NIST Special Publication 1500-7
	

NIST Big Data Interoperability 

Framework: 

Volume 7, Standards Roadmap
	

Final Version 1 

NIST Big Data Public Working Group 

Technology Roadmap Subgroup 

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.SP.1500-7 
 

 

NIST Special Publication 1500-7 

NIST Big Data Interoperability Framework: 

Volume 7, Standards Roadmap 

Final Version 1 

NIST Big Data Public Working Group (NBD-PWG) 

Technology Roadmap Subgroup 

Information Technology Laboratory 

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.SP.1500-7 

September 2015 

U. S. Department of Commerce 

Penny Pritzker, Secretary 

National Institute of Standards and Technology 

Willie May, Under Secretary of Commerce for Standards and Technology and Director 
 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

National Institute of Standards and Technology (NIST) Special Publication 1500-7 

46 pages (September 16, 2015) 

NIST Special Publication series 1500 is intended to capture external perspectives related to NIST 

standards, measurement, and testing-related efforts. These external perspectives can come from 

industry, academia, government, and others. These reports are intended to document external 

perspectives and do not represent official NIST positions. 

Certain commercial entities, equipment, or materials may be identified in this document in order to describe an 

experimental procedure or concept adequately. Such identification is not intended to imply recommendation or 

endorsement by NIST, nor is it intended to imply that the entities, materials, or equipment are necessarily the best 

available for the purpose. 

There may be references in this publication to other publications currently under development by NIST in 

accordance with its assigned statutory responsibilities. The information in this publication, including concepts and 

methodologies, may be used by federal agencies even before the completion of such companion publications. Thus, 

until each publication is completed, current requirements, guidelines, and procedures, where they exist, remain 

operative. For planning and transition purposes, federal agencies may wish to closely follow the development of 

these new publications by NIST. 

Organizations are encouraged to review all draft publications during public comment periods and provide feedback 

to NIST. All NIST publications are available at http://www.nist.gov/publication-portal.cfm. 

National Institute of Standards and Technology
 

Attn: Wo Chang, Information Technology Laboratory
 

100 Bureau Drive (Mail Stop 8900) Gaithersburg, MD 20899-8930
 

Email: SP1500comments@nist.gov
 

ii 
 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Reports on Computer Systems Technology 

The Information Technology Laboratory (ITL) at NIST promotes the U.S. economy and public welfare by 

providing technical leadership for the Nation’s measurement and standards infrastructure. ITL develops 

tests, test methods, reference data, proof of concept implementations, and technical analyses to advance 

the development and productive use of information technology (IT). ITL’s responsibilities include the 

development of management, administrative, technical, and physical standards and guidelines for the 

cost-effective security and privacy of other than national security-related information in federal 

information systems. This document reports on ITL’s research, guidance, and outreach efforts in IT and 

its collaborative activities with industry, government, and academic organizations. 

Abstract 

Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, 

information-driven world. While opportunities exist with Big Data, the data can overwhelm traditional 

technical approaches, and the growth of data is outpacing scientific and technological advances in data 

analytics. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is 

working to develop consensus on important fundamental concepts related to Big Data. The results are 

reported in the NIST Big Data Interoperability Framework series of volumes. This volume, Volume 7, 

contains summaries of the work presented in the other six volumes and an investigation of standards 

related to Big Data. 

Keywords 

Big Data; Big Data Application Provider; Big Data characteristics; Big Data taxonomy; Big Data 

standards; Data Consumer; Data Provider; Management Fabric; reference architecture; Security and 

Privacy Fabric; System Orchestrator; use cases. 

iii 
 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Acknowledgements 

This document reflects the contributions and discussions by the membership of the NBD-PWG, cochaired by Wo Chang of the NIST ITL, Robert Marcus of ET-Strategies, and Chaitanya Baru, University 

of California San Diego Supercomputer Center. 

The document contains input from members of the NBD-PWG: Technology Roadmap Subgroup, led by 

Carl Buffington (Vistronix), David Boyd (InCadence Strategic Solutions), and Dan McClary (Oracle); 

Definitions and Taxonomies Subgroup, led by Nancy Grady (SAIC), Natasha Balac (SDSC), and Eugene 

Luster (R2AD); Use Cases and Requirements Subgroup, led by Geoffrey Fox (University of Indiana) and 

Tsegereda Beyene (Cisco); Security and Privacy Subgroup, led by Arnab Roy (Fujitsu) and Akhil 

Manchanda (GE); and Reference Architecture Subgroup, led by Orit Levin (Microsoft), Don Krapohl 

(Augmented Intelligence), and James Ketner (AT&T). 

NIST SP1500-7, Version 1, has been collaboratively authored by the NBD-PWG. As of the date of this 

publication, there are over six hundred NBD-PWG participants from industry, academia, and government. 

Federal agency participants include the National Archives and Records Administration (NARA), National 

Aeronautics and Space Administration (NASA), National Science Foundation (NSF), and the U.S. 

Departments of Agriculture, Commerce, Defense, Energy, Health and Human Services, Homeland 

Security, Transportation, Treasury, and Veterans Affairs. 

NIST acknowledges the specific contributionsa

 to this volume by the following NBD-PWG members: 

Chaitan Baru Pavithra Kenjige Shawn Miller 

University of California, San PK Technologies U.S. Department of Veterans 

Diego, Supercomputer Center 

David Boyd 

Brenda Kirkpatrick 

Hewlett-Packard 

Affairs 

William Miller 

InCadence Strategic Services 

Carl Buffington 

Donald Krapohl 

Augmented Intelligence 

MaCT USA 

Sanjay Mishra 

Vistronix 

Pw Carey 

Luca Lepori 

Data Hold 

Verizon 

Quyen Nguyen 

Compliance Partners, LLC Orit Levin NARA 

Wo Chang Microsoft John Rogers 

NIST Jan Levine Hwelett-Packard 

Yuri Demchenko kloudtrack Doug Scrimager 

University of Amsterdam 

Nancy Grady 

Serge Mankovski 

CA Technologies 

Slalom Consulting 

Cherry Tom 

SAIC Robert Marcus IEEE-SA 

Keith Hare ET-Strategies Timothy Zimmerlin 

JCC Consulting, Inc. 

Bruno Kelpsas 

Gary Mazzaferro 

AlloyCloud, Inc. 

Automation Technologies Inc. 

Microsoft Consultant 

The editors for this document were David Boyd, Carl Buffington, and Wo Chang 

a “Contributors” are members of the NIST Big Data Public Working Group who dedicated great effort to prepare 

and substantial time on a regular basis to research and development in support of this document. 

iv 
   

         

             

       

     

   

   

       

     

         

   

   

     

     

   

           

     

                   

         

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Table of Contents 

EXECUTIVE SUMMARY ...................................................................................................................................... VI
 

1 INTRODUCTION ......................................................................................................................................... 1
 

1.1 BACKGROUND ..................................................................................................................................................1
 

1.2 NIST BIG DATA PUBLIC WORKING GROUP ............................................................................................................2
 

1.3 SCOPE AND OBJECTIVES OF THE TECHNOLOGY ROADMAP SUBGROUP..........................................................................3
 

1.4 REPORT PRODUCTION........................................................................................................................................3
 

1.5 FUTURE WORK ON THIS VOLUME .........................................................................................................................4
 

2 BIG DATA DEFINITION................................................................................................................................ 5
 

2.1 BIG DATA DEFINITIONS ......................................................................................................................................5
 

2.2 DATA SCIENCE DEFINITIONS ................................................................................................................................5
 

3 INVESTIGATING THE BIG DATA ECOSYSTEM ............................................................................................... 7
 

3.1 USE CASES.......................................................................................................................................................7
 

3.2 REFERENCE ARCHITECTURE SURVEY ......................................................................................................................9
 

3.3 TAXONOMY......................................................................................................................................................9
 

4 BIG DATA REFERENCE ARCHITECTURE .......................................................................................................11
 

4.1 OVERVIEW.....................................................................................................................................................11
 

4.2 NBDRA CONCEPTUAL MODEL ..........................................................................................................................11
 

5 BIG DATA SECURITY AND PRIVACY ............................................................................................................14
 

6 BIG DATA STANDARDS..............................................................................................................................15
 

6.1 EXISTING STANDARDS ......................................................................................................................................16
 

6.2 GAP IN STANDARDS .........................................................................................................................................34
 

6.3 PATHWAY TO ADDRESS STANDARDS GAPS ...........................................................................................................35
 

ACRONYMS A: ACRONYMS.............................................................................................................................. A‐1
 

APPENDIX B: REFERENCES ............................................................................................................................... B‐1
 

LIST OF FIGURES 

FIGURE 1: NIST BIG DATA REFERENCE ARCHITECTURE TAXONOMY............................................................................................10
 

FIGURE 2: NBDRA CONCEPTUAL MODEL .............................................................................................................................12
 

LIST OF TABLES 

TABLE 1: MAPPING OF USE CASE CATEGORIES TO THE NBDRA COMPONENTS.............................................................................11
 

TABLE 2: EXISTING BIG DATA STANDARDS .............................................................................................................................17
 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Executive Summary 

To provide a common Big Data framework, the NIST Big Data Public Working Group (NBD-PWG) is 

creating vendor-neutral, technology- and infrastructure-agnostic deliverables, which include the 

development of consensus-based definitions, taxonomies, reference architecture, and roadmap. This 

document, NIST Interoperability Framework: Volume 7, Standards Roadmap, summarizes the 

deliverables of the other NBD-PWG subgroups (presented in detail in the other volumes of this series) 

and presents the work of the NBD-PWG Technology Roadmap Subgroup. In the first phase of 

development, the NBD-PWG Technology Roadmap Subgroup investigated existing standards that relate 

to Big Data and recognized general categories of gaps in those standards. 

The NIST Big Data Interoperability Framework consists of seven volumes, each of which addresses a 

specific key topic, resulting from the work of the NBD-PWG. The seven volumes are: 

 Volume 1, Definitions 

 Volume 2, Taxonomies 

 Volume 3, Use Cases and General Requirements 

 Volume 4, Security and Privacy 

 Volume 5, Architectures White Paper Survey 

 Volume 6, Reference Architecture 

 Volume 7, Standards Roadmap 

The NIST Big Data Interoperability Framework will be released in three versions, which correspond to 

the three development stages of the NBD-PWG work. The three stages aim to achieve the following with 

respect to the NIST Big Data Reference Architecture (NBDRA). 

