9,772 research outputs found
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
Guidelines For Pursuing and Revealing Data Abstractions
Many data abstraction types, such as networks or set relationships, remain
unfamiliar to data workers beyond the visualization research community. We
conduct a survey and series of interviews about how people describe their data,
either directly or indirectly. We refer to the latter as latent data
abstractions. We conduct a Grounded Theory analysis that (1) interprets the
extent to which latent data abstractions exist, (2) reveals the far-reaching
effects that the interventionist pursuit of such abstractions can have on data
workers, (3) describes why and when data workers may resist such explorations,
and (4) suggests how to take advantage of opportunities and mitigate risks
through transparency about visualization research perspectives and agendas. We
then use the themes and codes discovered in the Grounded Theory analysis to
develop guidelines for data abstraction in visualization projects. To continue
the discussion, we make our dataset open along with a visual interface for
further exploration
Hierarchically Clustered Representation Learning
The joint optimization of representation learning and clustering in the
embedding space has experienced a breakthrough in recent years. In spite of the
advance, clustering with representation learning has been limited to flat-level
categories, which often involves cohesive clustering with a focus on instance
relations. To overcome the limitations of flat clustering, we introduce
hierarchically-clustered representation learning (HCRL), which simultaneously
optimizes representation learning and hierarchical clustering in the embedding
space. Compared with a few prior works, HCRL firstly attempts to consider a
generation of deep embeddings from every component of the hierarchy, not just
leaf components. In addition to obtaining hierarchically clustered embeddings,
we can reconstruct data by the various abstraction levels, infer the intrinsic
hierarchical structure, and learn the level-proportion features. We conducted
evaluations with image and text domains, and our quantitative analyses showed
competent likelihoods and the best accuracies compared with the baselines.Comment: 10 pages, 7 figures, Under review as a conference pape
QuizMap: Open social student modeling and adaptive navigation support with TreeMaps
In this paper, we present a novel approach to integrate social adaptive navigation support for self-assessment questions with an open student model using QuizMap, a TreeMap-based interface. By exposing student model in contrast to student peers and the whole class, QuizMap attempts to provide social guidance and increase student performance. The paper explains the nature of the QuizMap approach and its implementation in the context of self-assessment questions for Java programming. It also presents the design of a semester-long classroom study that we ran to evaluate QuizMap and reports the evaluation results. © 2011 Springer-Verlag Berlin Heidelberg
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