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 Typical Case of Recommending the Use of a Hierarchical Data Format

Storing large amounts of data in an efficient way from the point of view of very fast retrieval is animportant requirement for many industries like KEMA, a company offering consulting, testing andcertification in the energy business. Because their relational database could not cope fast enough with thelarge amounts of data involved, an alternative way for data storage was proposed, called HierarchicalData Format 5 (HDF5). HDF5 is a data model, a library, and file format for storing and managing data.Four hierarchical designs for storing and retrieving the large amounts of data involved were investigated.A benchmark was carried out in order to know which hierarchical structure would perform best.Eventually, a benchmark between HDF5 and MS SQL Server was carried out. It could be shown thatHDF5 performs four (4) times better for inserting and even 200 times better for retrieving data than theMS. SQL Server