3rd IEEE Big Data Governance and Metadata and Management Workshop (BDMM 2018)
IEEE Big Data Governance and Metadata and Management
Seattle, Washington, USA
December 10 - 11, 2018
In conjunction with
IEEE Big Data 2018
Registration: Coming Soon!
This workshop is aligned with the effort from the IEEE Big Data Technical Community (BDTC) on Standardization (see http://bigdata.ieee.org/). The BDTC standards research group is studying on where there is a need and opportunity for developing IEEE Standards for Big Data Metadata, its management, and governance. BDMM 2018 follows the successful BDMM workshop held at IEEE BigData 2018 (http://cci.drexel.edu/bigdata/bigdata2018).
Big Data is a collection of data so large, so complex, so distributed, and growing so fast (or 5Vs- volume, variety, velocity, veracity, and value). 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?
TopicsTopics 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
- Digital object architectures and infrastructures for Big Data management
- Best practices and standard based persistent identifiers, data types registry structures and representations for 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
- Big Data governance for data integrity, quality, provenance, retention, asset management, and business intelligence
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 use the followiing formatting instructions:
8.5" x 11" x 2 (DOC, PDF, LaTex Formatting Macros)
Please use this submission site to submit your paper(s).
Accepted papers will be published in the IEEE BigData2018 proceedings (EI indexed). For further information please see IEEE BigData2018 @ http://cci.drexel.edu/bigdata/bigdata2018.
All submitted papers will be reviewed by 3 international program committees.
Hackathon: 24 hours on Data Mashup (Varieties Problem) Big Data AnalyticsGovernance and metadata management poses unique challenges with regard to the Big Data paradigm shift. It is critical to develop interoperable data infrastructure for Big Data Governance and Metadata Management that is scalable and can enable the Findability, Accessibility, Interoperability, and Reusability between heterogeneous datasets from various domains without worrying about data source and structure.
Hackathon Track#1: Personalized Medicine for Drug Targeting in Prostate Cancer Patients
Submitted and Subject Matter Expert by Dr. Elizabeth Chang
Research Fellow, Department of Radiation Oncology, University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, USA
Personalized medicine is the act of tailoring chemotherapy or drugs based on a patient’s specific set of DNA or genes. When a person is diagnosed with cancer, a variety of tests are performed (blood, DNA, urine, or tissue analysis), giving physicians a snapshot into that patient's unique set of DNA. This information allows for "smart" prescribing of medications that complement a patient's signature genetic background and achieve therapeutic response.
Problem: how do we find these biomarkers?
One approach: NCI’s Genomic Data Commons data portal is a huge data repository of over 32,000 patient cases, and includes clinical data, treatment data, or biopsy results, and over 22,000 genes, as well as a whole host of other information. This allows accessibility to other researchers who want to uncover new biomarkers, find correlations between genes and survival, or look into whatever topic they are interested in.
The President's Council of Advisors on Science and Technology (PCAST) believes that the convergence of scientific and clinical opportunity and public health need represented by personalized medicine warrants significant public and private sector action to facilitate the development and introduction into clinical practice of this promising class of new medical products… Based on these deliberations, PCAST determined that specific policy actions in the realm of genomics-based molecular diagnostics had the greatest potential to accelerate progress in personalized medicine. PCAST on Priorities for Personalized Medicine, September 2008
Tutorial and Hands-on (No biooscience background is needed but willing to work within a team is preferred)
Subject Matter Expert: Provide Genomic Data Commons data portal overview and hands-on excerise on given datasets
- Dr. Elizabeth Chang, Research Fellow, Department of Radiation Oncology, University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, USA
Note: Not all data values may be present, and some patients may have multiple records
(Click here for step-by-step instructions using Xena Browser to check questions 2 and 3)
Develop data mashup scheme based on use cases to cross reference different clinical and genomic datasets and apply statistical analysis, visualization, and machine learning tools to statistically analyze and develop predictive models for survival data, uncover new molecular biomarkers, and find correlations between genes and cancer risk. Think outside the box and come up with innovative ideas that bring more value out of the data, or choose one or more of the following to:
- Find how many patient cases are “Primary Tumor” samples from Dataset 2 using Column A “sample_type”.
- Graph overall survival (OS) by Gleason score from Dataset 2 (using Kaplan Meier plot, see Computing Environment for reference)
- Graph overall survival by target gene TP53 when Gleason score is 6
- Graph overall survival by target gene TP53 when Gleason score is categorized into 3 groups (6-7, 8, and 9-10)
- Repeat #4 using your own Gleason score categories which produces the best P-value (example: P-value <0.05)
- Languages: Python, Java, etc.
