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Distinguished Alumnus Speaker Oct. 11

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The design and production of complex engineered systems (CES) require analysis of massive amounts of detailed information, including data on products and materials, engineering designs, manufacturing specifications, supply chain and delivery data, and changing customer needs. Visual analytics promises to offer tools and methods that will help stakeholders interactively explore, discover, and make sense of the underlying data.
Dimensionality reduction (DR) is often used for exploring the evolution patterns of a collection of dynamic ego-networks. However, DR often lacks flexibility: as analysts' questions evolve during data exploration, the low-dimensional projection remains static, limiting the depth of exploration. To address the inflexibility of DR, we designed a data transformation pipeline which enables analysts to transform dynamic ego-networks into event sequences for steering MDS to create different scatterplots of dynamic ego-networks.
Business ecosystems are characterized by large, complex, and global networks of firms, often from many different market segments, all collaborating, partnering, and competing to create and deliver new products and services. Given the rapidly increasing scale, complexity, and rate of change of business ecosystems, as well as economic and competitive pressures, analysts are faced with the formidable task of quickly understanding the fundamental characteristics of these interfirm networks.

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