Stage 1: Identify the high-level Big Data reference architecture key components, which are 

technology-, infrastructure-, and vendor-agnostic. 

Stage 2: Define general interfaces between the NBDRA components. 

Stage 3: Validate the NBDRA by building Big Data general applications through the general 

interfaces. 

Potential areas of future work for the Subgroup during stage 2 are highlighted in Section 1.5 of this 

volume. The current effort documented in this volume reflects concepts developed within the rapidly 

evolving field of Big Data. 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

1 INTRODUCTION
 

1.1 BACKGROUND 

There is broad agreement among commercial, academic, and government leaders about the remarkable 

potential of Big Data to spark innovation, fuel commerce, and drive progress. Big Data is the common 

term used to describe the deluge of data in today’s networked, digitized, sensor-laden, and informationdriven world. The availability of vast data resources carries the potential to answer questions previously 

out of reach, including the following: 

 How can a potential pandemic reliably be detected early enough to intervene? 

 Can new materials with advanced properties be predicted before these materials have ever been 

synthesized? 

 How can the current advantage of the attacker over the defender in guarding against cybersecurity threats be reversed? 

There is also broad agreement on the ability of Big Data to overwhelm traditional approaches. The growth 

rates for data volumes, speeds, and complexity are outpacing scientific and technological advances in data 

analytics, management, transport, and data user spheres. 

Despite widespread agreement on the inherent opportunities and current limitations of Big Data, a lack of 

consensus on some important fundamental questions continues to confuse potential users and stymie 

progress. These questions include the following: 

 What attributes define Big Data solutions? 

 How is Big Data different from traditional data environments and related applications? 

 What are the essential characteristics of Big Data environments? 

 How do these environments integrate with currently deployed architectures? 

 What are the central scientific, technological, and standardization challenges that need to be 

addressed to accelerate the deployment of robust Big Data solutions? 

Within this context, on March 29, 2012, the White House announced the Big Data Research and 

Development Initiative.1

 The initiative’s goals include helping to accelerate the pace of discovery in 

science and engineering, strengthening national security, and transforming teaching and learning by 

improving the ability to extract knowledge and insights from large and complex collections of digital 

data. 

Six federal departments and their agencies announced more than $200 million in commitments spread 

across more than 80 projects, which aim to significantly improve the tools and techniques needed to 

access, organize, and draw conclusions from huge volumes of digital data. The initiative also challenged 

industry, research universities, and nonprofits to join with the federal government to make the most of the 

opportunities created by Big Data. 

Motivated by the White House initiative and public suggestions, the National Institute of Standards and 

Technology (NIST) has accepted the challenge to stimulate collaboration among industry professionals to 

further the secure and effective adoption of Big Data. As one result of NIST’s Cloud and Big Data Forum 

held on January 15–17, 2013, there was strong encouragement for NIST to create a public working group 

for the development of a Big Data Interoperability Framework. Forum participants noted that this 

roadmap should define and prioritize Big Data requirements, including interoperability, portability, 

reusability, extensibility, data usage, analytics, and technology infrastructure. In doing so, the roadmap 

would accelerate the adoption of the most secure and effective Big Data techniques and technology. 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

On June 19, 2013, the NIST Big Data Public Working Group (NBD-PWG) was launched with extensive 

participation by industry, academia, and government from across the nation. The scope of the NBD-PWG 

involves forming a community of interests from all sectors—including industry, academia, and 

government—with the goal of developing consensus on definitions, taxonomies, secure reference 

architectures, security and privacy requirements, andfrom thesea standards roadmap. Such a 

consensus would create a vendor-neutral, technology- and infrastructure-independent framework that 

would enable Big Data stakeholders to identify and use the best analytics tools for their processing and 

visualization requirements on the most suitable computing platform and cluster, while also allowing 

value-added from Big Data service providers. 

The NIST Big Data Interoperability Framework consists of seven volumes, each of which addresses a 

specific key topic, resulting from the work of the NBD-PWG. The seven volumes are: 

 Volume 1, Definitions 

 Volume 2, Taxonomies 

 Volume 3, Use Cases and General Requirements 

 Volume 4, Security and Privacy 

 Volume 5, Architectures White Paper Survey 

 Volume 6, Reference Architecture 

 Volume 7, Standards Roadmap 

The NIST Big Data Interoperability Framework will be released in three versions, which correspond to 

the three stages of the NBD-PWG work. The three stages aim to achieve the following with respect to the 

NIST Big Data Reference Architecture (NBDRA.) 

Stage 1: Identify the high-level Big Data reference architecture key components, which are 

technology, infrastructure, and vendor agnostic. 

Stage 2: Define general interfaces between the NBDRA components. 

Stage 3: Validate the NBDRA by building Big Data general applications through the general 

interfaces. 

The NBDRA, created in Stage 1 and further developed in Stages 2 and 3, is a high-level conceptual model 

designed to serve as a tool to facilitate open discussion of the requirements, structures, and operations 

inherent in Big Data. It is discussed in detail in NIST Big Data Interoperability Framework: Volume 6, 

Reference Architecture. Potential areas of future work for the Subgroup during stage 2 are highlighted in 

Section 1.5 of this volume. The current effort documented in this volume reflects concepts developed 

within the rapidly evolving field of Big Data. 

1.2 NIST BIG DATA PUBLIC WORKING GROUP 

The focus of the NBD-PWG is to form a community of interest from industry, academia, and 

government, with the goal of developing consensus-based Big Data definitions, taxonomies, reference 

architectures, and standards roadmap. The aim is to create vendor-neutral, technology- and infrastructureagnostic deliverables to enable Big Data stakeholders to select the best analytics tools for their processing 

and visualization requirements on the most suitable computing platforms and clusters while allowing 

value-added from Big Data service providers and flow of data between the stakeholders in a cohesive and 

secure manner. 

To achieve this goal, five subgroups were formed to address specific issues and develop the deliverables. 

These subgroups are as follows: 

 NIST Big Data Definitions and Taxonomies Subgroup 

 NIST Big Data Use Case and Requirements Subgroup 

 NIST Big Data Security and Privacy Subgroup 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

 NIST Big Data Reference Architecture Subgroup
 

 NIST Big Data Technology Roadmap Subgroup
 

This volume and its companions were developed based on the following guiding principles: 

 Deliverables are technologically agnostic; 

 The audience is multi-sector, comprised of industry, government, and academia; 

 Findings from all subgroups are aligned; and 

 Deliverables represent the culmination of concepts from all subgroups. 

1.3 SCOPE AND OBJECTIVES OF THE TECHNOLOGY ROADMAP SUBGROUP 

The NBD-PWG Technology Roadmap Subgroup focused on forming a community of interest from 

industry, academia, and government, with the goal of developing a consensus vision with 

recommendations on how Big Data should move forward. The Subgroup’s approach was to perform a gap 

analysis through the materials gathered from all other subgroups. This included setting standardization 

and adoption priorities through an understanding of what standards are available or under development as 

part of the recommendations. The goals of the Subgroup will be realized throughout the three planed 

phases of the NBD-PWG work, as outlined in Section 1.1. The primary tasks of the NBD-PWG 

Technology Roadmap Subgroup include the following: 

 Gather input from NBD-PWG subgroups and study the taxonomies for the actors’ roles and 

responsibility, use cases and general requirements, and secure reference architecture; 

 Gain understanding of what standards are available or under development for Big Data; 

 Perform a gap analysis and document the findings; 

 Identify what possible barriers may delay or prevent adoption of Big Data; and 

 Document vision and recommendations. 

1.4 REPORT PRODUCTION 

The NIST Big Data Interoperability Framework: Volume 7, Standards Roadmap is one of seven volumes 

in the document, whose overall aims are to define and prioritize Big Data requirements, including 

interoperability, portability, reusability, extensibility, data usage, analytic techniques, and technology 

infrastructure in order to support secure and effective adoption of Big Data. The NIST Big Data 

Interoperability Framework: Volume 7, Standards Roadmap is dedicated to developing a consensus 

vision with recommendations on how Big Data should move forward specifically in the area of 

standardization. In the first phase, the Subgroup focused on the identification of existing standards 

relating to Big Data and inspection of gaps in those standards. 

Following the introductory material presented in Section 1, the remainder of this document is organized 

as follows: 

 Section 2 summarizes the Big Data definitions presented in the NIST Interoperability 

Framework: Volume 1, Definitions document; 

 Section 3 summarizes the assessment of the Big Data ecosystem, which was used to develop the 

NBDRA and this roadmap;
 

 Section 4 presents an overview of the NBDRA; 

 Section 5 presents an overview of the security and privacy fabric of the NBDRA; and 

 Section 6 investigates the standards related to Big Data and the gaps in those standards. 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

1.5 FUTURE WORK ON THIS VOLUME 

The NIST Big Data Interoperability Framework will be released in three versions, which correspond to 

the three stages of the NBD-PWG work, as outlined in Section 1.1. 

Version 2 activities will focus on the following: 

 Continue to build and refine the gap analysis and document the findings; 

 Identify where standards may accelerate the adoption and interoperability of Big Data 

technologies; 

 Document recommendations for future standards activities; and 

 Further map standards to NBDRA components and the interfaces between them. 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

2 BIG DATA DEFINITION
 

There are two fundamental concepts in the emerging discipline of Big Data that have been used to 

represent multiple concepts. These two concepts, Big Data and data science, are broken down into 

individual terms and concepts in the following subsections. As a basis for discussions of the NBDRA and 

related standards and measurement technology, associated terminology is defined in subsequent 

subsections. NIST Big Data Infrastructure Framework: Volume 1, Definitions contains additional details 

and terminology. 

2.1 BIG DATA DEFINITIONS 

Big Data refers to the inability of traditional data architectures to efficiently handle the new datasets. 

Characteristics of Big Data that force new architectures are volume (i.e., the size of the dataset) and 

variety (i.e., data from multiple repositories, domains, or types), and the data in motion characteristics of 

velocity (i.e., rate of flow) and variability (i.e., the change in other characteristics). These 

characteristics—volume, variety, velocity, and variability—are known colloquially as the ‘Vs’ of Big 

Data and are further discussed in Section 3. Each of these characteristics influences the overall design of a 

Big Data system, resulting in different data system architectures or different data life cycle process 

orderings to achieve needed efficiencies. A number of other terms are also used, several of which refer to 

the analytics process instead of new Big Data characteristics. The following Big Data definitions have 

been used throughout the seven volumes of the NIST Big Data Interoperability Framework and are fully 

described in Volume 1. 

