Five patterns for designing pattern mining workshops

Yuma Akado, Sakurako Kogure, Alice Sasabe, Jei Hee Hong, Keishi Saruwatari, Takashi Iba

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we present five ideas for designing pattern mining workshop. The ideas we propose are (1) Dual Motivation; setting different goals for participants and organizers, (2) Solution-Problem Pair; extracting both "Solution" and "Problem," (3) Topic Card; using "Topic cards" for accelerating the dialogues, (4) Generator; positioning a "Generator" who supports the mining process, and (5) Stage Setting; making suitable atmosphere based on the theme. As a practice of these five patterns, we will use the case of "Generative Beauty Workshop: A Workshop for living your beautiful life to the fullest" held by Generative Beauty Project from Takashi Iba Laboratory. We expect these ideas would be helpful for the people who are willing to design a pattern mining workshop for their research.

Original languageEnglish
Title of host publicationProceedings of the 20th European Conference on Pattern Languages of Programs, EuroPLoP 2015
PublisherAssociation for Computing Machinery
Volume08-12-July-2015
ISBN (Electronic)9781450338479
DOIs
Publication statusPublished - 2015 Jul 8
Event20th European Conference on Pattern Languages of Programs, EuroPLoP 2015 - Irsee, Germany
Duration: 2015 Jul 82015 Jul 12

Other

Other20th European Conference on Pattern Languages of Programs, EuroPLoP 2015
CountryGermany
CityIrsee
Period15/7/815/7/12

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Akado, Y., Kogure, S., Sasabe, A., Hong, J. H., Saruwatari, K., & Iba, T. (2015). Five patterns for designing pattern mining workshops. In Proceedings of the 20th European Conference on Pattern Languages of Programs, EuroPLoP 2015 (Vol. 08-12-July-2015). [a9] Association for Computing Machinery. https://doi.org/10.1145/2855321.2855331