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


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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 genes”. 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

Sep 20, 2016: Due date for full workshop papers submission 

Oct 20, 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 papers 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

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Mahmoud Daneshmand (PhD)

Professor, Stevens Institute of Technology, USA

Co-founder, IEEE BDIs

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Program Co-Chairs

Yinglong Xia (PhD),

Huawei Research America, USA

Co-chair, IEEE BDI - Big Data Management Standardization

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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
Lijun Qian Prairie View A&M University USA
Weining Qian East China Normal University


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. 

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