Big Data consists of extensive datasets—primarily in the characteristics of volume, 

variety, velocity, and/or variability—that require a scalable architecture for efficient 

storage, manipulation, and analysis. 

The Big Data paradigm consists of the distribution of data systems across horizontally 

coupled, independent resources to achieve the scalability needed for the efficient 

processing of extensive datasets. 

Veracity refers to accuracy of the data. 

Value refers to the inherent wealth, economic and social, embedded in any dataset. 

Volatility refers to the tendency for data structures to change over time. 

Validity refers to appropriateness of the data for its intended use. 

2.2 DATA SCIENCE DEFINITIONS 

In its purest form, data science is the fourth paradigm of science, following theory, experiment, and 

computational science. The fourth paradigm is a term coined by Dr. Jim Gray in 2007 to refer to the 

conduct of data analysis as an empirical science, learning directly from data itself. Data science as a 

paradigm would refer to the formulation of a hypothesis, the collection of the data—new or preexisting— 

to address the hypothesis, and the analytical confirmation or denial of the hypothesis (or the determination 

that additional information or study is needed.) As in any experimental science, the end result could in 

fact be that the original hypothesis itself needs to be reformulated. The key concept is that data science is 

an empirical science, performing the scientific process directly on the data. Note that the hypothesis may 

be driven by a business need, or can be the restatement of a business need in terms of a technical 

hypothesis. 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Data science is the empirical synthesis of actionable knowledge from raw data through 

the complete data life cycle process. 

The data science paradigm is extraction of actionable knowledge directly from data 

through a process of discovery, hypothesis, and hypothesis testing. 

While the above definition of the data science paradigm refers to learning directly from data, in the Big 

Data paradigm this learning must now implicitly involve all steps in the data life cycle, with analytics 

being only a subset. Data science can be understood as the activities happening in the data layer of the 

system architecture to extract knowledge from the raw data. 

The data life cycle is the set of processes that transform raw data into actionable 

knowledge. 

Traditionally, the term analytics has been used as one of the steps in the data life cycle of collection, 

preparation, analysis, and action. 

Analytics is the synthesis of knowledge from information. 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

3 INVESTIGATING THE BIG DATA ECOSYSTEM
 

The development of a Big Data reference architecture involves a thorough understanding of current 

techniques, issues, concerns, and other topics. To this end, the NBD-PWG collected use cases to gain an 

understanding of current applications of Big Data, conducted a survey of reference architectures to 

understand commonalities within Big Data architectures in use, developed a taxonomy to understand and 

organize the information collected, and reviewed existing Big Data relevant technologies and trends. 

From the collected information, the NBD-PWG created the NBDRA, which is a high-level conceptual 

model designed to serve as a tool to facilitate open discussion of the requirements, structures, and 

operations inherent in Big Data. These NBD-PWG activities were used as input during the development 

of the entire NIST Big Data Interoperability Framework. 

3.1 USE CASES 

A consensus list of Big Data requirements across stakeholders was developed by the NBD-PWG Use 

Cases and Requirements Subgroup. The development of requirements included gathering and 

understanding various use cases from the nine diversified areas, or application domains, listed below. 

 Government Operation 

 Commercial 

 Defense 

 Healthcare and Life Sciences 

 Deep Learning and Social Media 

 The Ecosystem for Research 

 Astronomy and Physics 

 Earth, Environmental, and Polar Science 

 Energy 

Participants in the NBD-PWG Use Cases and Requirements Subgroup and other interested parties 

supplied publically available information for various Big Data architecture examples from the nine 

application domains, which developed organically from the 51 use cases collected by the Subgroup. 

After collection, processing, and review of the use cases, requirements within seven Big Data 

characteristic categories were extracted from the individual use cases. Requirements are the challenges 

limiting further use of Big Data. The complete list of requirements extracted from the use cases is 

presented in the document NIST Big Data Interoperability Framework: Volume 3, Use Cases and 

General Requirements. 

The use case specific requirements were then aggregated to produce high-level general requirements, 

within seven characteristic categories. The seven categories were as follows: 

 Data sources (e.g., data size, file formats, rate of growth, at rest or in motion) 

 Data transformation (e.g., data fusion, analytics) 

 Capabilities (e.g., software tools, platform tools, hardware resources such as storage and 

networking) 

 Data consumer (e.g., processed results in text, table, visual, and other formats) 

 Security and privacy 

 Life cycle management (e.g., curation, conversion, quality check, pre-analytic processing) 

 Other requirements 

The general requirements, created to be vendor-neutral and technology-agnostic, are listed below. 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

DATA SOURCE REQUIREMENTS (DSR) 

 DSR-1: Needs to support reliable real-time, asynchronous, streaming, and batch processing to 

collect data from centralized, distributed, and cloud data sources, sensors, or instruments. 

 DSR-2: Needs to support slow, bursty, and high-throughput data transmission between data 

sources and computing clusters. 

	 DSR-3: Needs to support diversified data content ranging from structured and unstructured text, 

document, graph, web, geospatial, compressed, timed, spatial, multimedia, simulation, and 

instrumental data. 

TRANSFORMATION PROVIDER REQUIREMENTS (TPR) 

	 TPR-1: Needs to support diversified compute-intensive, statistical and graph analytic processing, 

and machine-learning techniques. 

 TPR-2: Needs to support batch and real-time analytic processing. 

 TPR-3: Needs to support processing large diversified data content and modeling. 

 TPR-4: Needs to support processing data in motion (e.g., streaming, fetching new content, 

tracking). 

CAPABILITY PROVIDER REQUIREMENTS (CPR) 

 CPR-1: Needs to support legacy and advanced software packages (software). 

 CPR-2: Needs to support legacy and advanced computing platforms (platform). 

 CPR-3: Needs to support legacy and advanced distributed computing clusters, co-processors, and 

input output processing (infrastructure). 

 CPR-4: Needs to support elastic data transmission (networking). 

 CPR-5: Needs to support legacy, large, and advanced distributed data storage (storage). 

 CPR-6: Needs to support legacy and advanced executable programming: applications, tools, 

utilities, and libraries (software). 

DATA CONSUMER REQUIREMENTS (DCR) 

 DCR-1: Needs to support fast searches from processed data with high relevancy, accuracy, and 

recall. 

 DCR-2: Needs to support diversified output file formats for visualization, rendering, and 

reporting. 

 DCR-3: Needs to support visual layout for results presentation. 

 DCR-4: Needs to support rich user interface for access using browser, visualization tools. 

 DCR-5: Needs to support high-resolution, multi-dimension layer of data visualization. 

 DCR-6: Needs to support streaming results to clients. 

SECURITY AND PRIVACY REQUIREMENTS (SPR) 

 SPR-1: Needs to protect and preserve security and privacy of sensitive data. 

 SPR-2: Needs to support sandbox, access control, and multilevel, policy-driven authentication on 

protected data. 

LIFE CYCLE MANAGEMENT REQUIREMENTS (LMR) 

	 LMR-1: Needs to support data quality curation including pre-processing, data clustering, 

classification, reduction, and format transformation. 

 LMR-2: Needs to support dynamic updates on data, user profiles, and links. 

 LMR-3: Needs to support data life cycle and long-term preservation policy, including data 

provenance. 

 LMR-4: Needs to support data validation.
 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

 LMR-5: Needs to support human annotation for data validation. 

 LMR-6: Needs to support prevention of data loss or corruption. 

 LMR-7: Needs to support multisite archives. 

 LMR-8: Needs to support persistent identifier and data traceability. 

 LMR-9: Needs to support standardizing, aggregating, and normalizing data from disparate 

sources. 

OTHER REQUIREMENTS (OR) 

 OR-1: Needs to support rich user interface from mobile platforms to access processed results. 

 OR-2: Needs to support performance monitoring on analytic processing from mobile platforms. 

 OR-3: Needs to support rich visual content search and rendering from mobile platforms. 

 OR-4: Needs to support mobile device data acquisition. 

 OR-5: Needs to support security across mobile devices. 

Additional information about the Subgroup, use case collection, analysis of the use cases, and generation 

of the use case requirements are presented in the NIST Big Data Interoperability Framework: Volume 3, 

Use Cases and General Requirements document. 

3.2 REFERENCE ARCHITECTURE SURVEY 

The NBD-PWG Reference Architecture Subgroup conducted the reference architecture survey to advance 

understanding of the operational intricacies in Big Data and to serve as a tool for developing systemspecific architectures using a common reference framework. The Subgroup surveyed currently published 

Big Data platforms by leading companies or individuals supporting the Big Data framework and analyzed 

the collected material. This effort revealed a remarkable consistency between Big Data architectures. 

Survey details, methodology, and conclusions are reported in NIST Big Data Interoperability Framework: 

Volume 5, Architectures White Paper Survey. 

3.3 TAXONOMY 

The NBD-PWG Definitions and Taxonomy Subgroup developed a hierarchy of reference architecture 

components. Additional taxonomy details are presented in the NIST Big Data Interoperability 

Framework: Volume 2, Taxonomy document. 

Figure 1 outlines potential actors for the seven roles developed by the NBD-PWG Definition and 

Taxonomy Subgroup. The dark blue boxes contain the name of the role at the top with potential actors 

listed directly below. 

9 
NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Figure 1: NIST Big Data Reference Architecture Taxonomy 

10
 
NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

4 BIG DATA REFERENCE ARCHITECTURE
 

4.1 OVERVIEW 

The goal of the NBD-PWG Reference Architecture Subgroup is to develop a Big Data open reference 

architecture that facilitates the understanding of the operational intricacies in Big Data. It does not 

represent the system architecture of a specific Big Data system, but rather is a tool for describing, 

discussing, and developing system-specific architectures using a common framework of reference. The 

reference architecture achieves this by providing a generic high-level conceptual model that is an 

effective tool for discussing the requirements, structures, and operations inherent to Big Data. The model 

is not tied to any specific vendor products, services, or reference implementation, nor does it define 

prescriptive solutions that inhibit innovation. 

