Model-Driven Engineering for Big Data

Accessing heterogeneous and huge amount of data through different sources heavily impacts users of data nowadays worldwide. Thus, Big Data has now become a hot emerging paradigm in computing environments. Issues in scalability, interoperability, platform independency, adaptability and reusability in big data systems are considered the main current challenges. This raises the need for appropriate software engineering approaches to develop effective and efficient Big Data system models, i.e. an approach which reduce investment cost and development time. Today, software engineering has emerged advanced methodologies to solve problems from different perspectives, while still further research is needed to overcome new challenges raised in emerging technologies, i.e. Big Data. Thus, the author believe model-driven engineering technique is the appropriate approach to alleviate complexities in modeling Big Data system

Big Data Reference Architectures, a systematic literature review

Today, we live in a world that produces data at an unprecedented rate. The significant amount of data has raised lots of attention and many strive to harness the power of this new material. In the same direction, academics and practitioners have considered means through which they can incorporate datadriven functions and explore patterns that were otherwise unknown. This has led to a concept called Big Data. Big Data is a field that deals with data sets that are too large and complex for traditional approaches to handle. Technical matters are fundamentally critical, but what is even more necessary, is an architecture that supports the orchestration of Big Data systems; an image of the system providing with clear understanding of different elements and their interdependencies. Reference architectures aid in defining the body of system and its key components, relationships, behaviors, patterns and limitations. This study provides an in-depth review of Big Data Reference Architectures by applying a systematic literature review. The study demonstrates a synthesis of high-quality research to offer indications of new trends. The study contributes to the body of knowledge on the principles of Reference Architectures, the current state of Big Data Reference Architectures, and their limitations

A system engineering study and concept development for a Humanitarian Aid and Disaster Relief Operations Management Platform

This thesis develops a concept and initial system definition of a Humanitarian Aid and Disaster Relief (HADR) Operations Management Platform (OMP) that supports various stakeholders involved in time critical humanitarian response efforts. The concept for the OMP explores the various functions necessary to manage HADR operations to include facilitation of information exchange, collaboration among disaster responders, and a common operating picture (COP) that informs decision makers of the operational environment. The development of the OMP uses system engineering methodologies and a tailored development process to identify the requirements, functions, and architecture necessary to support the platform. The OMP concept also includes multiple data sources for near real-time information and support tools for assessments, planning, implementation, execution, and evaluation. This thesis also assesses advances in technology and applications to more effectively support and manage HADR efforts. As such, the OMP takes into consideration how current HADR operations are conducted today, and the role of virtual volunteers in supporting the platform. These virtual volunteers support the HADR effort by conducting tasks virtually via their computers and an internet connection anywhere in the world.http://archive.org/details/asystemengineeri1094550472Captain, United States Air ForceApproved for public release; distribution is unlimited

Secure Development of Big Data Ecosystems

A Big Data environment is a powerful and complex ecosystem that helps companies extract important information from data to make the best business and strategic decisions. In this context, due to the quantity, variety, and sensitivity of the data managed by these systems, as well as the heterogeneity of the technologies involved, privacy and security especially become crucial issues. However, ensuring these concerns in Big Data environments is not a trivial issue, and it cannot be treated from a partial or isolated perspective. It must be carried out through a holistic approach, starting from the definition of requirements and policies, and being present in any relevant activity of its development and deployment. Therefore, in this paper, we propose a methodological approach for integrating security and privacy in Big Data development based on main standards and common practices. In this way, we have defined a development process for this kind of ecosystems that considers not only security in all the phases of the process but also the inherent characteristics of Big Data. We describe this process through a set of phases that covers all the relevant stages of the development of Big Data environments, which are supported by a customized security reference architecture (SRA) that defines the main components of this kind of systems along with the key concepts of security

Towards a Reference Architecture with Modular Design for Large-scale Genotyping and Phenotyping Data Analysis: A Case Study with Image Data

With the rapid advancement of computing technologies, various scientific research communities have been extensively using cloud-based software tools or applications. Cloud-based applications allow users to access software applications from web browsers while relieving them from the installation of any software applications in their desktop environment. For example, Galaxy, GenAP, and iPlant Colaborative are popular cloud-based systems for scientific workflow analysis in the domain of plant Genotyping and Phenotyping. These systems are being used for conducting research, devising new techniques, and sharing the computer assisted analysis results among collaborators. Researchers need to integrate their new workflows/pipelines, tools or techniques with the base system over time. Moreover, large scale data need to be processed within the time-line for more effective analysis. Recently, Big Data technologies are emerging for facilitating large scale data processing with commodity hardware. Among the above-mentioned systems, GenAp is utilizing the Big Data technologies for specific cases only. The structure of such a cloud-based system is highly variable and complex in nature. Software architects and developers need to consider totally different properties and challenges during the development and maintenance phases compared to the traditional business/service oriented systems. Recent studies report that software engineers and data engineers confront challenges to develop analytic tools for supporting large scale and heterogeneous data analysis. Unfortunately, less focus has been given by the software researchers to devise a well-defined methodology and frameworks for flexible design of a cloud system for the Genotyping and Phenotyping domain. To that end, more effective design methodologies and frameworks are an urgent need for cloud based Genotyping and Phenotyping analysis system development that also supports large scale data processing. In our thesis, we conduct a few studies in order to devise a stable reference architecture and modularity model for the software developers and data engineers in the domain of Genotyping and Phenotyping. In the first study, we analyze the architectural changes of existing candidate systems to find out the stability issues. Then, we extract architectural patterns of the candidate systems and propose a conceptual reference architectural model. Finally, we present a case study on the modularity of computation-intensive tasks as an extension of the data-centric development. We show that the data-centric modularity model is at the core of the flexible development of a Genotyping and Phenotyping analysis system. Our proposed model and case study with thousands of images provide a useful knowledge-base for software researchers, developers, and data engineers for cloud based Genotyping and Phenotyping analysis system development