- Visualization: Kaplan Meier survival plot (example: use Python Lifelines https://github.com/CamDavidsonPilon/lifelines)
Please see A PRACTICAL GUIDE TO UNDERSTANDING KAPLAN-MEIER CURVES for more detailed description.
DatasetsTwo public available datasets shall be used:
- Dataset 1: gene expression RNAseq – IlluminaHiSeq
The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. Level 3 data was downloaded from TCGA data coordination center. This dataset shows the gene-level transcription estimates, as in log2(x+1) transformed RSEM normalized count. Genes are mapped onto the human genome coordinates using UCSC Xena HUGO probeMap.
- Dataset 2: phenotype – Phenotypes
This dataset provides clinical data including overall survival (OS), treatment regimens, cancer staging (Gleason scores), diagnostic results, histology, pathologic staging, tumor characteristics, and much more.
Hackathon Track#2: Brain Data Bank on Video Gaming Enhances Cognitive Skills
Submitted and Subject Matter Expert by Dr. David Ziegler
Director of Technology Program; Multimodal Biosensing – Neuroscape University of California San Francisco
[Reference: Nature. 2013 Sep 5; 501(7465): 97–101, doi: 10.1038/nature12486]
Cognitive control is defined by a set of neural processes that allow us to interact with our complex environment in a goal-directed manner. Humans regularly challenge these control processes when attempting to simultaneously accomplish multiple goals (multitasking), generating interference as the result of fundamental information processing limitations. It is clear that multitasking behaviour has become ubiquitous in today’s technologically dense world3, and substantial evidence has accrued regarding multitasking difficulties and cognitive control deficits in our ageing population4.
Here we show that multitasking performance, as assessed with a custom-designed three-dimensional video game (NeuroRacer), exhibits a linear age-related decline from 20 to 79 years of age. By playing an adaptive version of NeuroRacer in multitasking training mode, older adults (60 to 85 years old) reduced multitasking costs compared to both an active control group and a no-contact control group, attaining levels beyond those achieved by untrained 20-year-old participants, with gains persisting for 6 months.
These findings highlight the robust plasticity of the prefrontal cognitive control system in the ageing brain, and provide the first evidence, to our knowledge, of how a custom-designed videogame can be used to assess cognitive abilities across the lifespan, evaluate underlying neural mechanisms, and serve as a powerful tool for cognitive enhancement.
Tutorial and Hands-on (No neuroscience background is needed but willing to work within a team is preferred)
Subject Matter Experts: Provide neuroscience overview and hands-on excerise on given datasets
- Dr. David Ziegler (Tutorial), Director of Technology Program, Multimodal Biosensing, UCSF, USA
- Dr. Seth Elkin-Frankston (Hands-on), Scientist, Cognitive Systems, Charles River Analytics Inc., USA
Develop data mashup scheme based on use cases to cross reference different datasets and apply statistical analysis, visualization, and machine learning tools to statistically analyze and develop predictive models for what changed between “shoot only or single tasking” and “drive & shoot or multi-tasking” from the EEG (electroencephalography) signals. Think outside the box and come up with innovative ideas that bring more value out of the data, or choose one or more of the following to:
Beginner Challenge Questions
- What are the strengths vs. limitations of the EEG technology? How do consumer-level EEG headsets compare to laboratory-grade equipment?
- What are the realistic EEG applications in daily life (automatic driving, interactive games, Internet Marketing, etc.)? Provide convincing prototypes (virtual or real).
- Try to conduct an event-related potential (ERP) analysis of the data in one or more conditions. How does this approach compare to that used in the Nature paper (i.e., ERSP-Event-Related Spectral Perturbation or time-frequency analysis)? Hint: check out the EEGLab and Fieldtrip tutorial
- Try conducting an ICA decomposition analysis of the data (Hint: this is best done in EEGLab). How does this approach compare to that used in the Nature paper or the ERP analysis suggested above? What new information can we learn using this approach?
- Would a micro-state analysis be appropriate for the data? What new knowledge might we learn from such an approach?
- What advanced methods (e.g., deep learning, but also others) are available that would help predict post-game performance? Specifically by what mechanisms and by how much?
- Languages: MATLAB, Python, C++.