The design of the NBDRA does not address the following: 

 Detailed specifications for any organization’s operational systems; 

 Detailed specifications of information exchanges or services; and 

 Recommendations or standards for integration of infrastructure products 

Building on the work from other subgroups, the NBD PWG Reference Architecture Subgroup evaluated 

the general requirements formed from the use cases, evaluated the Big Data Taxonomy, performed a 

reference architecture survey, and developed the NBDRA conceptual model. The NIST Big Data 

Interoperability Framework: Volume 3, Use Cases and General Requirements document contains details 

of the Subgroup’s work. 

The NBD-PWG Use Case Subgroup developed requirements in seven categories, which correspond to the 

reference architecture components as shown in Table 1. The requirements from each category were used 

as input for the development of the corresponding NBDRA component. 

Table 1: Mapping of Use Case Categories to the NBDRA Components 

Use Case Characterization Reference Architecture Components And 

Categories Fabrics 

Data sources → Data Provider 

Data transformation → Big Data Application Provider 

Capabilities → Big Data Framework Provider 

Data consumer → Data Consumer 

Security and privacy → Security and Privacy Fabric 

Life cycle management → System Orchestrator; Management Fabric 

Other requirements → To all components and fabric 

4.2 NBDRA CONCEPTUAL MODEL 

The NBD-PWG Reference Architecture Subgroup used a variety of inputs from other NBD-PWG 

subgroups in developing a vendor-neutral, technology- and infrastructure-agnostic conceptual model of 

Big Data architecture. This conceptual model, the NBDRA, is shown in Figure 2 and represents a Big 

Data system comprised of five logical functional components connected by interoperability interfaces 

11
 
                 

     

   

     

     

         

   

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

(i.e., services). Two fabrics envelop the components, representing the interwoven nature of management 

and security and privacy with all five of the components. 

The NBDRA is intended to enable system engineers, data scientists, software developers, data architects, 

and senior decision makers to develop solutions to issues that require diverse approaches due to 

convergence of Big Data characteristics within an interoperable Big Data ecosystem. It provides a 

framework to support a variety of business environments, including tightly integrated enterprise systems 

and loosely coupled vertical industries, by enhancing understanding of how Big Data complements and 

differs from existing analytics, business intelligence, databases, and systems. 

K E Y : Big Data Information Service Use SW

Flow

Software Tools and

Algorithms Transfer

Big Data Application Provider

Collection Analytics Visualization Access

System Orchestrator

Secur i t y

& P r i v a c

y

M a n a g e m e n t

DATA

SW

DATA

SW

I NFO R M AT I O N VA LU E C H A I N

I T VA LU E C H AIN

Data Consumer

Data Provider

DATA

Virtual Resources

Physical Resources

Indexed Storage

File Systems

Big Data Framework Provider

Processing: Computing and Analytic

Platforms: Data Organization and Distribution

Infrastructures: Networking, Computing, Storage DATA SW

Preparation

/ Curation

Messaging/ Communications

Streaming

Resource Management

Batch Interactive

Figure 2: NBDRA Conceptual Model 

Note: None of the terminology or diagrams in these documents is intended to be normative or to imply 

any business or deployment model. The terms “provider” and “consumer” as used are descriptive of 

general roles and are meant to be informative in nature. 

The NBDRA is organized around two axes representing the two Big Data value chains: the information 

(horizontal axis) and the Information Technology (IT) (vertical axis). Along the information axis, the 

value is created by data collection, integration, analysis, and applying the results following the value 

chain. Along the IT axis, the value is created by providing networking, infrastructure, platforms, 

application tools, and other IT services for hosting of and operating the Big Data in support of required 

12
 
NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

data applications. At the intersection of both axes is the Big Data Application Provider component, 

indicating that data analytics and its implementation provide the value to Big Data stakeholders in both 

value chains. The names of the Big Data Application Provider and Big Data Framework Provider 

components contain “providers” to indicate that these components provide or implement a specific 

technical function within the system. 

The five main NBDRA components, shown in Figure 2 and discussed in detail in Section 4, represent 

different technical roles that exist in every Big Data system. These functional components are: 

 System Orchestrator 

 Data Provider 

 Big Data Application Provider 

 Big Data Framework Provider 

 Data Consumer 

The two fabrics shown in Figure 2 encompassing the five functional components are: 

 Management 

 Security and Privacy 

These two fabrics provide services and functionality to the five functional components in the areas 

specific to Big Data and are crucial to any Big Data solution. 

The “DATA” arrows in Figure 2 show the flow of data between the system’s main components. Data 

flows between the components either physically (i.e., by value) or by providing its location and the means 

to access it (i.e., by reference). The “SW” arrows show transfer of software tools for processing of Big 

Data in situ. The “Service Use” arrows represent software programmable interfaces. While the main focus 

of the NBDRA is to represent the run-time environment, all three types of communications or transactions 

can happen in the configuration phase as well. Manual agreements (e.g., service-level agreements 

[SLAs]) and human interactions that may exist throughout the system are not shown in the NBDRA. 

The components represent functional roles in the Big Data ecosystem. In system development, actors and 

roles have the same relationship as in the movies, but system development actors can represent 

individuals, organizations, software, or hardware. According to the Big Data taxonomy, a single actor can 

play multiple roles, and multiple actors can play the same role. The NBDRA does not specify the business 

boundaries between the participating actors or stakeholders, so the roles can either reside within the same 

business entity or can be implemented by different business entities. Therefore, the NBDRA is applicable 

to a variety of business environments, from tightly integrated enterprise systems to loosely coupled 

vertical industries that rely on the cooperation of independent stakeholders. As a result, the notion of 

internal versus external functional components or roles does not apply to the NBDRA. However, for a 

specific use case, once the roles are associated with specific business stakeholders, the functional 

components would be considered as internal or external—subject to the use case’s point of view. 

The NBDRA does support the representation of stacking or chaining of Big Data systems. For example, a 

Data Consumer of one system could serve as a Data Provider to the next system down the stack or chain. 

The five main components and the two fabrics of the NBDRA are discussed in the NIST Big Data 

Interoperability Framework: Volume 6, Reference Architecture and Volume 4, Security and Privacy. 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

5 BIG DATA SECURITY AND PRIVACY 

Security and privacy measures for Big Data involve a different approach than traditional systems. Big 

Data is increasingly stored on public cloud infrastructure built by various hardware, operating systems, 

and analytical software. Traditional security approaches usually addressed small-scale systems holding 

static data on firewalled and semi-isolated networks. The surge in streaming cloud technology 

necessitates extremely rapid responses to security issues and threats.2 

Security and privacy considerations are a fundamental aspect of Big Data and affect all components of the 

NBDRA. This comprehensive influence is depicted in Figure 2 by the grey rectangle marked “Security 

and Privacy” surrounding all of the reference architecture components. At a minimum, a Big Data 

reference architecture will provide verifiable compliance with both governance, risk management, and 

compliance (GRC) and confidentiality, integrity, and availability (CIA) policies, standards, and best 

practices. Additional information on the processes and outcomes of the NBD PWG Security and Privacy 

Subgroup are presented in NIST Big Data Interoperability Framework: Volume 4, Security and Privacy. 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

6 BIG DATA STANDARDS 

Big Data has generated interest in a wide variety of multi-stakeholder collaborative organizations, 

including those involved in the de jure standards process, industry consortia, and open source 

organizations. These organizations may operate differently and focus on different aspects, but they all 

have a stake in Big Data. Integrating additional Big Data initiatives with ongoing collaborative efforts is a 

key to success. Identifying which collaborative initiative efforts address architectural requirements and 

which requirements are not currently being addressed is a starting point for building future multistakeholder collaborative efforts. Collaborative initiatives include, but are not limited to the following: 

 Subcommittees and working groups of American National Standards Institute (ANSI) 

 Accredited standards development organizations (SDOs; the de jure standards process) 

 Industry consortia 

 Reference implementations 

 Open source implementations 

Some of the leading SDOs and industry consortia working on Big Data-related standards include: 

 International Committee for Information Technology Standards (INCITS) and International 

Organization for Standardization (ISO)—de jure standards process 

 Institute of Electrical and Electronics Engineers (IEEE)—de jure standards process 

 International Electrotechnical Commission (IEC) 

 Internet Engineering Task Force (IETF) 

 World Wide Web Consortium (W3C)—Industry consortium 

 Open Geospatial Consortium (OGC®)—Industry consortium 

 Organization for the Advancement of Structured Information Standards (OASIS)—Industry 

consortium 

 Open Grid Forum (OGF)—Industry consortium 

The organizations and initiatives referenced in this document do not form an exhaustive list. It is 

anticipated that as this document is more widely distributed, more standards efforts addressing additional 

segments of the Big Data mosaic will be identified. 

There are a number of government organizations that publish standards relative to their specific problem 

areas. The U.S. Department of Defense alone maintains hundreds of standards. Many of these are based 

on other standards (e.g., ISO, IEEE, ANSI) and could be applicable to the Big Data problem space. 

However, a fair, comprehensive review of these standards would exceed the available document 

preparation time and may not be of interest to the majority of the audience for this report. Readers 

interested in domains covered by the government organizations and standards are encouraged to review 

the standards for applicability to their specific needs. 

Open source implementations are providing useful new technology that is being used either directly or as 

the basis for commercially supported products. These open source implementations are not just individual 

products. One needs to integrate an ecosystem of products to accomplish one’s goals. Because of the 

ecosystem complexity, and because of the difficulty of fairly and exhaustively reviewing open source 

implementations, such implementations are not included in this section. However, it should be noted that 

those implementations often evolve to become the de facto reference implementations for many 

technologies. 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

6.1 EXISTING STANDARDS 

This section presents a list of existing standards from the above listed organizations that are relevant to 

Big Data and the NBDRA. Determining the relevance of standards to the Big Data domain is challenging 

since almost all standards in some way deal with data. Whether a standard is relevant to Big Data is 

generally determined by the impact of Big Data characteristics (i.e., volume, velocity, variety, and 

veracity) on the standard or, more generally, by the scalability of the standard to accommodate those 

characteristics. A standard may also be applicable to Big Data depending on the extent to which that 

standard helps to address one or more of the Big Data characteristics. Finally, a number of standards are 

also very domain- or problem-specific and, while they deal with or address Big Data, they support a very 

specific functional domain. Developing even a marginally comprehensive list of such standards would 

require a massive undertaking involving subject matter experts in each potential problem domain, which 

is beyond the scope of the NBD-PWG. 

