Content-aware compression for big textual data analysis

A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software libraries which often rely on functional content to work. Secondly, a context-free bit-oriented compression scheme (Approximated Huffman Compression) based on the Huffman algorithm is developed. This uses a hybrid data structure that allows pattern searching in compressed data in linear time. Thirdly, several modern compression schemes have been extended so that the compressed data can be safely split with respect to logical data records in distributed file systems. Furthermore, an innovative two layer compression architecture is used, in which each compression layer is appropriate for the corresponding stage of data processing. Peripheral libraries are developed that seamlessly link the proposed compression schemes to existing analytic platforms and computational frameworks, and also make the use of the compressed data transparent to developers. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.

Nurses', midwives' and key stakeholders' experiences and perceptions on requirements to demonstrate the maintenance of professional competence.

Aim: To present the qualitative findings from a study on the development of scheme(s) to give evidence of maintenance of professional competence for nurses and midwives. Background: Key issues in maintenance of professional competence include notions of self- assessment, verification of engagement and practice hours, provision of an evidential record, the role of the employer and articulation of possible consequences for non-adherence with the requirements. Schemes to demonstrate the maintenance of professional competence have application to nurses, midwives and regulatory bodies and healthcare employers worldwide. Design: A mixed methods approach was used. This included an online survey of nurses and midwives and focus groups with nurses and midwives and other key stakeholders. The qualitative data are reported in this study. Methods: Focus groups were conducted among a purposive sample of nurses, midwives and key stakeholders from January–May 2015. A total of 13 focus groups with 91 participants contributed to the study. Findings: Four major themes were identified: Definitions and Characteristics of Competence; Continuing Professional Development and Demonstrating Competence; Assessment of Competence; The Nursing and Midwifery Board of Ireland and employers as regulators and enablers of maintaining professional competence. Conclusion: Competence incorporates knowledge, skills, attitudes, professionalism, application of evidence and translating learning into practice. It is specific to the nurse's/midwife's role, organizational needs, patient's needs and the individual nurse's/midwife's learning needs. Competencies develop over time and change as nurses and midwives work in different practice areas. Thus, role-specific competence is linked to recent engagement in practice.