IEEE Workshop on Big Data Metadata and Management
(BDMM '2016)

Washington DC, USA
Dec 5 or 8 (TBD), 2016

In conjunction with the 2016 IEEE International Conference on Big Data
(Big Data 2016 @

Sponsored by IEEE Big Data Initiative (BDI)


This workshop is partially aligned with the effort from the IEEE Big Data Initiative (BDI) on Standardization (see The BDI standard research group is studying on where there is a need and opportunity for developing IEEE Standards for Big Data Metadata and Management.

Big Data is a collection of data so large, so complex, so distributed, and growing so fast (or 5Vs- volume, variety, velocity, veracity, and vinculation). It has been known for unlocking new sources of economic values, providing fresh insights into sciences, and assisting on policy making. However, Big Data is not practically consumable until it can be aggregated and integrated into a manner that a computer system can process. For instance, in the Internet of Things (IoT) environment, there is a great deal of variation in the hardware, software, coding methods, terminologies and nomenclatures used among the data generation systems. Given the variety of data locations, formats, structures and access policies, data aggregation has been extremely complex and difficult. More specifically, a health researcher was interested in finding answers to a series of questions, such as "How is the gene 'myosin light chain 2' associated with the chamber type hypertrophic cardiomyopathy? What is the similarity to a subset of the genes' features? What are the potential connections among pairs of gene". To answer these questions, one may retrieve information from databases he knows, such as the NCBI Gene database or PubMed database. In the Big Data era, it is highly likely that there are other repositories also storing the relevant data. Thus, we are wondering

  • is there an approach to manage such big data, so that a single search engine available to obtain all relevant information drawn from a variety of data sources and to act as a whole?
  • How do we know if the data provided is related to the information contained in our study?

To achieve this objective, we need a mechanism to help us describe a digital source so well that allows it to be understood by both human and machine. Metadata is "data about data". It is descriptive information about a particular dataset, object or resource, including how it is formatted, and when and by whom it is collected. With those information, the finding of and the working with particular instances of Big Data would become easier. Besides, the Big Data must be managed effectively. This has partially manifested in data models a.k.a. "NoSQL".

The goal of this multidisciplinary workshop is to gather both researchers and practitioners to discuss methodological, technical and standard aspects for Big Data management. Papers describing original research on both theoretical and practical aspects of metadata for Big Data management are solicited.


Topics include, but are not limited to:

  • Metadata standard(s) development for Big Data management
  • Methodologies, architecture and tools for metadata annotation, discovery, and interpretation
  • Case study on metadata standard development and application
  • Metadata interoperability (crosswalk)
  • Metadata and Data Privacy
  • Metadata for Semantic Webs
  • Human Factors on Metadata
  • Innovations in Big Data management
  • Opportunities in standardizing Big Data management
  • Query languages and ontology in Big Data
  • NoSQL databases and Schema-less data modeling
  • Multimodal resource and workload management
  • Availability, reliability and Fault tolerance
  • Frameworks for parallel and distributed information retrieval
  • Domain standardization for Big Data management

Paper submission instructions

This workshop will only accept for review original papers that have not been previously published. Papers should be formatted based on the IEEE Transactions journals and conferences style; maximum allowed camera-ready paper length is ten (10) pages. Submissions must be in Adobe PDF format, including text, figures and references. Please use the following submission site to submit your paper(s):

Workshop website: TBD

Accepted papers will be published in the IEEE BigData2016 proceedings (EI indexed). For further information please see IEEE BigData2016 web page @

Important Dates

Oct 15, 2016: Due date for full workshop papers submission
Oct 25, 2016: Notification of paper acceptance to authors
Nov 15, 2016: Camera-ready of accepted papers
Dec 5 or 8, 2016: Workshops (TBD)

Review procedure

All submitted paper will be reviewed by 3 international program committees.

Workshop Organizers

General Co-Chairs

Alex Mu-Hsing Kuo (PhD),

University of Victoria, Canada

Leader, IEEE Big Data Education Tracks

Co-chair, IEEE BDI - Big Data Management Standardization


Mahmoud Daneshmand (PhD),

Professor, Stevens Institute of Technology, USA

Co-founder, IEEE BDIs


Program Co-Chairs

Yinglong Xia (PhD),

Huawei Research America, USA

Co-chair, IEEE BDI - Big Data Management Standardization


Chonggang Wang (PhD),

InterDigital Communications, USA

Co-founder, IEEE BDI

Publicity Chair

Lijun Qian (PhD),

Prairie View A&M University, USA


Prairie View A&M University, USA

Technical Program Committee

    Name    Organization    Country
    Miyuru Dayarathna    WSO2 Inc.    Sri Lanka
    Kathy Grise    IEEE    USA
    Wei Hu    Nanjing University    China
    Carson Leung    University of Manitoba    Canada
    Huansheng Ning    USTB    China
    Arindam Pal    TCS Research    India
    Lijun Qian    Prairie View A&M University    USA
    Weining Qian    East China Normal University    China
    Yufei Ren    IBM    USA
    Alex Thomo    University of Victoria    Canada
    Cherry Tom    IEEE    USA
    Jens Weber    University of Victoria    Canada
    Lingfei Wu    College of William and Mary    USA