In selecting standards to include in Table 2, the working group focused on standards that would do the 

following: 

 Facilitate interfaces between NBDRA components; 

 Facilitate the handling of data with one or more Big Data characteristics; and 

 Represent a fundamental function needing to be implemented by one or more NBDRA 

components. 

Table 2 represents a portion of potentially applicable standards from a portion of contributing 

organizations working in Big Data domain. 

As most standards represent some form of interface between components, Table 2 is annotated with 

whether the NBDRA component would be an Implementer or User of the standard. For the purposes of 

this table, the following definitions were used for Implementer and User. 

Implementer: A component is an implementer of a standard if it provides services based 

on the standard (e.g., a service that accepts Structured Query Language [SQL] 

commands would be an implementer of that standard) or encodes or presents data based 

on that standard. 

User: A component is a user of a standard if it interfaces to a service via the standard or 

if it accepts/consumes/decodes data represented by the standard. 

While the above definitions provide a reasonable basis for some standards, the difference between 

implementation and use may be negligible or nonexistent. 

The NBDRA components are abbreviated in the table header as follows: 

 SO = System Orchestrator component 

 DP = Data Provider component 

 DC = Data Consumer component 

 BDAP = Big Data Application Provider component 

 BDFP = Big Data Framework Provider component 

 S&P = Security and Privacy Fabric 

 M = Management Fabric 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Table 2: Existing Big Data Standards 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

ISO/IEC 9075-* ISO/IEC 9075 defines SQL. 

The scope of SQL is the 

definition of data structure and 

the operations on data stored in 

that structure. ISO/IEC 9075-1, 

ISO/IEC 9075-2 and ISO/IEC 

9075-11 encompass the 

minimum requirements of the 

language. Other parts define 

extensions. 

I I/U U I/U U U 

ISO/IEC Technical 

Report (TR) 9789 

Guidelines for the Organization 

and Representation of Data 

Elements for Data Interchange

 I/U I/U I/U I/U 

ISO/IEC 11179-* The 11179 standard is a 

multipart standard for the 

definition and implementation 

of Metadata Registries. The 

series includes the following 

parts: 

 Part 1: Framework 

 Part 2: Classification 

 Part 3: Registry metamodel 

and basic attributes 

 Part 4: Formulation of data 

definitions 

 Part 5: Naming and 

identification principles 

 Part 6: Registration 

I I/U I/U U 

ISO/IEC 10728-* Information Resource 

Dictionary System Services 

Interface 

ISO/IEC 13249-* Database Languages – SQL 

Multimedia and Application 

Packages 

I I/U U I/U 

ISO/IE TR 19075-* This is a series of TRs on SQL 

related technologies. 

 Part 1: Xquery 

 Part 2: SQL Support for 

Time-Related Information 

 Part 3: Programs Using the 

Java Programming 

Language 

 Part 4: Routines and Types 

Using the Java 

Programming Language 

I I/U U I/U 

ISO/IEC 19503 Extensible Markup Language 

(XML) Metadata Interchange 

(XMI) 

I I/U U I/U U 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

ISO/IEC 19773 Metadata Registries Modules I I/U U I/U I/U 

ISO/IEC TR 20943 Metadata Registry Content 

Consistency 

I I/U U I/U U U 

ISO/IEC 19763-* Information Technology— 

Metamodel Framework for 

Interoperability (MFI) ISO/IEC 

19763, Information Technology 

–MFI. The 19763 standard is a 

multipart standard that includes 

the following parts: 

 Part 1: Reference model 

 Part 3: Metamodel for 

ontology registration 

 Part 5: Metamodel for 

process model registration 

 Part 6: Registry Summary 

 Part 7: Metamodel for 

service registration 

 Part 8: Metamodel for role 

and goal registration 

 Part 9: On Demand Model 

Selection (ODMS) TR 

 Part 10: Core model and 

basic mapping 

 Part 12: Metamodel for 

information model 

registration 

 Part 13: Metamodel for 

forms registration 

 Part 14: Metamodel for 

dataset registration 

 Part 15: Metamodel for data 

provenance registration 

I I/U U U 

ISO/IEC 9281:1990 Information Technology— 

Picture Coding Methods 

I U I/U I/U 

ISO/IEC 10918:1994 Information Technology— 

Digital Compression and 

Coding of Continuous-Tone 

Still Images 

I U I/U I/U 

ISO/IEC 11172:1993 Information Technology— 

Coding of Moving Pictures and 

Associated Audio for Digital 

Storage Media at up to About 

1,5 Mbit/s 

I U I/U I/U 

ISO/IEC 13818:2013 Information Technology— 

Generic Coding of Moving 

Pictures and Associated Audio 

Information 

I U I/U I/U 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

ISO/IEC 14496:2010 Information Technology— 

Coding of Audio-Visual 

Objects 

I U I/U I/U 

ISO/IEC 15444:2011 Information Technology— 

JPEG (Joint Photographic 

Experts Group) 2000 Image 

Coding System

 I U I/U I/U 

ISO/IEC 21000:2003 Information Technology— 

Multimedia Framework (MPEG 

[Moving Picture Experts 

Group]-21) 

I U I/U I/U 

ISO 6709:2008 Standard Representation of 

Geographic Point Location by 

Coordinates

 I U I/U I/U 

ISO 19115-* Geographic Metadata I U I/U U 

ISO 19110 Geographic Information Feature 

Cataloging 

I U I/U 

ISO 19139 Geographic Metadata XML 

Schema Implementation 

I U I/U 

ISO 19119 Geographic Information 

Services 

I U I/U 

ISO 19157 Geographic Information Data 

Quality 

I U I/U U 

ISO 19114 Geographic Information— 

Quality Evaluation Procedures 

I 

IEEE 21451 -* Information Technology— 

Smart transducer interface for 

sensors and actuators 

 Part 1: Network Capable 

Application Processor 

(NCAP) information model 

 Part 2: Transducer to 

microprocessor 

communication protocols 

and Transducer Electronic 

Data Sheet (TEDS) formats 

 Part 4: Mixed-mode 

communication protocols 

and TEDS formats 

 Part 7: Transducer to radio 

frequency identification 

(RFID) systems 

communication protocols 

and TEDS formats 

I U 

IEEE 2200-2012 Standard Protocol for Stream 

Management in Media Client 

Devices

 I U I/U 

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Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

ISO/IEC 15408-2009 Information Technology— 

Security Techniques— 

Evaluation Criteria for IT 

Security 

U I 

ISO/IEC 27033-1:2009 Information Technology— 

Security Techniques—Network 

Security 

I/U I/U I/U I 

ISO/IEC TR 

14516:2002 

Information Technology— 

Security Techniques— 

Guidelines for the Use and 

Management of Trusted Third 

Party Services 

U U 

ISO/IEC 29100:2011 Information Technology— 

Security Techniques—Privacy 

Framework 

I 

ISO/IEC 9798:2010 Information Technology— 

Security Techniques—Entity 

Authentication 

I/U U U U I/U 

ISO/IEC 11770:2010 Information Technology— 

Security Techniques—Key 

Management

 I/U U U U I/U 

ISO/IEC 80000-13:2008 ISO/IEC 80000-13:2008 ­

Quantities and units -- Part 13: 

Information science and 

technology standard cancels 

and replaces subclauses 3.8 and 

3.9 of IEC 60027-2:2005 (those 

related to Information theory 

and Prefixes for binary 

multiples). 

U U U U I/U U U 

ISO/IEC 27000 ISO/IEC 27000, Information 

security management systems— 

Overview and vocabulary 

I U U U U I/U U 

ISO/IEC 27001 ISO/IEC 27001, Information 

security management systems— 

Requirements 

I U U U U I/U U 

ISO/IEC 27002 ISO/IEC 27002, Code of 

practice for information 

security controls 

 I/U I/U U U I/U U 

ISO/IEC 27003 ISO/IEC 27003, Information 

security management system 

implementation guidance 

I U U U U U U 

ISO/IEC 27004 ISO/IEC 27004, Information 

security management— 

Measurement 

 I/U I/U U U I/U U 

ISO/IEC 27005 ISO/IEC 27005, Information 

security risk management 

I U U U U U U 

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Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

ISO/IEC 27006 ISO/IEC 27006, Requirements 

for bodies providing audit and 

certification of information 

security management systems

 U U U U I/U U 

ISO/IEC 27007 ISO/IEC 27007, Guidelines for 

information security 

management systems auditing 

U U U U I/U U 

ISO/IEC/TR 27008 ISO/IEC/TR 27008, Guidelines 

for auditors on information 

security controls 

I 

ISO/IEC/DIS 27009 ISO/IEC/DIS 27009, Sectorspecific application of ISO/IEC 

27001–Requirements 

I 

ISO/IEC 27010 ISO/IEC 27010, Information 

security management for intersector and inter-organizational 

communications 

I/U I/U I/U I/U 

ISO/IEC 27011 ISO/IEC 27011, Information 

security management guidelines 

for telecommunications 

organizations based on ISO/IEC 

27002 

I U U U U U U 

ISO/IEC 27013 ISO/IEC 27013, Guidance on 

the integrated implementation 

of ISO/IEC 27001 and ISO/IEC 

20000-1 

I U U U U U U 

ISO/IEC 27014 ISO/IEC 27014, Governance of 

information security 

I U U U U U U 

ISO/IEC/TR 27015 ISO/IEC/TR 27015, 

Information security 

management guidelines for 

financial services 

I U U U U U U 

ISO/IEC/TR 27016 ISO/IEC/TR 27016, 

Information security 

management — Organizational 

economics 

I U U U U U U 

ISO/IEC 27017 ISO/IEC 27017, Code of 

practice for information 

security controls based on 

ISO/IEC 27002 for cloud 

services 

I U U U U I/U U 

ISO/IEC 27018 ISO/IEC 27018, Code of 

practice for PII protection in 

public clouds acting as PII 

processors 

I U U U U I/U U 

ISO/IEC 27019 ISO/IEC 27019, Information 

security management guidelines 

based on ISO/IEC 27002 for 

process control systems specific 

to the energy utility industry 

I U U U U U U 

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NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

ISO/IEC 27035:2011 Information Technology— 

Security Techniques— 

Information Security Incident 

Management 

U I 

ISO/IEC 27037:2012 Information Technology— 

Security Techniques— 

Guidelines for Identification, 

Collection, Acquisition and 

Preservation of Digital 

Evidence 

U I 

JSR (Java Specification 

Request) 221 (developed 

by the Java Community 

Process) 

JDBCTM 4.0 Application 

Programming Interface (API) 

Specification 

I/U I/U I/U I/U 

W3C XML XML 1.0 (Fifth Edition) W3C 

Recommendation 26 November 

2008 

I/U I/U I/U I/U I/U I/U I/U 

W3C Resource 

Description Framework 

(RDF) 

The RDF is a framework for 

representing information in the 

Web. RDF graphs are sets of 

subject-predicate-object triples, 

where the elements are used to 

express descriptions of 

resources. 

