Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on

A Nine Month Report on Progress Towards a Framework for Evaluating Advanced Search Interfaces considering Information Retrieval and Human Computer Interaction

This is a nine month progress report detailing my research into supporting users in their search for information, where the questions, results or even thei

Relativity in space-times with short-distance structure governed by an observer-independent (Planckian) length scale

I show that it is possible to formulate the Relativity postulates in a way that does not lead to inconsistencies in the case of space-times whose short-distance structure is governed by an observer-independent length scale. The consistency of these postulates proves incorrect the expectation that modifications of the rules of kinematics involving the Planck length would necessarily require the introduction of a preferred class of inertial observers. In particular, it is possible for every inertial observer to agree on physical laws supporting deformed dispersion relations of the type $E^2- c^2 p^2- c^4 m^2 + f(E,p,m;L_p)=0$, at least for certain types of $f$.Comment: Same formulas and results as in 1st version, but a change of notation is introduced in order to clarify that the studied illustrative example is consistent with the R.P. for both choices of the overall sign. 1 ref added and 2 refs upgraded. Some rewording of the text in Sec5, and addition of an analogy with background fields in ordinary electromagnetism which I use to illustrate difference between space-times with an observer-independent Lp, and space-times in which Lp is introduced without modifications of Special Relativit

Exploring the front end of New Zealand curriculum in student teacher education: an example from language and mathematics education.

This paper reports on two components of a collaborative project conducted by members of the language and literacy education, mathematics education and social studies teaching teams at the Faculty of Education, The University of Waikato. The teams decided to research the implications of the front end of The New Zealand Curriculum document [NZC] (Ministry of Education, 2007). The front end of the document includes key competencies and a statement describing each learning area. The language and literacy team chose to explore student teacher understandings of the English essence statement and the way in which that learning area is structured. The mathematics education team explored student teacher understandings of and implications for the "thinking" key competency for the teaching and learning of mathematics. Data were collected through in-class observations and tasks, and the analysis of aspects of student assessment work. The findings highlighted the value of an explicit focus on a particular facet of the NZC along with the challenges student teachers experience in envisaging how this might play out in practice

Hypothesis exploration with visualization of variance.

BackgroundThe Consortium for Neuropsychiatric Phenomics (CNP) at UCLA was an investigation into the biological bases of traits such as memory and response inhibition phenotypes-to explore whether they are linked to syndromes including ADHD, Bipolar disorder, and Schizophrenia. An aim of the consortium was in moving from traditional categorical approaches for psychiatric syndromes towards more quantitative approaches based on large-scale analysis of the space of human variation. It represented an application of phenomics-wide-scale, systematic study of phenotypes-to neuropsychiatry research.ResultsThis paper reports on a system for exploration of hypotheses in data obtained from the LA2K, LA3C, and LA5C studies in CNP. ViVA is a system for exploratory data analysis using novel mathematical models and methods for visualization of variance. An example of these methods is called VISOVA, a combination of visualization and analysis of variance, with the flavor of exploration associated with ANOVA in biomedical hypothesis generation. It permits visual identification of phenotype profiles-patterns of values across phenotypes-that characterize groups. Visualization enables screening and refinement of hypotheses about variance structure of sets of phenotypes.ConclusionsThe ViVA system was designed for exploration of neuropsychiatric hypotheses by interdisciplinary teams. Automated visualization in ViVA supports 'natural selection' on a pool of hypotheses, and permits deeper understanding of the statistical architecture of the data. Large-scale perspective of this kind could lead to better neuropsychiatric diagnostics

Data-driven through-life costing to support product lifecycle management solutions in innovative product development

Innovative product usually refers to product that comprises of creativity and new ideas. In the development of such a new product, there is often a lack of historical knowledge and data available to be used to perform cost estimation accurately. This is due to the fact that traditional cost estimation methods are used to predict costs only after a product model has been built, and not at an early design stage when there is little data and information available. In light of this, original equipment manufacturers are also facing critical challenges of becoming globally competitive and increasing demands from customer for continuous innovation. To alleviate these situations this research has identified a new approach to cost modelling with the inclusion of product lifecycle management solutions to address innovative product development.The aim of this paper, therefore, is to discuss methods of developing an extended-enterprise data-driven through-life cost estimating method for innovative product development