US-Japan Workshop: June 5 – 6 2017

1st US-Japan Workshop Enabling Global Collaborations in Big Data Research

J.W. Marriott Hotel, Buckhead, Atlanta, GA, USA June 5 – 6, 2017 


Workshop Objective

This workshop will bring together researchers from around the world to discuss experiences, challenges, and opportunities in transnational and international collaborative research on information technology (IT) and IT-supported applications, with focus on active collaborations between the United States (U.S.) and Japan. The workshop will provide opportunities for participants to interact directly and promote collaborative research activities. The collaboration is expected to achieve scientific knowledge that would be difficult to obtain individually.

Workshop attendance will be by invitation only, following response to this call as outlined below. Prospective attendees are expected to be investigators on funded research projects, and should submit papers or whitepaper for consideration by the program committee. Workshop organizers are requesting funding from NSF and JST to support partially the travel costs and registration fees of invited attendees from the U.S. and Japan. Principal Investigators of projects in relevant NSF and JST programs are strongly encouraged to apply. Additionally, early career researchers and underrepresented minorities are encouraged to apply.

Priority Research Topics

Topics of interest for collaboration include, but are not limited to:

  • Big Data Fundamental Technologies and Applications: novel techniques, methodologies, and technologies in computer science, statistics, computational science, and mathematics, together with innovative applications in domain science, which may include, for example, disaster management.
  • Smart and Connected Communities (SCC): strongly interdisciplinary, integrative research that will improve understanding of smart and connected communities and lead to discoveries that enable sustainable change to enhance community functioning.
  • Cyber-Physical Systems (CPS) and Internet of Things (IoT): system science needed to engineer complex, reliable cyber-physical systems that people can use, interact with, and depend upon, namely the cross-cutting fundamental scientific and engineering principles that underpin the integration of cyber and physical elements across all application sectors.
  • Artificial Intelligence and Machine Learning: research enabling computational understanding and modeling of intelligence in complex, realistic contexts, or more generally, processing and functionality to address data of unprecedented scale, complexity, heterogeneity, etc.



Please find the agenda here. [download id=”22″]

White Papers

These whitepapers are being distributed for open access as part of the US-Japan Workshop, co-located with 2017 ICDCS, Atlanta, GA. For citations to work described in the whitepapers, please contact the authors.

[download id=”20″]


Regular Papers

These regular papers are a part of the 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW) Proceedings.

[download id=”21″]


Organizing Committee

Workshop Organizers

Calton Pu, Georgia Institute of Technology, USA

Masaru Kitsuregawa, University of Tokyo, Japan

Program Committee

Jose Fortes, University of Florida, USA

Etsuya Shibayama, University of Tokyo, Japan

Qingyang Wang, Louisiana State University, USA

A new publication by GRAIT-DM team has been added

We added a new book chapter “Multi-hazard Detection by Integrating Social Media and Physical Sensors” developed by the GRAIT-DM team and published by Springer: see the Publications section of the portal.

Here is the abstract from the book chapter:
Disaster Management is one of the most important functions of the government. FEMA and CDC are two examples of government agencies directly charged with handling disasters, whereas USGS is a scientific agency oriented towards disaster research. But regardless of the type or purpose, each of the mentioned agencies utilizes Social Media as part of its activities. One of the uses of Social Media is in detection of disasters, such as earthquakes. But disasters may lead to other kinds of disasters, forming multi-hazards such as landslides. Effective detection and management of multi-hazards cannot rely only on one information source. In this chapter, we describe and evaluate a prototype implementation of a landslide detection system LITMUS, which combines multiple physical sensors and Social Media to handle the inherent varied origins and composition of multi-hazards. Our results demonstrate that LITMUS detects more landslides than the ones reported by an authoritative source.

LITMUS: Landslide Detection by Integrating Multiple Sources

A new paper has been added to the Publications page: “LITMUS: Landslide Detection by Integrating Multiple Sources” by our team of Aibek Musaev, De Wang and Calton Pu. We will be presenting it at the ISCRAM conference later this year. In this paper, we evaluate a landslide detection system LITMUS, which combines multiple physical sensors and social media to handle the inherent varied origins and composition of multi-hazards. LITMUS integrates near real-time data from USGS seismic network, NASA TRMM rainfall network, Twitter, YouTube, and Instagram. The landslide detection process consists of several stages of social media filtering and integration with physical sensor data, with a final ranking of relevance by integrated signal strength.

NSF & JST Workshop: May 23-24

A joint NSF and JST Workshop: Examining and Prioritizing Collaborative Research Opportunities in Big Data and Disaster Research Washington, D.C., USA, May 23-24, 2013

Workshop website:

A team composed of scientists from each Japan and the USA will meet May 23-24, 2013 in Washington, D.C. The ultimate goal of this workshop is to provide a consensus report that identifies and prioritizes key strategic areas where Research on Big Data and Disaster Research offers the greatest opportunity to advance novel scientific knowledge, and to foster greater collaborative interactions between Japan and US scientists in these priority research areas. This report will be considered by NSF and JST in developing future and joint Research Funding Announcements. The workshop will emphasize basic science and fundamental discovery as well as innovations for resilient and sustainable society.

Attendance to this workshop is by invitation only and space is limited. If you are interested in attending and have not been invited, please contact Prof. Calton Pu at for further information.

DBpedia datasets

DBpedia is a project aiming to extract structured content from the information created as part of the Wikipedia project. Wikipedia articles consist mostly of free text, but also include structured information embedded in the articles, such as “infobox” tables, categorisation information, images, geo-coordinates and links to external Web pages. This structured information is extracted and put in a uniform dataset which can be queried.

DBpedia also provides its datasets in a downloadable format. One of the most relevant downloads for our project is geographic coordinates: see here.