Clare Stanier

Strategic Value of Cloud Computing in Healthcare Organisations Using the Balanced Scorecard Approach

The evolution of Cloud Computing over the past few years has the potential to provide many benefits for healthcare organisations. However, healthcare organisations still need to discover the strategic values of adopting such a technology model. The paper discusses the strategic value of implementing Cloud Computing solutions in a Saudi hospital based on the Balanced Scorecard Approach. The paper also presents the strategy map and the KPIs that were used by the hospital. The results of this paper (KPIs, strategy map.) could act as guidelines for similar projects and similar organisations, while taking into consideration the uniqueness of each organisation.

An Evaluation of the Challenges of Multilingualism in Data Warehouse Development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environment.

Defining Big Data

As Big Data becomes better understood, there is a need for a comprehensive definition of Big Data to support work in fields such as data quality for Big Data. Existing definitions of Big Data define Big Data by comparison with existing, usually relational, definitions, or define Big Data in terms of data characteristics or use an approach which combines data characteristics with the Big Data environment. In this paper we examine existing definitions of Big Data and discuss the strengths and limitations of the different approaches, with particular reference to issues related to data quality in Big Data. We identify the issues presented by incomplete or inconsistent definitions. We propose an alternative definition and relate this definition to our work on quality in Big Data.

Measuring the Success of Changes to Existing Business Intelligence Solutions to Improve Business Intelligence Reporting

To objectively evaluate the success of alterations to existing Business Intelligence (BI) environments, we need a way to compare measures from altered and unaltered versions of applications. The focus of this paper is on producing an evaluation tool which can be used to measure the success of amendments or updates made to existing BI solutions to support improved BI reporting. We define what we understand by success in this context, we elicit appropriate clusters of measurements together with the factors to be used for measuring success, and we develop an evaluation tool to be used by relevant stakeholders to measure success. We validate the evaluation tool with relevant domain experts and key users and make suggestions for future work.

Towards Differentiating Business Intelligence, Big Data, Data Analytics and Knowledge Discovery

Confusion, ambiguity and misunderstanding of the concepts and terminology regarding different approaches concerned with analysing massive data sets (Business Intelligence, Big Data, Data Analytics and Knowledge Discovery) was identified as a significant issue faced by many academics, fellow researchers, industry professionals and domain experts. In that context, a need to critically evaluate these concept and approaches focusing on their similarities, differences and relationships was identified as useful for further research and industry professionals. In this position paper, we critically review these four approaches and produce a framework, which provides visual representation of the relationship between them to effectively support their identification and easier differentiation.

Text Mining Approach to Extract Associations Between Obesity and Arabic Herbal Plants

Historical information on herbal medicines is underexploited and this is particularly true of the important resources of Arabic herbal medicines. Current research into Arabic medicinal plants as alternative medicine is limited and there is a lack of accurate translations and interpretations of herbal medicine texts. This research focuses on an investigation of Arabic herbal medicinal plants in relation to the problem of obesity. This paper demonstrates how text mining can help extract relevant concepts associated with Arabic herbal plants and obesity in order to discover associations between the herbal medicinal ingredients and obesity symptoms.

Cloud Computing Adoption in Healthcare Organisations: A Qualitative Study in Saudi Arabia

This paper provides a comprehensive review of Cloud Computing by discussing the benefits and challenges of implementing such solution and discusses various Cloud Computing adoption models. The paper describes Cloud Computing in healthcare domains. It provides also information about Cloud Computing in Saudi Arabia and how it could be applied for healthcare domain. The paper presents a qualitative study which provides an in-depth understanding of the Cloud Computing adoption decision-making process in healthcare organisations in Saudi Arabia. The paper discusses the factors which will affect Cloud Computing decision making process in Saudi Arabia. The findings of the study showed that the factors affecting Cloud Computing adoption can be divided into five main categories, Technological, Business, Environmental, Organisational and Human. This paper also identifies some of the key drivers and challenges of Cloud Computing adoption in Saudi healthcare organisations. This study will help both Saudi healthcare organisations and Cloud Computing vendors in understanding healthcare organisations’ attitude towards the adoption of Cloud Computing.

A Knowledge based Decision Making Tool to Support Cloud Migration Decision Making

Cloud computing represents a paradigm shift in the way that IT services are delivered within enterprises. Cloud computing promises to reduce the cost of computing services, provide on-demand computing resources and a pay per use model. However, there are numerous challenges for enterprises planning to migrate to a cloud computing environment as cloud computing impacts multiple aspects of enterprises and the implications of migration to the cloud vary between enterprises. This paper discusses the development of an holistic model to support strategic decision making for cloud computing migration. The proposed model uses a hybrid approach to support decision making, combining the analytical hierarchical approach (AHP) with Case Based Reasoning (CBR) to provide a knowledge based decision support model and takes into account five factors identified from the secondary research as covering all aspects of cloud migration decision making. The paper discusses the different phases of the model and describes the next stage of the research which will include the development of a prototype tool and use of the tool to evaluate the model in a real life context.

Mobile Holistic Enterprise Transformation Framework

Abstract Mobile shipments have surpassed those of PCs and tablets, and the demand for mobile services has never been higher. Although, many businesses believe mobile devices and services are beneficial to them, they have not actually taken steps to adopt mobile on a large scale. Other enterprises are limiting adoption to provision of a mobile friendly web page or including mobile elements within their existing electronic services. This paper proposes a holistic framework that highlights the goals of mobile adoption, presents a taxonomy of enterprise mobile services capabilities which if utilised should assist organisations to achieve the goals of mobile adoption and categorises the components of mobile solutions and mobile strategy. Developing a taxonomy of enterprise mobile services capabilities helps the transformation to a mobile enterprise by supporting the visualisation of a future state of the enterprise.

Text Mining Approach to Analyse Stock Market Movement.

Stock Market (SM) is a significant sector of countries’ economy and represents a crucial role in the growth of their commerce and industry. Hence, discovering efficient ways to analyse and visualise stock market data is considered a significant issue in modern finance. The use of Data Mining (DM) techniques to predict stock market has been extensively studied using historical market prices but such approaches are constrained to make assessments within the scope of existing information, and thus they are not able to model any random behaviour of stock market or provide causes behind events. One area of limited success in stock market prediction comes from textual data, which is a rich source of information and analysing it may provide better understanding of random behaviours of the market. Text Mining (TM) combined with Random Forest (RF) algorithm offers a novel approach to study critical indicators, which contribute to the prediction of stock market abnormal movements. A Stock Market Random Forest-Text Mining system (SMRF-TM) is developed to mine the critical indicators related to the 2009 Dubai stock market debt standstill. Random forest is applied to classify the extracted features into a set of semantic classes, thus extending current approaches from three to eight classes: critical down, down, neutral, up, critical up, economic, social and political. The study demonstrates that Random Forest has outperformed the other classifiers and has achieved the best accuracy in classifying the bigram features extracted from the corpus.