A Reference Architecture for Big Data Systems in the National Security Domain

A Reference Architecture for Big Data Systems in the National Security Domain

Table of Contents


Acquirers, system builders, and other stakeholders of big data systems need to define requirements, develop and evaluate solutions, and integrate systems together. A reference architecture enables these software engineering activities by standardizing nomenclature, defining key solution elements and their relationships, collecting relevant solution patterns, and classifying existing technologies. Within the national security domain, existing reference architectures for big data systems have not been useful because they are too general or are not vendor-neutral. We present a reference architecture for big data systems that are focused on addressing typical national defense requirements and that is vendor-neutral, and we demonstrate how to use this reference architecture to define solutions in one mission area.


Reference architecture; big data


systems used by government organizations such as police at the local, state, and federal levels; military; and intelligence. Big data systems are pervasive in this domain, with applications ranging
• Predictive maintenance of aircraft, ships, and vehicles, combining measured data collected on the platform with meteorological data, equipment supplier data, and other sources to optimize maintenance schedules (e.g., [1]).
• Geospatial analytics that identifies movement and changes of features on the ground, to support tactical, operational, and strategic intelligence analysis and planning.
• Network graph analysis to help police identify associates and organizational affiliations.
Stakeholders who specify, evaluate, and acquire these big data systems often lack software engineering technical expertise in this emerging and dynamic technology space [2]. While these
stakeholders may have competence in other types of software systems, the principles, and practices for big data systems are different, and general software knowledge may not be sufficient to ensure success [3].
A reference architecture (RA) serves as a mechanism to represent and transfer software engineering knowledge that bridges from the problem domain to a family of solutions. A RA defines domain concepts and relevant qualities decompose the solution and create a lexicon to enable efficient communication, and provides guidance and principles for system stakeholders [4].
There are a number of published RAs for big data systems.
However, these were not useful for our clients in the national security domain, because they were too general (e.g., [5], [6], or[7]) or because the solutions were specific to a particular vendor’s technology (e.g., [8]). We discuss these in more detail in the Related Work section below.
The contribution of this paper is a big data RA for applications in the national security domain, which includes:
• Motivating use cases;
• Architecture decomposition based on grouping of related
concerns into architectural modules;
• Mapping of current technologies onto the concerns;
• Demonstration of how to use the RA to create big data system architectures.


We have described a reference architecture for big data systems in the national security application domain, including the principles used to organize the architecture decomposition. This RA serves as knowledge capture and transfer mechanism, containing both domain knowledge (such as use cases) and solution knowledge (such as mapping to concrete technologies). We have also shown how the RA can be used to define architectures for big data systems in our domain.
Future work includes:
• Using the module decomposition in the RA to make decisions on where to standardize interfaces and implementations within a particular enterprise;
• Creating new narrow and deep knowledge bases, similar to QuABaseBD (www.quabase.sei.cmu.edu) for other modules within the RA;
• Evaluating the utility of the RA to define software product lines for sub-domains within the scope of the RA;
• Creating instantiations of the RA for specific use cases within the intelligence domain.

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.

Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.

The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.

FULL Paper PDF file:




John Klein

Ross Buglak, David Blockow, Troy Wuttke, Brenton Coope





PDF reference and original file: Click here


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Maryam kakaei was born in 1984 in Arak. She holds a Master's degree in Software Engineering from Azad University of Arak.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.