Defining Big Data

ABSTRACT 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 Dat

Working with Newer Data Management Technologies

Data management technologies are changing rapidly and this presents a significant challenge for database teaching. There is a requirement to teach traditional relational database concepts and to ensure that students are equipped with the advanced skills expected by employers. There is also a requirement to prepare students to work with newer data models and NoSQL and to understand and be able to leverage concepts such as Big Data analytics. This paper discusses the experience of working with MongoDB and MapReduce and starting to work with Hadoop in undergraduate and postgraduate teaching at Staffordshire University. It is suggested that while the amount of time that can be given to newer technologies in the undergraduate curriculum is limited, this is a subject area which has the power to capture students’ imaginations and provides a good basis for undergraduate projects and Masters level dissertations

Identifying New Directions in Database Performance Tuning

Database performance tuning is a complex and varied active research topic. With enterprise relational database management systems still reliant on the same set-based relational concepts that defined early data management products, the disparity between the object-oriented application development model and the object-relational database model, called the object-relational impedance mismatch problem, is addressed by techniques such as object-relational mapping (ORM). However, this has resulted in generally poor query performance for SQL developed by object applications and an irregular fit with cost-based optimisation algorithms, and leads to questions about the need for the relational model to better adapt to ORM-generated queries. This paper discusses database performance optimisation developments and seeks to demonstrate that current database performance tuning approaches need re-examination. Proposals for further work include exploring concepts such as dynamic schema redefinition; query analysis and optimisation modelling driven by machine learning; and augmentation or replacement of the cost-based optimiser model

Understanding the determinants of Cloud Computing adoption in Saudi healthcare organisations

Cloud Computing is an evolving information technology paradigm that impacts many sectors in many countries. Although Cloud Computing is an emerging technology there is little in the literature concerning its application in the Saudi healthcare sector. This paper examines and identifies the factors that will influence the adoption of Cloud Computing in Saudi healthcare organisations. The study integrates the TOE (Technology–Organization–Environment) framework with the Information System Strategic Triangle (IS Triangle) and the HOT-fit (Human–Organization–Technology) model to provide a holistic evaluation of the determinants of Cloud Computing adoption in healthcare organisations. Of the five perspectives examined in this study, the Business perspective was found to be the most important followed by the Technology, Organisational and Environmental perspectives and finally the Human perspective. The findings of the study showed that the five most important factors influencing the adoption of Cloud Computing in this context are soft financial analysis, relative advantage, hard financial analysis, attitude toward change and pressure from partners in the business ecosystem. This study identifies the critical factors for both practitioners and academics that influence Cloud Computing adoption decision-making in Saudi healthcare

The Determinants of Cloud Computing Adoption in Saudi Arabia

There is a large volume of published studies investigating the factors that affect cloud adoption. However, there are very few studies which investigate cloud computing adoption in technologically developing countries and one focus of the research was to examine whether the factors which influence cloud computing adoption in technologically developed countries also apply in technologically developing countries. The research presented in this paper in this paper builds on the diffusion of innovation theory (DOI) and the Technology-organisation-environment (TOE) framework in order to investigate the factors which influence cloud computing adoption. Fourteen hypothesis were developed from the literature based on cloud adoption and were examined in the research. DOI and TOE. Data was collected by using a web-based questionnaire and was analysed using a range of statistical measures. This paper discusses the design and implementation of the study, the data analysis and conclusions from the analysis and compares the findings of this study with the findings of similar studies in technologically developed countries. The study show that there are some similarities as well as some differences in the factors that affect cloud computing adoption between technologically developed countries and technologically developing countrie

MULTIDIMENSIONAL DATA MODELLING: DATA SETS AND TOOLS FOR BUSINESS INTELLIGENCE

Business Intelligence (BI) is an expanding data management area. BI analysis requires students to understand key concepts such as multidimensional data modelling, data cleansing and data mining algorithms, tools and techniques. Practical work for BI is often based around Excel which has a number of limitations as far as the teaching of BI is concerned. This paper discusses the experience of using the SSAS (SQL Server Analysis Services) tool to introduce students to multidimensional data modelling. We describe the way in which the tool was used, the issues encountered, the strengths and limitations of working with SSAS and possible future extensions. We also discuss issues with identifying and using data sets and outline a proposal to extend the data sets available to our student

Mobile Holistic Enterprise Transformation Framework

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 enterpris

Scaffolding in a Higher Education Context

This paper describes the experience of using scaffolding with Computing undergraduates. Scaffolding is a well established teaching and learning approach within the constructivist framework. In a scaffolding approach, students are provided with supports, known as scaffolds, which allow learners to extend their knowledge and go beyond their existing skills and capabilities. The scaffolds are then removed, in a process known as fading, allowing students to develop as independent learners. Most of the literature on scaffolding discusses scaffolding in the context of early learners or school based instruction and scaffolding is often used in a task based context where the emphasis is on mastering specific skills rather than higher order concepts. There is comparatively little literature which discusses the use of scaffolding in Higher Education. Using scaffolding in Higher Education presents a number of issues and challenges as teaching and learning in Higher Education emphasises the higher order skills of analysis, synthesis and evaluation which are more difficult to scaffold than task based practical skills and tertiary level students have different expectations about the teaching and learning process. A scaffolding approach was used to introduce undergraduates to a complex, unstructured problem which required them to explore a range of different issues. Hard and soft scaffolds were used to support students as they moved from entry level concepts to more advanced concepts and the scaffolds were faded as students began to consider strategic approaches rather than discrete tasks. The scaffolds were easy to design and the scaffolding approach proved a good mechanism for supporting students in the initial exploration of material. However, the process of fading was much more challenging, partly because the scaffolding was linked to the assessment. Based on our experience, we propose that scaffolding approaches in Higher Education should not be linked to assessment and should be designed to support metacognitive and strategic rather than task based skills

Stock market random forest-text mining system mining critical indicators of stock market movements

Stock Market (SM) is believed to be a significant sector of a free market economy as it plays a crucial role in the growth of commerce and industry of a country. The increasing importance of SMs and their direct influence on economy were the main reasons for analysing SM movements. The need to determine early warning indicators for SM crisis has been the focus of study by many economists and politicians. Whilst most research into the identification of these critical indicators applied data mining to uncover hidden knowledge, very few attempted to adopt a text mining approach. This paper demonstrates how text mining combined with Random Forest algorithm can offer a novel approach to the extraction of critical indicators, and classification of related news articles. The findings of this study extend the current classification of critical indicators from three to eight classes; it also show that Random Forest can outperform other classifiers and produce high accuracy

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 environmen