user interface in response to the user‟s behaviour, tasks and context. The Proteus Model describes the architectural, interface, data and algorithm design of an adaptive MMV system. A prototype adaptive MMV system, called MediaMaps, was designed and implemented based on the Proteus Model. MediaMaps allows users to capture, location-tag, organise and visualise multimedia on their mobile phones. Information adaptation is performed through the use of an algorithm to assist users in sorting media items into collections based on time and location. Visualisation adaptation is performed by adapting various parameters of the map-based visualisations according to user preferences. Interface adaptation is performed through the use of adaptive lists. An international field study of MediaMaps was conducted in which participants were required to use MediaMaps on their personal mobile phones for a period of three weeks. The results of the field study showed that high levels of accuracy were achieved by both the information and interface adaptations. High levels of user satisfaction were reported, with participants rating all three forms of adaptation as highly useful. The successful implementation of MediaMaps provides practical evidence that the model-based design of adaptive MMV systems is feasible. The positive results of the field study clearly show that the adaptations implemented were highly accurate and that participants found these adaptations to be useful, usable and easy to understand. This research thus provides empirical evidence that the use of AUIs can provide significant benefits for the visualisation of map-based information on mobile devices