Standards

 

IEEE Big Data Governance and Metadata Management (BDGMM)

Developing an interoperable data infrastructure through extensible governance and metadata lifecycle framework.

Visit the website for more information.

 

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

This workshop is partially aligned with the effort from the IEEE Big Data Initiative (BDI) on Standardization (see http://bigdata.ieee.org/) 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.

Visit the website for more information.

 

1st IEEE Big Data Initiative (BDI) Standards Workshop (BDISW)

The 1st IEEE Big Data Initiative Standards Workshop was held in collaboration with the IEEE Reliability Society's ISSRE 2015 conference at NIST Headquarters in Gaithersburg, MD on 2 November 2015. Through this workshop, IEEE BDI identified areas of need and opportunity for standardization of data-related technologies.

Please access workshop presentations below. Additional presentations are coming soon.

TopicChampionsDescriptionDownload PDF
Metadata Standard for Big Data Management Alex Kuo and Yinglong Xia

Our primary objective was to form a research group to study on where there is a need and opportunity for developing a Metadata Standard for Big Data Management.
We also propose to develop a portal as a proof-of-concept to facilitate easy big data annotation, discovery, and interpretation by using the metadata standard.

Download (PDF, 806 KB)
Mobile Health Platform N. Keshava, C. Carey, D. Hudson, W. Malik

The development of mobile technology as platforms for measuring physiological and behavioral parameters is a rapidly growing area. While there is great interest in using mobile technology platforms to collect persistent measurement, translating those measurements reliably into clinical insight is a major leap.
Areas of potential interest and applicability:

  1. Inference of physiological parameters during clinical trials as possible endpoints and surrogate biomarkers;
  2. Estimation of cognitive state (e.g., after injury, during recovery);
  3. Interoperability between platforms
  4. Visualization
  5. Methods for addressing missing data, artifacts, etc.
Download (PDF, 94 KB)
Curation of EHRs for Reuse N. Keshava, C. Carey, D. Hudson, W. Malik

EHRs and payer/claims databases are a potential source of value to a wide variety of health care stakeholders. However, longitudinal patient records suffer from a wide variety of distortions ranging from missing data, gaps in coverage, inconsistent medical coding, and different standards of care. Moreover, different commercial vendors currently employ different formats and schema for collecting data.
We propose to develop standards for the curation of EHRs and payer/claims databases to enable key information products to be derived with the least variation. These products can provide the foundation for advanced applications and services. Examples of possible curation algorithms could include patient matching algorithms, data imputation algorithms, and algorithms that generally reconstruct the patient journey through the medical system using medical records.

Download (PDF, 94 KB)
Data Representation in Big Data Management Yinglong Xia, IBM Research Represent massive data into a format based on (a set of) property graph models, with none or multiple proprietorial interactions among items Properties are organized as an extended JSON or YAML Properties can be partitioned for partial (de)serialization Download (PDF, 10 MB)
Data Access Interface (API/Query Languages) Yinglong Xia, IBM Research Enhanced property graph access interface Derivative Key-Value Store API (vertices w/ non-nested 12-CRS- 0106 REVISED 8 FEB 2013 non-partitioned property) Derivative Column Store API (vertices w/ non-nested partitioned property) Derivative DocDB API (vertices w/ full property) Coming Soon
Big Data Metadata Standards Robby Robson, Eduworks Corporation Big data presents large volumes of data in varied formats and types for analysis. To expose this data as a successful strategic information management initiative, its metadata must be suitably defined. This research helps information leaders to leverage metadata management in big data initiatives. Download (PDF, 1.61 MB)
Wireless Sensor Networks and Big Data Fernando Velez, University of Beira Interior, Portugal Coming Soon Coming Soon
Smart Grid and Big Data Melike Erol-Kantarci, Clarkson University, NY Coming Soon Download (PDF, 2.16 MB)
Energy efficient Data Acquisition Houbing Song, West Virginia University, WV Coming Soon Coming Soon
Green Datacenters and Big Data Dzmitry Kliazovich, University of Luxembourg Coming Soon Coming Soon
Wireless & Device Big Data Analytics Ye Ouyang, Verizon Wireless Coming Soon Download (PDF, 153 KB)
Mobile Big Data Kan Zheng, Beijing University of Posts & Telecommunications, China Coming Soon Coming Soon
Big Data for 5G Networks Ahmed Zoha, Qatar Mobility Innovations Center, Qatar Coming Soon Coming Soon
Mobile Cloud Computing (MCC) and Big Data Constantinos Mavromoustakis, University of Nicosia, Cyprus Coming Soon Coming Soon