Metaphors of big data

Metaphors are a common instrument of human cognition, activated when seeking to make sense of novel and abstract phenomena. In this article we assess some of the values and assumptions encoded in the framing of the term big data, drawing on the framework of conceptual metaphor. We first discuss the terms data and big data and the meanings historically attached to them by different usage communities and then proceed with a discourse analysis of Internet news items about big data. We conclude by characterizing two recurrent framings of the concept: as a natural force to be controlled and as a resource to be consumed.

Producing the magnum opus : A metaphor for nephrology nursing expertise acquisition

Aim. This paper elucidates the nature of metaphor and the conditions necessary to its use as an analytic device in qualitative research, and describes how the use of metaphor assisted in the analytic processes of a grounded theory study of nephrology nursing expertise. Background. The use of metaphor is pervasive in everyday thought, language and action. It is an important means for the comprehension and management of everyday life, and makes challenging or problematic concepts easier to explain. Metaphors are also pervasive in quantitative and qualitative research for the same reason. In both everyday life and in research, their use may be implicit or explicit. Methods. The study using grounded theory methodology took place in one renal unit in New South Wales, Australia between 1999 and 2000 and included six non-expert and 11 expert nurses. It involved simultaneous data collection and analysis using participant observation, semi-structured interviews and review of nursing documentation. Findings. A three stage skills-acquisitive process was identified in which an orchestral metaphor was used to explain the relationships between stages and to satisfactorily capture the data coded within each stage. Conclusion. Metaphors create images, clarify and add depth to meanings and, if used appropriately and explicitly in qualitative research, can capture data at highly conceptual levels. Metaphors also assist in explaining the relationship between findings in a clear and coherent manner. © 2005 Blackwell Publishing Ltd.

Extending conceptual models for Web based applications

The next phase envisioned for the World Wide Web is automated ad-hoc interaction between intelligent agents, web services, databases and semantic web enabled applications. Although at present this appears to be a distant objective, there are practical steps that can be taken to advance the vision. We propose an extension to classical conceptual models to allow the definition of application components in terms of public standards and explicit semantics, thus building into web-based applications, the foundation for shared understanding and interoperability. The use of external definitions and the need to store outsourced type information internally, brings to light the issue of object identity in a global environment, where object instances may be identified by multiple externally controlled identification schemes. We illustrate how traditional conceptual models may be augmented to recognise and deal with multiple identities.