I U I/U I/U 

W3C JavaScript Object 

Notation (JSON)-LD 1.0 

JSON-LD 1.0 A JSON-based 

Serialization for Linked Data 

W3C Recommendation 16 

January 2014 

I U I/U I/U 

W3C Document Object 

Model (DOM) Level 1 

Specification 

This series of specifications 

define the DOM, a platformand language-neutral interface 

that allows programs and scripts 

to dynamically access and 

update the content, structure 

and style of HyperText Markup 

Language (HTML) and XML 

documents. 

I U I/U I/U 

W3C XQuery 3.0 The XQuery specifications 

describe a query language 

called XQuery, which is 

designed to be broadly 

applicable across many types of 

XML data sources. 

I U I/U I/U 

W3C XProc This specification describes the 

syntax and semantics of XProc: 

An XML Pipeline Language, a 

language for describing 

operations to be performed on 

XML documents. 

I I U I/U I/U 

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Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

W3C XML Encryption 

Syntax and Processing 

Version 1.1 

This specification covers a 

process for encrypting data and 

representing the result in XML. 

I U I/U 

W3C XML Signature 

Syntax and Processing 

Version 1.1 

This specification covers XML 

digital signature processing 

rules and syntax. XML 

Signatures provide integrity, 

message authentication, and/or 

signer authentication services 

for data of any type, whether 

located within the XML that 

includes the signature or 

elsewhere. 

I U I/U 

W3C XPath 3.0 XPath 3.0 is an expression 

language that allows the 

processing of values 

conforming to the data model 

defined in [XQuery and XPath 

Data Model (XDM) 3.0]. The 

data model provides a tree 

representation of XML 

documents as well as atomic 

values and sequences that may 

contain both references to nodes 

in an XML document and 

atomic values.

 I U I/U I/U 

W3C XSL 

Transformations 

(XSLT) Version 2.0 

This specification defines the 

syntax and semantics of XSLT 

2.0, a language for transforming 

XML documents into other 

XML documents. 

I U I/U I/U 

W3C Efficient XML 

Interchange (EXI) 

Format 1.0 (Second 

Edition) 

This specification covers the 

EXI format. EXI is a very 

compact representation for the 

XML Information Set that is 

intended to simultaneously 

optimize performance and the 

utilization of computational 

resources. 

I U I/U 

W3C RDF Data Cube 

Vocabulary 

The Data Cube vocabulary 

provides a means to publish 

multidimensional data, such as 

statistics on the Web using the 

W3C RDF standard. 

I U I/U I/U 

W3C Data Catalog 

Vocabulary (DCAT) 

DCAT is an RDF vocabulary 

designed to facilitate 

interoperability between data 

catalogs published on the Web. 

This document defines the 

schema and provides examples 

for its use.

 I U I/U 

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Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

W3C HTML5 A 

vocabulary and 

associated APIs for 

HTML and XHTML 

This specification defines the 

5th major revision of the core 

language of the World Wide 

Web—HTML. 

I U I/U 

W3C 

Internationalization 

Tag Set (ITS) 2.0 

The ITS 2.0 specification 

enhances the foundation to 

integrate automated processing 

of human language into core 

Web technologies and concepts 

that are designed to foster the 

automated creation and 

processing of multilingual Web 

content.

 I U I/U I/U 

W3C OWL 2 Web 

Ontology Language 

The OWL 2 Web Ontology 

Language, informally OWL 2, 

is an ontology language for the 

Semantic Web with formally 

defined meaning. 

I U I/U I/U 

W3C Platform for 

Privacy Preferences 

(P3P) 1.0 

The P3P enables Web sites to 

express their privacy practices 

in a standard format that can be 

retrieved automatically and 

interpreted easily by user 

agents. 

I U I/U I/U 

W3C Protocol for Web 

Description Resources 

(POWDER) 

POWDER—the Protocol for 

Web Description Resources— 

provides a mechanism to 

describe and discover Web 

resources and helps the users to 

make a decision whether a 

given resource is of interest. 

I U I/U 

W3C Provenance Provenance is information 

about entities, activities, and 

people involved in producing a 

piece of data or thing, which 

can be used to form 

assessments about its quality, 

reliability, or trustworthiness. 

The Provenance Family of 

Documents (PROV) defines a 

model, corresponding 

serializations, and other 

supporting definitions to enable 

the interoperable interchange of 

provenance information in 

heterogeneous environments 

such as the Web. 

I U I/U I/U U 

24
 
 

 

 

 

 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

W3C Rule Interchange 

Format (RIF) 

RIF is a series of standards for 

exchanging rules among rule 

systems, in particular among 

Web rule engines. 

I U I/U I/U 

W3C Service Modeling 

Language (SML) 1.1 

This specification defines the 

SML, Version 1.1 used to 

model complex services and 

systems, including their 

structure, constraints, policies, 

and best practices. 

I/U I U I/U 

W3C Simple 

Knowledge 

Organization System 

Reference (SKOS) 

This document defines the 

SKOS, a common data model 

for sharing and linking 

knowledge organization 

systems via the Web. 

I U I/U 

W3C Simple Object 

Access Protocol (SOAP) 

1.2 

SOAP is a protocol 

specification for exchanging 

structured information in the 

implementation of web services 

in computer networks. 

I U I/U 

W3C SPARQL 1.1 SPARQL is a language 

specification for the query and 

manipulation of linked data in a 

RDF format. 

I U I/U I/U 

W3C Web Service 

Description Language 

(WSDL) 2.0 

This specification describes the 

WSDL Version 2.0, an XML 

language for describing Web 

services. 

U I U I/U 

W3C XML Key 

Management 

Specification (XKMS) 

2.0 

This standard specifies 

protocols for distributing and 

registering public keys, suitable 

for use in conjunction with the 

W3C Recommendations for 

XML Signature [XML-SIG] 

and XML Encryption [XMLEnc]. The XKMS comprises 

two parts — the XML Key 

Information Service 

Specification (X-KISS) and the 

XML Key Registration Service 

Specification (X-KRSS). 

U I U I/U 

OGC® OpenGIS® 

Catalogue Services 

Specification 2.0.2 -

ISO Metadata 

Application Profile 

This series of standard covers 

Catalogue Services based on 

ISO19115/ISO19119, which are 

organized and implemented for 

the discovery, retrieval and 

management of data metadata, 

services metadata, and 

application metadata. 

I U I/U 

25
 
 

 

 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

OGC® OpenGIS® 

GeoAPI 

The GeoAPI Standard defines, 

through the GeoAPI library, a 

Java language API including a 

set of types and methods which 

can be used for the 

manipulation of geographic 

information structured 

following the specifications 

adopted by the Technical 

Committee 211 of the ISO and 

by the OGC® . 

I U I/U I/U 

OGC® OpenGIS® 

GeoSPARQL 

The OGC® GeoSPARQL 

standard supports representing 

and querying geospatial data on 

the Semantic Web. 

GeoSPARQL defines a 

vocabulary for representing 

geospatial data in RDF, and it 

defines an extension to the 

SPARQL query language for 

processing geospatial data.

 I U I/U I/U 

OGC® OpenGIS® 

Geography Markup 

Language (GML) 

Encoding Standard 

The GML is an XML grammar 

for expressing geographical 

features. GML serves as a 

modeling language for 

geographic systems as well as 

an open interchange format for 

geographic transactions on the 

Internet. 

I U I/U I/U 

OGC® Geospatial 

eXtensible Access 

Control Markup 

Language 

(GeoXACML) Version 

1 

The Policy Language 

introduced in this document 

defines a geo-specific extension 

to the XACML Policy 

Language, as defined by the 

OASIS standard eXtensible 

Access Control Markup 

Language (XACML), Version 

2.0.

 I U I/U I/U I/U 

OGC® Network 

Common Data Form 

(netCDF) 

netCDF is a set of software 

libraries and self-describing, 

machine-independent data 

formats that support the 

creation, access, and sharing of 

array-oriented scientific data. 

I U I/U 

26
 
 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

OGC® Open Modelling 

Interface Standard 

(OpenMI) 

The purpose of the OpenMI is 

to enable the runtime exchange 

of data between process 

simulation models and also 

between models and other 

modelling tools such as 

databases and analytical and 

visualization applications. 

I U I/U I/U 

OGC® OpenSearch Geo 

and Time Extensions 

This OGC standard specifies 

the Geo and Time extensions to 

the OpenSearch query protocol. 

OpenSearch is a collection of 

simple formats for the sharing 

of search results. 

I U I/U I 

OGC® Web Services 

Context Document 

(OWS Context) 

The OGC® OWS Context was 

created to allow a set of 

configured information 

resources (service set) to be 

passed between applications 

primarily as a collection of 

services. 

I U I/U I 

OGC® Sensor Web 

Enablement (SWE) 

This series of standards support 

interoperability interfaces and 

metadata encodings that enable 

real-time integration of 

heterogeneous sensor webs. 

These standards include a 

modeling language 

(SensorML), common data 

model, and sensor observation, 

planning, and alerting service 

interfaces. 

I U I/U 

OGC® OpenGIS® 

Simple Features Access 

Describes the common 

architecture for simple feature 

geometry and is also referenced 

as ISO 19125. It also 

implements a profile of the 

spatial schema described in ISO 

19107:2003.

