The Second IEEE Workshop on Human Machine Collaboration in BigData (HMData2018)

The Second IEEE Workshop on
Human-in-the-loop Methods and
Human Machine Collaboration in BigData
(IEEE HMData2018)

co-located with IEEE Bigdata 2018
Seattle, Dec. 10th (Tentative)

Photo courtesy of wikimedia commons
AboutKeynotesDatesSubmission Program Organization HMData 2017

About IEEE HMData 2018


Human power is a key factor to maximize the impact of bigdata technologies. This workshop addresses human-in-the-loop approaches in bigdata lifecycle - in collecting, processing, analyzing, utilizing, archiving and disposing them. The purpose of this workshop is to give excellent opportunities for students, researchers and practitioners to identify important research problems and exchange their ideas on human-in-the-loop in the bigdata context. To make the workshop an attractive place for those people, we solicit practitioner papers as well as research papers, in order to facilitate discussion among researchers who know solutions and practitioners who know problems. We also would like to make the place valuable for young researchers. All papers accepted for the workshop will be included in the Workshop Proceedings published by the IEEE Computer Society Press, made available at the Conference.


This workshop covers a wide range of human-related topics in the bigdata context, such as crowdsourcing, collaborative recommendation, crowdsensing, workflow model for humans and machines, incentives, human-assisted bigdata analysis, bigdata-human interaction, supporting tools for humans in human-in-the-loop systems, security and privacy in human-machine collaboration , human factors and ELSI (ethical, legal and social issues) in human-in-the-loop systems, and human-machine collaboration in real-world problems.


Dan Weld (University of Washington)

Bio: Daniel S. Weld is Thomas J. Cable / WRF Professor in the Paul G. Allen School of Computer Science & Engineering and Entrepreneurial Faculty Fellow at the University of Washington. After formative education at Phillips Academy, he received bachelor's degrees in both Computer Science and Biochemistry at Yale University in 1982. He landed a Ph.D. from the MIT Artificial Intelligence Lab in 1988, received a Presidential Young Investigator's award in 1989, an Office of Naval Research Young Investigator's award in 1990, was named AAAI Fellow in 1999 and deemed ACM Fellow in 2005. Dan was a founding editor for the Journal of AI Research, was area editor for the Journal of the ACM, guest editor for Computational Intelligence and Artificial Intelligence, and was Program Chair for AAAI-96. Dan has published two books and scads of technical papers. Dan is an active entrepreneur with several patents and technology licenses. He co-founded Netbot Incorporated, creator of Jango Shopping Search (acquired by Excite), AdRelevance, a monitoring service for internet advertising (acquired by Nielsen NetRatings), and data integration company Nimble Technology (acquired by Actuate). Dan is a Venture Partner at the Madrona Venture Group and on the Scientific Advisory Boards of the Allen Institute for Artificial Intelligence and the Madrona Venture Group. Dan teaches many courses, including graduate classes on Artificial Intelligence, Extracting, Managing & Personalizing Web Information and Intelligent User Interfaces, and undergraduate classes on Artficial Intelligence, Advanced Internet Systems, and Machine Learning. In 2012, Dan co-organized a workshop on Crowdsourcing Personalized Online Education. During sabbaticals Dan was a visiting professor at Griffith University in Brisbane, Australia and visited the VIBE group at Microsoft Research.

Important Dates (Tentative)

  • Oct 10, 2018: Due date for full workshop papers submission
  • Nov 1, 2018: Notification of paper acceptance to authors
  • Nov 15, 2018: Camera-ready of accepted papers
  • Dec 10-13, 2018: Workshops


All submissions must be submitted electorically through CyberChair. Please prefix your submission category such as [Research Paper] to the Title of Paper field in the submission page. For example, if you would like to submit a project-in-progress paper "Crowd-centric Approach to Digital Archive Maintenance," you have to put "[project-in-progress paper] Crowd-centric Approach to Digital Archive Maintenance" into the Title of Paper field.
All papers accepted for the workshop will be included in the Workshop Proceedings published by the IEEE Computer Society Press, made available at the Conference.

Submission Categories (Tentative)

  • Research Papers (*) (long presentation): They report significant and original results relevant to the scope of this workshop. We solicit innovative or thought-provoking work but they do not necessarily have to reach the level of completion. The expected length is between 4 and 6 pages. The maximum length is 10 pages, though the paper should be commensurate with the size of the contribution.
  • Practitioner papers (*)(long presentation): They present interesting problems that require human-in-the-loop solutions in a variety of application domains, or present the interesting results of applying existing human-in-the-loop solutions to their domains. The expected length is between 4 and 6 pages. The maximum length is 10 pages, though the paper should be commensurate with the size of the contribution.
  • Project-in-progress papers (short presentation): They present the goals, challenges, and preliminary results of research or real-world projects in progress. The maximum length is 3 pages.
(*) Some of the papers submitted to the research or practitioner paper categories may be accepted as project-in-progress papers and allotted to short presentation slots.


Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines in the IEEE Bigdata 2018 CFP page



Senjuti Basu Roy (NJIT)
Lei Chen (HKUST)
Atsuyuki Morihsima (Univesity of Tsukuba)

Program Committee (to be extended)

Sihem Amer-Yahia (CNRS/LIG)
Yukino Baba (University of Tsukuba)
Wolf-Tilo Balke (Technische Universitat Braunschweig)
Adam Bradley (Amazon)
Marina Danilevsky (IBM Research Almaden )
Yoshiharu Ishikawa (Nagoya University)
Vana Kalogeraki (Athens University of Economics and Business)
Dongwon Lee (Penn State University)
John O\'Donovan (Univerisity of California Santa Barbara)
Satoshi Oyama (Hokkaido University)
Nobuyoshi Shimizu (Yahoo!Japan Research)
Siddharth Suri (Microsoft Research)
Keishi Tajima (Kyoto University)
Saravanan Thirumuruganathan (QCRI)
Vladimir Zadorozhny (University of Pittsburgh)
Demetris Zeinalipour (Max Planck Institute for Informatics and University of Cyprus)
Jing Zhang (Nanjing U. of Science & Technology)
Yudian Zheng (Twitter)