Mobile Computing, Applications, and Services. 9th International Conference, MobiCASE 2018, Osaka, Japan, February 28 – March 2, 2018, Proceedings

Research Article

Making Pier Data Broader and Deeper:

  • @INPROCEEDINGS{10.1007/978-3-319-90740-6_1,
        author={Takeshi Kurata and Ryosuke Ichikari and Ryo Shimomura and Katsuhiko Kaji and Takashi Okuma and Masakatsu Kourogi},
        title={Making Pier Data Broader and Deeper:},
        proceedings={Mobile Computing, Applications, and Services. 9th International Conference, MobiCASE 2018,  Osaka, Japan, February 28 -- March 2, 2018, Proceedings},
        proceedings_a={MOBICASE},
        year={2018},
        month={5},
        keywords={Lab-forming fields Field-forming labs Big data Deep data Pier data PDR IoT IoH VR Service engineering},
        doi={10.1007/978-3-319-90740-6_1}
    }
    
  • Takeshi Kurata
    Ryosuke Ichikari
    Ryo Shimomura
    Katsuhiko Kaji
    Takashi Okuma
    Masakatsu Kourogi
    Year: 2018
    Making Pier Data Broader and Deeper:
    MOBICASE
    Springer
    DOI: 10.1007/978-3-319-90740-6_1
Takeshi Kurata,*, Ryosuke Ichikari1,*, Ryo Shimomura, Katsuhiko Kaji2,*, Takashi Okuma1,*, Masakatsu Kourogi,*
  • 1: National Institute of Advanced Industrial Science and Technology
  • 2: Aichi Institute of Technology
*Contact email: t.kurata@aist.go.jp, r.ichikari@aist.go.jp, kaji@aitech.ac.jp, takashiokuma@aist.go.jp, m.kourogi@aist.go.jp

Abstract

Big data can be gathered on a daily basis, but it has issues on its quality and variety. On the other hand, deep data is obtained in some special conditions such as in a lab or in a field with edge-heavy devices. It compensates for the above issues of big data, and also it can be training data for machine learning. Just like a platform of pier supported by stakes, there is structure in which big data is supported by deep data. That is why we call the combination of big and deep data “pier data.” By making pier data broader and deeper, it becomes much easier to understand what is happening in the real world and also to realize Kaizen and innovation. We introduce two examples of activities on making pier data broader and deeper. First, we outline “PDR Challenge in Warehouse Picking”; a PDR (Pedestrian Dead Reckoning) performance competition which is very useful for gathering big data on behavior. Next, we discuss methodologies of how to gather and utilize pier data in “Virtual Mapping Party” which realizes map-content creation at any time and from anywhere to support navigation services for visually impaired individuals.