Community Organizer: supporting the formation of network communities through spatial representation

Proceedings 2001 Symposium on Applications and the Internet ◽

10.1109/saint.2001.905184 ◽

2002 ◽

Cited By ~ 2

Author(s):

K. Kamei ◽

E. Jettmar ◽

K. Fujita ◽

S. Yoshida ◽

K. Kuwabara

Keyword(s):

Spatial Representation ◽

Network Communities ◽

Community Organizer

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Effectiveness of spatial representation in the formation of network communities: experimental study on community organizer

Interacting with Computers ◽

10.1016/s0953-5438(02)00017-6 ◽

2002 ◽

Vol 14 (6) ◽

pp. 739-759 ◽

Cited By ~ 1

Author(s):

Koji Kamei ◽

Kunihiko Fujita ◽

Eva Jettmar ◽

Sen Yoshida ◽

Kazuhiro Kuwabara

Keyword(s):

Experimental Study ◽

Spatial Representation ◽

Network Communities ◽

Community Organizer

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Identifying groups of airborne particles by ordination

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100172012 ◽

1994 ◽

Vol 52 ◽

pp. 856-857

Author(s):

M. Shlepr ◽

C. M. Vicroy

Keyword(s):

Elemental Analysis ◽

Energy Dispersive Spectroscopy ◽

Manufacturing Process ◽

Spatial Representation ◽

Airborne Particles ◽

Common Source ◽

Data Set ◽

Microelectronics Industry ◽

Induced Changes ◽

Source Materials

The microelectronics industry is heavily tasked with minimizing contaminates at all steps of the manufacturing process. Particles are generated by physical and/or chemical fragmentation from a mothersource. The tools and macrovolumes of chemicals used for processing, the environment surrounding the process, and the circuits themselves are all potential particle sources. A first step in eliminating these contaminants is to identify their source. Elemental analysis of the particles often proves useful toward this goal, and energy dispersive spectroscopy (EDS) is a commonly used technique. However, the large variety of source materials and process induced changes in the particles often make it difficult to discern if the particles are from a common source.Ordination is commonly used in ecology to understand community relationships. This technique usespair-wise measures of similarity. Separation of the data set is based on discrimination functions. Theend product is a spatial representation of the data with the distance between points equaling the degree of dissimilarity.

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