Keynote Speaker


Panel Discussion

Topic:Challenges and Opportunities in Standardizing Big Data Management

Big Data must be managed. The vinculation nature of Big Data motivates us to explore heterogenous resources as they may inherently connected. However, if not managed properly, it is challenging to interpret the data from other domains, possibly due to different data representations, definitions, and more. Because of the high volume and velocity natures, it is critical to standardize the data management as early as possible; otherwise, significant efforts may be needed to process the data as the volume increases. In this panel, we will discuss how standards can be made in this domain by jointly discussing with experts from both the industry standardization community with IEEE background and various leading companies in industry.

The panel discussion is to address various issues in managing big data using metadata and/or other relevant techniques, from the perspective of international standards. The panel discussion will involve panelists from both standardization community and the big data management community.


========= begin ===========

9:00am~ 9:10am Opening Remark: IEEE Big Data Initiative (BDI) - Mahmoud Daneshmand (Stevens Institute of Technology)

Session 1


  • MetaStore: Metadata Framework for Scientific Data Repository - Ajinkya Prabhune, Anil Keshav, Hasebullah Ansari, Rainer Stotzka(Karlsruhe Institute of Technology) Michael Gertz, Juergen Hesser(Heidelberg University)

  • Fault-tolerant Data Transfer Strategy Using Bandwidth Scheduling Service in High-performance Networks - Liudong Zuo (California State University Dominguez Hills) Michelle Zhu (Montclair State University)

  • 10:00am-10:20am Coffee Break

  • Keynote
  • Standards for Big Datasets - Robby Robson (CEO of Eduworks Corp.)

  • Session II: Computational Management

  • Detecting Spammers on Social Networks Based on a Hybrid modeling- Guangxia Xu, et. al.(CQUPT, China)
  • Linked Data Platform for Building Resilience Based Applications and Connecting API Access Points with Data Discovery Techniques - Holly Ferguson (University of Notre Dame)
  • Constellation: A Science Graph Network for Scalable Data and Knowledge Discovery in Extreme-Scale Scientific Collaborations- Sudharshan S. Vazhkudai, et. al.(Oak Ridge National Laboratory)

  • 3:30pm-3:50pm Coffee Break

  • Panel Discussion
  • Challenges and Opportunities in Standardizing Big Data Management - Moderator: Alex KuoPanelists: Robby Robinson, Cherry Tom, Mahmoud Daneshmand, Kathy Grise, Yinglong Xia
  • Close Remark- Yinglong Xia (Huawei Research America)

  • ============= end =============

    WiFi Code: BD2016


    Information on the Keynote Speech

    Title: Standards for Big Datasets

    Abstract: "Big Data" poses challenges to those who want to discover it and analyze it, and it is a thesis of this Big Data Metadata and Management workshop that these tasks can be made easier through metadata standards. This introductory keynote will examine this thesis from three perspectives. First, we will review and summarize the relevant challenges, technologies, and research presented in the papers that are to be delivered in the workshop. This review will set the stage and provide context. Second, we will discuss general principles of standardization, including when it is appropriate, when it tends to be successful and impactful, and how the process works. This will provide background that can inform any standardization efforts proposed by workshop participants. Finally, we will explore potential standardization paths and how they might enhance big data management. This exploration will pay special attention to semantic interoperability and automated methods, which are two of the most common themes in workshop papers.

    Bio: Dr. Robby Robson is a former mathematician, current entrepreneur, and longtime standards wonk. During his career he has made fundamental contributions to the mathematical field of semi-algebraic geometry, to online learning and related technologies, to the theory and practice of reusability and reusable design, to competency-based education and training, and to metadata standards and automated metadata extraction. In the standards world, Dr. Robson served as chair of the IEEE Learning Technology Standards Committee during its most productive period, has been on the executive committee of the IEEE Computer Society Standards Activity Board for most of the last decade, served on the IEEE Standards Association (IEEE-SA) Standards Board for two years, and is currently the IEEE-SA representative to the IEEE Future Directions Committee. He has participated in IEEE Industry Connections, IMS Global Learning Consortium, Learning Resource Metadata Initiative, and other standards activities as well. In the research world, Dr. Robson has served as the principle investigator on numerous projects funded by the NSF, DOD, and other agencies, and in the business world he is co-founder and CEO of Eduworks Corporation, a company that applies machine learning and natural language processing to solutions that unlock human potential.