 I U I/U I/U 

OGC® OpenGIS® 

Georeferenced Table 

Joining Service (TJS) 

Implementation 

Standard 

This standard is the 

specification for a TJS that 

defines a simple way to 

describe and exchange tabular 

data that contains information 

about geographic objects. 

I U I/U I/U 

27
 
 

 

 

 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

OGC® OpenGIS® Web 

Coverage Processing 

Service Interface 

(WCPS) Standard 

Defines a protocol-independent 

language for the extraction, 

processing, and analysis of 

multi-dimensional gridded 

coverages representing sensor, 

image, or statistics data. 

I U I/U I 

OGC® OpenGIS® Web 

Coverage Service 

(WCS) 

This document specifies how a 

WCS offers multidimensional 

coverage data for access over 

the Internet. This document 

specifies a core set of 

requirements that a WCS 

implementation must fulfill. 

I U I/U I 

OGC® Web Feature 

Service (WFS) 2.0 

Interface Standard 

The WFS standard provides for 

fine-grained access to 

geographic information at the 

feature and feature property 

level. This International 

Standard specifies discovery 

operations, query operations, 

locking operations, transaction 

operations, and operations to 

manage stored, parameterized 

query expressions. 

I U I/U I 

OGC® OpenGIS® Web 

Map Service (WMS) 

Interface Standard 

The OpenGIS® WMS Interface 

Standard provides a simple 

HTTP interface for requesting 

geo-registered map images 

from one or more distributed 

geospatial databases. 

I U I/U I 

OGC® OpenGIS® Web 

Processing Service 

(WPS) Interface 

Standard 

The OpenGIS® WPS Interface 

Standard provides rules for 

standardizing how inputs and 

outputs (requests and 

responses) for geospatial 

processing services, such as 

polygon overlay. The standard 

also defines how a client can 

request the execution of a 

process, and how the output 

from the process is handled. It 

defines an interface that 

facilitates the publishing of 

geospatial processes and 

clients’ discovery of and 

binding to those processes. 

I U I/U I 

OASIS AS4 Profile of 

ebMS 3.0 v1.0 

Standard for business to 

business exchange of messages 

via a web service platform. 

I U I/U 

28
 
 

 

 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

OASIS Advanced 

Message Queuing 

Protocol (AMQP) 

Version 1.0 

The AMQP is an open internet 

protocol for business 

messaging. It defines a binary 

wire-level protocol that allows 

for the reliable exchange of 

business messages between two 

parties. 

I U U I 

OASIS Application 

Vulnerability 

Description Language 

(AVDL) v1.0 

This specification describes a 

standard XML format that 

allows entities (such as 

applications, organizations, or 

institutes) to communicate 

information regarding web 

application vulnerabilities. 

I U I U 

OASIS Biometric 

Identity Assurance 

Services (BIAS) Simple 

Object Access Protocol 

(SOAP) Profile v1.0 

This OASIS BIAS profile 

specifies how to use XML 

(XML10) defined in ANSI 

INCITS 442-2010—BIAS to 

invoke SOAP-based services 

that implement BIAS 

operations.

 I U I/U U 

OASIS Content 

Management 

Interoperability 

Services (CMIS) 

The CMIS standard defines a 

domain model and set of 

bindings that include Web 

Services and ReSTful AtomPub 

that can be used by applications 

to work with one or more 

Content Management 

repositories/systems. 

I U I/U I 

OASIS Digital 

Signature Service (DSS) 

This specification describes two 

XML-based request/response 

protocols - a signing protocol 

and a verifying protocol. 

Through these protocols, a 

client can send documents (or 

document hashes) to a server 

and receive back a signature on 

the documents; or send 

documents (or document 

hashes) and a signature to a 

server, and receive back an 

answer on whether the signature 

verifies the documents. 

I U I/U 

29
 
 

 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

OASIS Directory 

Services Markup 

Language (DSML) v2.0 

The DSML provides a means 

for representing directory 

structural information as an 

XML document methods for 

expressing directory queries 

and updates (and the results of 

these operations) as XML 

documents 

I U I/U I 

OASIS ebXML 

Messaging Services 

These specifications define a 

communications-protocol 

neutral method for exchanging 

electronic business messages as 

XML.

 I U I/U 

OASIS ebXML RegRep ebXML RegRep is a standard 

defining the service interfaces, 

protocols, and information 

model for an integrated registry 

and repository. The repository 

stores digital content while the 

registry stores metadata that 

describes the content in the 

repository. 

I U I/U I 

OASIS ebXML 

Registry Information 

Model 

The Registry Information 

Model provides a blueprint or 

high-level schema for the 

ebXML Registry. It provides 

implementers with information 

on the type of metadata that is 

stored in the Registry as well as 

the relationships among 

metadata Classes. 

I U I/U 

OASIS ebXML 

Registry Services 

Specification 

An ebXML Registry is an 

information system that 

securely manages any content 

type and the standardized 

metadata that describes it. The 

ebXML Registry provides a set 

of services that enable sharing 

of content and metadata 

between organizational entities 

in a federated environment. 

I U I/U 

OASIS eXtensible 

Access Control Markup 

Language (XACML) 

The standard defines a 

declarative access control 

policy language implemented in 

XML and a processing model 

describing how to evaluate 

access requests according to the 

rules defined in policies.

 I U I/U I/U I/U 

30
 
 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

OASIS Message 

Queuing Telemetry 

Transport (MQTT) 

MQTT is a Client Server 

publish/subscribe messaging 

transport protocol for 

constrained environments such 

as for communication in 

Machine to Machine and 

Internet of Things contexts 

where a small code footprint is 

required and/or network 

bandwidth is at a premium. 

I U I/U 

OASIS Open Data 

(OData) Protocol 

The OData Protocol is an 

application-level protocol for 

interacting with data via 

RESTful interfaces. The 

protocol supports the 

description of data models and 

the editing and querying of data 

according to those models. 

I U I/U I/U 

OASIS Search Web 

Services (SWS) 

The OASIS SWS initiative 

defines a generic protocol for 

the interaction required between 

a client and server for 

performing searches. SWS 

define an Abstract Protocol 

Definition to describe this 

interaction. 

I U I/U 

OASIS Security 

Assertion Markup 

Language (SAML) v2.0 

The SAML defines the syntax 

and processing semantics of 

assertions made about a subject 

by a system entity. This 

specification defines both the 

structure of SAML assertions 

and an associated set of 

protocols, in addition to the 

processing rules involved in 

managing a SAML system. 

I U I/U I/U I/U 

OASIS SOAP-overUDP (User Datagram 

Protocol) v1.1 

This specification defines a 

binding of SOAP to user 

datagrams, including message 

patterns, addressing 

requirements, and security 

considerations. 

I U I/U 

31
 
 

 

 

 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

OASIS Solution 

Deployment Descriptor 

Specification v1.0 

This specification defines 

schema for two XML document 

types: Package Descriptors and 

Deployment Descriptors. 

Package Descriptors define 

characteristics of a package 

used to deploy a solution. 

Deployment Descriptors define 

characteristics of the content of 

a solution package, including 

the requirements that are 

relevant for creation, 

configuration, and maintenance 

of the solution content. 

U I/U 

OASIS Symptoms 

Automation Framework 

(SAF) Version 1.0 

This standard defines reference 

architecture for the Symptoms 

Automation Framework, a tool 

in the automatic detection, 

optimization, and remediation 

of operational aspects of 

complex systems. 

I/U 

OASIS Topology and 

Orchestration 

Specification for Cloud 

Applications Version 

1.0 

The concept of a “service 

template” is used to specify the 

“topology” (or structure) and 

“orchestration” (or invocation 

of management behavior) of IT 

services. This specification 

introduces the formal 

description of Service 

Templates, including their 

structure, properties, and 

behavior. 

I/U U I I/U 

OASIS Universal 

Business Language 

(UBL) v2.1 

The OASIS UBL defines a 

generic XML interchange 

format for business documents 

that can be restricted or 

extended to meet the 

requirements of particular 

industries. 

I U I/U U 

OASIS Universal 

Description, Discovery 

and Integration (UDDI) 

v3.0.2 

The focus of UDDI is the 

definition of a set of services 

supporting the description and 

discovery of (1) businesses, 

organizations, and other Web 

services providers, (2) the Web 

services they make available, 

and (3) the technical interfaces 

which may be used to access 

those services. 

I U I/U U 

32
 
 

 

 

 

 

 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

OASIS Unstructured 

Information 

Management 

Architecture (UIMA) 

v1.0 

The UIMA specification 

defines platform-independent 

data representations and 

interfaces for text and 

multimodal analytics. 

U I 

OASIS Unstructured 

Operation Markup 

Language (UOML) v1.0 

UOML is an interface standard 

to process unstructured 

documents; it plays the similar 

role as SQL to structured data. 

UOML is expressed with 

standard XML.

 I U I/U I 

OASIS/W3C WebCGM 

v2.1 

Computer Graphics Metafile 

(CGM) is an ISO standard, 

defined by ISO/IEC 8632:1999, 

for the interchange of 2D vector 

and mixed vector/raster 

graphics. WebCGM is a profile 

of CGM, which adds Web 

linking and is optimized for 

Web applications in technical 

illustration, electronic 

documentation, geophysical 

data visualization, and similar 

fields. 

I U I/U I 

OASIS Web Services 

Business Process 

Execution Language 

(WS-BPEL) v2.0 

This standard defines a 

language for specifying 

business process behavior based 

on Web services. WS-BPEL 

provides a language for the 

specification of Executable and 

Abstract business processes. 

U I 

OASIS/W3C - Web 

Services Distributed 

Management (WSDM): 

Management Using 

Web Services (MUWS) 

v1.1 

MUWS defines how an IT 

resource connected to a network 

provides manageability 

interfaces such that the IT 

resource can be managed 

locally and from remote 

locations using Web services 

technologies. 

U I I U U 

OASIS WSDM: 

Management of Web 

Services (MOWS) v1.1 

This part of the WSDM 

specification addresses 

management of the Web 

services endpoints using Web 

services protocols. 

U I I U U 

OASIS Web Services 

Dynamic Discovery 

(WS-Discovery) v1.1 

This specification defines a 

discovery protocol to locate 

services. The primary scenario 

for discovery is a client 

searching for one or more target 

services. 

