scholarly journals R-Wingu, the ‘Big Data’ Analytic Framework: a Solution to Intelligent Correlation of Research Output in a Private Cloud Prototype for Seamless Research Ontologies

2014 ◽  
Vol 2 (4) ◽  
pp. 89-93
Author(s):  
Duncan Waga ◽  
Kefa Rabah ◽  
James Ogalo
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.


2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Norma Alias ◽  
Nadia Nofri Yeni Suhari ◽  
Hafizah Farhah Saipan Saipol ◽  
Abdullah Aysh Dahawi ◽  
Masyitah Mohd Saidi ◽  
...  

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance.


Author(s):  
Ömer Özgenç ◽  
◽  
Nur Çağlar ◽  
Işıl Ruhi-Sipahioğlu

Global research output grows exponentially each year. This paper attempts to drive meaning out of this big data on two fields of research in architecture. It maps the interaction between the research fields of sustainability in architecture and architectural education through the perspective of bibliometric data analysis and its visualization. Based on the analysis of bibliometric data, it draws and juxtaposes two timelines for the field of sustainable architecture and the field of architectural education. The objective is to propose a retrospective method that can provide insight for a broader understanding of sustainability and its impacts on architectural education. It utilizes VOSviewer, CiteSpace, and Gephi to visualize bibliometric networks, along with Tableau to analyze the number of journal articles and publications published across years. The paper presents initial findings concerning the leading scholars, trends, and patterns of the research areas, milestone events, and dominant studies to point out the significance of the cooperation between research and education fields of the related topic.


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