Engineering adaptive user interfaces for enterprise applications

The user interface (UI) layer is considered an important component in software applications since it links the users to the software’s functionality. Enterprise applications such as enterprise resource planning and customer relationship management systems have very complex UIs that are used by users with diverse needs in terms of the required features and layout preferences. The inability to cater for the variety of user needs diminishes the usability of these applications. One way to cater for those needs is through adaptive UIs. Some enterprise software providers offer mechanisms for tailoring UIs based on the variable user needs, yet those are not generic enough to be used with other applications and require maintaining multiple UI copies manually. A generic platform based on a model-driven approach could be more reusable since operating on the model level makes it technology independent. The main objective of this research is devising a generic, scalable, and extensible platform for building adaptive enterprise application UIs based on a runtime model-driven approach. This platform primarily targets UI simplification, which we defined as a mechanism for increasing usability through adaptive behavior by providing users with a minimal feature-set and an optimal layout based on the context-of-use. This paper provides an overview of the research questions and methodology, the results that were achieved so far, and the remaining work

A Framework for Evaluating Model-Driven Self-adaptive Software Systems

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

A user profiling component with the aid of user ontologies

Abstract: What follows is a contribution to the field of user modeling for adaptive teaching and learning programs especially in the medical field. The paper outlines existing approaches to the problem of extracting user information in a form that can be exploited by adaptive software. We focus initially on the so-called stereotyping method, which allocates users into classes adaptively, reflecting characteristics such as physical data, social background, and computer experience. The user classifications of the stereotyping method are however ad hoc and unprincipled, and they can be exploited by the adaptive system only after a large number of trials by various kinds of users. We argue that the remedy is to create a database of user ontologies from which readymade taxonomies can be derived in such a way as to enable associated software to support a variety of different types of users

Synthesis of FMSP Experience and Lessons Learned for Fisheries Co-Management, Final Technical Report

In November 2012, the UK Department for International Development (DFID) set the terms of reference for a commissioned assessment of fisheries and aquaculture science. The task was to complete a "scoping review", consisting of an in-depth assessment of the existing evidence related to fisheries and aquaculture activities in developing countries and their contribution to economic growth, food security and nutrition. For this the assessment was expected to identify the existing evidence and 'evidence in the pipeline' (i.e. to be published imminently) from the existing literature, compile it, and provide an assessment of the strength (in the sense, scientific rigor) of that evidence, and identify knowledge or evidence gaps. In addition the assessment was to be complemented by a mapping of existing relevant interventions in fisheries and aquaculture. In order to conduct this assessment, the team of consultants adopted a six step methodological protocol that allowed them to assess in a consistent manner the scientific quality of the documents included in the assessment, based on quality, size and consistency of the evidence. After scanning, 202 documents were retained. The main evidences from these 202 documents were organised under two main threads: (i) Developmental outcomes, including food security; nutrition; health; economic growth and (ii) Mediating factors focusing on governance; and gender

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

Mediated data integration and transformation for web service-based software architectures

Service-oriented architecture using XML-based web services has been widely accepted by many organisations as the standard infrastructure to integrate heterogeneous and autonomous data sources. As a result, many Web service providers are built up on top of the data sources to share the data by supporting provided and required interfaces and methods of data access in a unified manner. In the context of data integration, problems arise when Web services are assembled to deliver an integrated view of data, adaptable to the specific needs of individual clients and providers. Traditional approaches of data integration and transformation are not suitable to automate the construction of connectors dedicated to connect selected Web services to render integrated and tailored views of data. We propose a declarative approach that addresses the oftenneglected data integration and adaptivity aspects of serviceoriented architecture