U I U I/U U 

33
 
 

 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Standard 

Name/Number Description 

NBDRA Components 

SO DP DC BDAP BDF 

P 

S&P M 

OASIS Web Services 

Federation Language 

(WS-Federation) v1.2 

This specification defines 

mechanisms to allow different 

security realms to federate, such 

that authorized access to 

resources managed in one realm 

can be provided to security 

principals whose identities and 

attributes are managed in other 

realms. 

I U I/U U 

OASIS Web Services 

Notification (WSN) v1.3 

WSN is a family of related 

specifications that define a 

standard Web services approach 

to notification using a topicbased publish/subscribe pattern. 

I U I/U 

IETF Simple Network 

Management Protocol 

(SNMP) v3 

SNMP is a series of IETFsponsored standards for remote 

management of system/network 

resources and transmission of 

status regarding network 

resources. The standards 

include definitions of standard 

management objects along with 

security controls. 

I I I/U U 

IETF Extensible 

Provisioning Protocol 

(EPP) 

This IETF series of standards 

describes an application-layer 

client-server protocol for the 

provisioning and management 

of objects stored in a shared 

central repository. Specified in 

XML, the protocol defines 

generic object management 

operations and an extensible 

framework that maps protocol 

operations to objects. 

U I/U 

Table Notes: 

SO = System Orchestrator component 

DP = Data Provider component 

DC = Data Consumer component 

BDAP = Big Data Application Provider component 

BDFP = Big Data Framework Provider component 

S&P = Security and Privacy Fabric 

M = Management Fabric 

6.2 GAP IN STANDARDS 

The potential gaps in Big Data standardization are provided in this section to describe broad areas that 

may be of interest to SDOs, consortia, and readers of this document. The list provided below was 

produced by an ISO/IEC Joint Technical Committee 1 (JTC1) Study Group on Big Data to serve as a 

potential guide to ISO in their establishment of Big Data standards activities. 3 The potential Big Data 

standardization gaps, identified by the study group, described broad areas that may be of interest to this 

community. These gaps in standardization activities related to Big Data are in the following areas: 

34
 
NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

1.	 Big Data use cases, definitions, vocabulary and reference architectures (e.g., system, data, 

platforms, online/offline) 

2.	 Specifications and standardization of metadata including data provenance 

3.	 Application models (e.g., batch, streaming) 

4.	 Query languages including non-relational queries to support diverse data types (e.g., XML, RDF, 

JSON, multimedia) and Big Data operations (e.g., matrix operations) 

5.	 Domain-specific languages 

6.	 Semantics of eventual consistency 

7.	 Advanced network protocols for efficient data transfer 

8.	 General and domain-specific ontologies and taxonomies for describing data semantics including 

interoperation between ontologies 

9.	 Big Data security and privacy access controls 

10. Remote, distributed, and federated analytics (taking the analytics to the data) including data and 

processing resource discovery and data mining 

11. Data sharing and exchange 

12. Data storage (e.g., memory storage system, distributed file system, data warehouse) 

13. Human consumption of the results of big data analysis (e.g., visualization) 

14. Energy measurement for Big Data 

15. Interface between relational (i.e., SQL) and non-relational (i.e., not only [or no] Structured Query 

Language [NoSQL]) data stores 

16. Big Data quality and veracity description and management 

6.3 PATHWAY TO ADDRESS STANDARDS GAPS 

Standards often evolve from implementation of best practices and approaches which are proven against 

real-world applications or from theory that is tuned to reflect additional variables and conditions 

uncovered during implementation. In the case of Big Data, most standards are evolving from existing 

standards modified to address the unique characteristics of Big Data. Like many terms that have come 

into common usage in the current information age, Big Data has many possible meanings depending on 

the context from which it is viewed. Big Data discussions are complicated by the lack of accepted 

definitions, taxonomies, and common reference views. The products of the NBD-PWG are designed to 

specifically address the lack of consistency. Recognizing this lack of a common framework on which to 

build standards, ISO/IEC JTC1 has specifically charted a working group, which will first focus on 

developing common definitions and a reference architecture. Once established, the definitions and 

reference architecture will form the basis for evolution of existing standards to meet the unique needs of 

Big Data and evaluation of existing implementations and practices as candidates for new Big Data-related 

standards. In the first case, existing standards efforts may address these gaps by either expanding or 

adding to the existing standard to accommodate Big Data characteristics or developing Big Data unique 

profiles within the framework of the existing standards. The exponential growth of data is already 

resulting in the development of new theories addressing topics from synchronization of data across large 

distributed computing environments to addressing consistency in high volume and velocity environments. 

As actual implementations of technologies are proven, reference implementations will evolve based on 

community-accepted open source efforts. 

35
 
 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Acronyms A: Acronyms 

AMQP Advanced Message Queuing Protocol 

ANSI American National Standards Institute 

API application programming interface 

AVDL Application Vulnerability Description Language 

BDAP Big Data Application Provider component 

BDFP Big Data Framework Provider component 

BIAS Biometric Identity Assurance Services 

CGM Computer Graphics Metafile 

CIA confidentiality, integrity, and availability 

CMIS Content Management Interoperability Services 

CPR Capability Provider Requirements 

DC Data Consumer component 

DCAT Data Catalog Vocabulary 

DCR Data Consumer Requirements 

DOM Document Object Model 

DP Data Provider component 

DSML Directory Services Markup Language 

DSR Data Source Requirements 

DSS Digital Signature Service 

EPP Extensible Provisioning Protocol 

EXI Efficient XML Interchange 

GeoXACML Geospatial eXtensible Access Control Markup Language 

GML Geography Markup Language 

GRC governance, risk management, and compliance 

HTML HyperText Markup Language 

IEC International Electrotechnical Commission 

IEEE Institute of Electrical and Electronics Engineers 

IETF Internet Engineering Task Force 

INCITS International Committee for Information Technology Standards 

ISO International Organization for Standardization 

IT information technology 

ITL Information Technology Laboratory 

ITS Internationalization Tag Set 

JPEG Joint Photographic Experts Group 

JSON JavaScript Object Notation 

JSR Java Specification Request 

JTC1 Joint Technical Committee 1 

LMR Life cycle Management Requirements 

M Management Fabric 

MFI Metamodel Framework for Interoperability 

MOWS Management of Web Services 

MPEG Moving Picture Experts Group 

MQTT Message Queuing Telemetry Transport 

MUWS Management Using Web Services 

NARA National Archives and Records Administration 

NASA National Aeronautics and Space Administration 

NBD-PWG NIST Big Data Public Working Group 

NCAP Network Capable Application Processor 

A-1 
 

 

 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

netCDF Network Common Data Form 

NIST National Institute of Standards and Technology 

NoSQL Not only (or no) Structured Query Language 

NSF National Science Foundation 

OASIS Organization for the Advancement of Structured Information Standards 

OData Open Data 

ODMS On-Demand Model Selection 

OGC Open Geospatial Consortium 

OpenMI Open Modelling Interface Standard 

OR Other Requirements 

OWS Context OGC Web Services Context Document 

P3P Platform for Privacy Preferences Project 

PICS Platform for Internet Content Selection 

POWDER Protocol for Web Description Resources 

RDF Resource Description Framework 

RFID radio frequency identification 

RIF Rule Interchange Format 

S&P Security and Privacy Fabric 

SAF Symptoms Automation Framework 

SAML Security Assertion Markup Language 

SDO standards development organization 

SKOS Simple Knowledge Organization System Reference 

SLA service-level agreement 

SML Service Modeling Language 

SNMP Simple Network Management Protocol 

SO System Orchestrator component 

SOAP Simple Object Access Protocol 

SPR Security and Privacy Requirements 

SQL Structured Query Language 

SWE Sensor Web Enablement 

SWS Search Web Services 

TEDS Transducer Electronic Data Sheet 

TJS Table Joining Service 

TPR Transformation Provider Requirements 

TR Technical Report 

UBL Universal Business Language 

UDDI Universal Description, Discovery, and Integration 

UDP User Datagram Protocol 

UIMA Unstructured Information Management Architecture 

UOML Unstructured Operation Markup Language 

W3C World Wide Web Consortium 

WCPS Web Coverage Processing Service Interface 

WCS Web Coverage Service 

WFS Web Feature Service 

WMS Web Map Service 

WPS Web Processing Service 

WS-BPEL Web Services Business Process Execution Language 

WS-Discovery Web Services Dynamic Discovery 

WSDL Web Services Description Language 

WSDM Web Services Distributed Management 

WS-Federation Web Services Federation Language 

A-2 
NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

WSN Web Services Notification 

XACML eXtensible Access Control Markup Language 

XDM XPath Data Model 

X-KISS XML Key Information Service Specification 

XKMS XML Key Management Specification 

X-KRSS XML Key Registration Service Specification 

XMI XML Metadata Interchange 

XML Extensible Markup Language 

XSLT XSL Transformations 

A-3 
 

 

 

 

 

NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 7, STANDARDS ROADMAP 

Appendix B: References 

GENERAL RESOURCES 

Institute of Electrical and Electronics Engineers (IEEE). https://www.ieee.org/index.html
 

International Committee for Information Technology Standards (INCITS). http://www.incits.org/
 

International Electrotechnical Commission (IEC). http://www.iec.ch/
 

International Organization for Standardization (ISO). http://www.iso.org/iso/home.html
 

Open Geospatial Consortium (OGC). http://www.opengeospatial.org/
 

Open Grid Forum (OGF). https://www.ogf.org/ogf/doku.php
 

Organization for the Advancement of Structured Information Standards (OASIS). https://www.oasisopen.org/
 

World Wide Web Consortium (W3C). http://www.w3.org/
 

DOCUMENT REFERENCES 

1

 The White House Office of Science and Technology Policy, “Big Data is a Big Deal,” OSTP Blog, accessed 

February 21, 2014, http://www.whitehouse.gov/blog/2012/03/29/big-data-big-deal. 

2

 Cloud Security Alliance, Expanded Top Ten Big Data Security and Privacy Challenges, April 2013. 

https://downloads.cloudsecurityalliance.org/initiatives/bdwg/Expanded_Top_Ten_Big_Data_Security_and_Privacy_ 

Challenges.pdf. 

3

 “Big Data, Preliminary Report 2014,” ISO/IEC JTC1: Information Technology. 

http://www.iso.org/iso/big_data_report-jtc1.pdf. (Accessed March 2, 2015). Pages 21-23. 

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