Peter A. Gloor

Bio: Peter A. Gloor is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Social network analysis & Social network. The author has an hindex of 37, co-authored 211 publications receiving 4918 citations. Previous affiliations of Peter A. Gloor include University of Cologne & Union Bank of Switzerland.

Sensible Organizations: Changing Our Businesses and Work Styles through Sensor Data

Abstract: We introduce the concept of sensor-based applications for the daily business settings of organizations and their individual workers. Wearable sensor devices were developed and deployed in a real organization, a bank, for a month in order to study the effectiveness and potential of using sensors at the organizational level. It was found that patterns of physical interaction changed dynamically while e-mail is more stable from day to day. Different patterns of behavior between people in different rooms and teams (p < 0.01), as well as correlations between communication and a worker's subjective productivity, were also identified. By analyzing a fluctuation of network parameters, i.e., “betweenness centrality, ” it was also found that communication patterns of people are different: some people tend to communicate with the same people in regular frequency (which is hypothesized as a typical pattern of throughput-oriented jobs) while some others drastically changed their communication day by day (which is hypothesized as a pattern of creative jobs). Based on these hypotheses, a reorganization, such that people having similar characteristics work together, was proposed and implemented.

Analysis of Informal Communication Networks – A Case Study

Abstract: The structure and dynamics of informal communication networks are of central importance for the functionality of enterprise workflows and for performance and innovation of knowledge-centric organizations. While most executives are aware of this fact, there is a general lack of (semi-) automated, IT-supported methods and instruments to make informal communication networks measurable. Although logging of electronic communications has made considerable progress over the past few years, it is still extremely difficult to map personal interaction; manual approaches in particular are extremely error-prone. The article shows how informal communication networks can be investigated by IT-based methods. At the same time, the authors will be presenting an instrument (“Social Badges”) that collects personal communications automatically and more precisely than legacy approaches allow. The practical applicability of the approach is evaluated through a case study.

Using Multivariate Statistics

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Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.