- EEG Analysis & Visualization tools (all three are free and have excellent tutorials):
- MATLAB toolbox for M/EEG analysis
- Largely command-line functionality
- Particular emphasis on analyses in the time-frequency domain
- Interactive MATLAB toolbox for processing M/EEG data
- GUI and command line options
- Particular emphasis on ICA methods for decomposing data and extracting meaningful components
- Open-source Python software for visualizing and analyzing M/EEG data
- Particularly good for source-localization analysis
- Stand-alone (C++) EEG analysis software
- Option to conduct micro-state analysis
A subset sample dataset brain_sample.zip (Under Documentation: one subject, 330MB, freely available) and Full Datasets (49 subjects, 17GB, simple registration is required via 'Login') can be download from the IEEE DataPort. Datasets contains pair “single tasking” and “multi-tasking” with the following set of files:
- Dataset 1 (group of): xxxx_DS_n.bdf where DS = “drive and shoot” or multi-tasking and n = 1,2,3, etc.
- Dataset 2 (group of): xxxx_SO_n.bdf where SO = “shoot only” or single-tasking and n = 1,2,3, etc.
- Dataset 3 (group of): xxxxB_DS_n.bdf where DS = “drive and shoot” or multi-tasking and B = POST training and n = 1,2,3, etc.
- Dataset 4 (group of): xxxxB_SO_n.bdf where SO = “shoot only” or single-tasking and B = POST training and n = 1,2,3, etc.
All data recorded with BioSemi 64 (with bdf extension) and each bdf file is about 40MB. Files with the same “xxxx” (subject name) that have the ending ‘B’ are the POST training EEG files for a given subject. Note that all participants have both a PRE and POST (but most do).
Hackathon Team, Computing Environment, and Implementation White Paper
All participants must be registered via the IEEE Big Data 2018 Registration and attend physically. You may register as a team (up to four per team) or an individual (we will place you on a team). Each participant brings his/her own laptop with all the necessary computing tools. No remote computing resources are allowed. All implementation must be based on the original work. Participating teams are encouraged to submit implementation approach as a white paper which will be published as part of the IEEE Big Data Governance and Metadata Management publication three months after the hackathon event.
- David Belanger, Chair of IEEE Big Data Technical Community, Stevens Institute of Technology
- Mahmoud Daneshmand, Vice-Chair of BDGMM, Steven Institute of Technology
- Kathy Grise, Senior Program Director, Future Directions, IEEE
- Joan Woolery, Senior Project Manager, Industry Connections, IEEE Standards Association, IEEE
- Cherry Tom, Emerging Technologies Initiatives Manager, IEEE Standards Association, IEEE
- Elizabeth Chang, Research Fellow, Department of Radiation Oncology, University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, USA
- David Ziegler, Director of Technology Program; Multimodal Biosensing – Neuroscape University of California San Francisco, USA
- Seth Elkin-Frankston, Scientist, Cognitive Systems, Charles River Analytics Inc, USA
Evaluation CriteriaTechnical Approach (40 pts)
- Data mashup (20)
- Big Data analytics (20)
Novelty (40 pts)
- Creativity (20)
- Efficiency (20)
Results (20 pts)
- Output content (10)
- Output format (10)
- IEEE Certificates for 1st, 2nd, 3rd winners
- All team members win a t-shirt
Oct 1, 2018: Paper submission deadline
Nov 1, 2018: Paper acceptance notification
Nov 15, 2018: Camera ready version due
Dec 3, 2018: Deadline for hackathon sign-up
Dec 10, 2018: Hackathon
Mar 11, 2019: Hackathon White Paper due
Day-1: Dec 10, 2018
|08:00 – 08:10||Welcome, Wo Chang, Chair of IEEE BDGMM, NIST, USA||08:10 – 08:20||Opening Remark, David Belanger, Chair of IEEE Big Data Technical Community, Stevens Institute of Technology||08:20 – 10:00|
Hackathon Briefing on use case, datasets, challenges, Q/As
Hackathon Track#1: Personalized Medicine for Drug Targeting in Prostate Cancer Patients
