A review of data visualization: opportunities in manufacturing sequence management.

Data visualization now benefits from developments in technologies that offer innovative ways of presenting complex data. Potentially these have widespread application in communicating the complex information domains typical of manufacturing sequence management environments for global enterprises. In this paper the authors review the visualization functionalities, techniques and applications reported in literature, map these to manufacturing sequence information presentation requirements and identify the opportunities available and likely development paths. Current leading-edge practice in dynamic updating and communication with suppliers is not being exploited in manufacturing sequence management; it could provide significant benefits to manufacturing business. In the context of global manufacturing operations and broad-based user communities with differing needs served by common data sets, tool functionality is generally ahead of user application

Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns

This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM) implements SOM-type learning to one-dimensional arrays for individual time units, preserves the orientation with short-term memory and arranges the arrays in an ascending order of time. The two-dimensional representation of the SOTM attempts thus twofold topology preservation, where the horizontal direction preserves time topology and the vertical direction data topology. This enables discovering the occurrence and exploring the properties of temporal structural changes in data. For representing qualities and properties of SOTMs, we adapt measures and visualizations from the standard SOM paradigm, as well as introduce a measure of temporal structural changes. The functioning of the SOTM, and its visualizations and quality and property measures, are illustrated on artificial toy data. The usefulness of the SOTM in a real-world setting is shown on poverty, welfare and development indicators

Providing scientific visualisation for spatial data analysis: criteria and an assessment of SAGE

A consistent theme in recent work on developing exploratory spatial data analysis (ESDA) has been the importance attached to visualization techniques, particularly following the pioneering development of packages such as REGARD by Haslett et al (1990). The focus on visual techniques is often justified in two ways: (a) the power of modern graphical interfaces means that graphics is no longer a way of simply presenting results in the form of maps or graphs, but a tool for the extraction of information from data; (b)graphical, exploratory methods are felt to be more intuitive for non-specialists to use than methods of numerical spatial statistics enabling wider participation in the process of getting data insights. Despite the importance attached to visualisation techniques, very little work has been done to assess the effectiveness of techniques, either in the wider scientific visualisation community, or among those working with spatial data. This paper will describe a theoretical framework for developing visualisation tools for ESDA that incorporates a data model of what the analyst is looking for based on the concepts of "rough" and "smooth" elements of a data set and a theoretical scheme for assessing visual tools. The paper will include examples of appropriate tools and a commentary on the effectiveness of some existing packages

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