Dr. Elizabeth Chang (Tutorial & Hands-on), Research Fellow, Department of Radiation Oncology, University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, USA
Hackathon Track#2: Brain Data Bank on Video Gaming Enhances Cognitive Skills
Dr. David Ziegler (Tutorial), Director of Technology Program, Multimodal Biosensing, UCSF, USA
Dr. Seth Elkin-Frankston (Hands-on), Scientist, Cognitive Systems, Charles River Analytics Inc., USA
|10:00 – till next day 08:00||Solving hackathon challenges||Next day 09:00 – 12:00||Hackathon Presentation and Evaluation, See Team & Criteria|
Day-2: Dec 11, 2018
|09:00 - 12:00||Hackathon Evaluation, Evaluation Team|
|13:00 – 13:10||Welcome, Wo Chang, Chair of IEEE BDGMM, NIST||13:10 – 13:30||Opening Remark, David Belanger, Chair of IEEE Big Data Technical Community, Stevens Institute of Technology||13:30 – 14:00|| Keynote Speaker: Topic: TBD
|14:00 – 14:20||Invited Speaker on Big Data Governance Management:
|14:20 – 14:40||Invited Speaker on Big Data Metadata Management:
|15:40 – 15:00||Invited Speaker: Special Topic
|15:00 – 15:20||Coffee Break||15:20 – 15:30||Paper Presentation #1
|15:30 - 15:40||Paper Presentation #2
|15:40 – 15:50||Paper Presentation #3
|15:50 – 16:00||Paper Presentation #4
|16:00 – 16:10||Paper Presentation #5
|16:10 – 16:20||Paper Presentation #6
|16:20 - 16:30||Paper Presentation #7
|16:30 – 16:40||Paper Presentation #8
|16:40 – 16:50||Paper Presentation #9
|16:50 – 17:20||Hackathon Ceremony
David Belanger and Kathy Grise
|17:20 – 17:30||Announcement for next BDGMM Event, Wo Chang|
General Co-ChairsWo Chang
Digital Data Advisor
National Institute of Standards and Technology, USA
Convenor, ISO/IEC JTC 1/WG 9 Working Group on Big Data
Chair, IEEE Big Data Governance and Metadata Management
David Belanger (PhD)
Chair of IEEE Big Data Technical Community
Stevens Institute of Technology
Mahmoud Daneshmand (PhD)
Professor, Stevens Institute of Technology, USA
Co-Chair, IEEE Big Data Governance and Metadata Management
Co-founder, IEEE BDIs
Program Co-ChairsKathy Grise
Senior Program Director, Future Directions, IEEE Technical Activities, USA
Yinglong Xia (PhD)
Huawei Research America, USA
Co-chair, IEEE BDI - Big Data Management Standardization
Publicity ChairsCherry Tom
Emerging Technologies Intelligence Manager
IEEE Standards Association
445 Hoes Lane, Piscataway, NJ 08854-4141
Technical Program Committee
|Name||Organization||Country||Paventhan Arumugam||ERNET||India||Claire Austin||S&T Strategies,Environment & Climate Change Canada||Canada||Ismael Caballero||UCLM||Spain||Yue-Shan Chang||National Central University||Taiwan||Periklis Chatzimisios||Department of Informatics, Alexander TEI of Thessaloniki||Greece||Hung-Ming Chen||National Taichung University of Science and Technology||Taiwan||Miyuru Dayarathna||WSO2 Inc.||Sri Lanka||Jacob Dilles||Acuant Corp.||US||Robert Hsu||Chung Hua University||Taiwan||Wei Hu||Nanjing University||China||Carson Leung||University of Manitoba||Canada||Sian Lun Lau||Sunway University||Malaysia||Christian Camilo Urcuqui Lóepz||Icesi University||Colombia||Neil Miller||The bioinformatics for Children's Mercy Hospital||USA||Jinghua Min||China Electronic Cyberspace Great Wall Co., Ltd.||China||Carlos Monroy||Rice University||US||Huansheng Ning||USTB||China||Arindam Pal||TCS Research||India||Lijun Qian||Prairie View A&M University||USA||Weining Qianx||East China Normal University||China||Yufei Ren||IBM||USA||Robby Robson||Eduworks Corporation||US||Angelo Simone Scotto||European Food Safety Authority||Italy||Priyaa Thavasimani||Newcastle University||UK||Alex Thomo||University of Victoria||Canada||Chongang Wang||InterDigital Communications||USA||Jianwu Wang||University of Maryland, Baltimore County||US||Shu-Lin Wang||National Taichung University of Science and Technology||Taiwan||Jens Weber||University of Victoria||Canada||Lingfei Wu||IBM Research||USA||Hao Xu||University of North Carolina at Chapel Hill||US||Godwin Yeboah||University of Warwick||UK||Tim Zimmerlin||Automation Technologies||US|