An Integrated Framework for Disaster Event Analysis in Big Data Environments

Author(s):  
Pyke Tin ◽  
Thi Thi Zin ◽  
Takashi Toriu ◽  
Hiromitsu Hama
Author(s):  
C.S. Sindhu ◽  
Nagaratna P. Hegde

With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an error-free data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in one execution time. Considering the synthetic big data of healthcare, we carried out the investigation to find that our proposed system using deep learning architecture offers better optimization of computational resources. The study outcome is found to offer comparatively better response time and higher accuracy rate as compared to existing optimization technqiues that is found and practiced widely in literature.


2017 ◽  
Vol 10 (13) ◽  
pp. 207
Author(s):  
Pranav Vilas Vaidya ◽  
Janaki Meena M ◽  
Syed Ibrahim Sp

Mobile analytics studies the behavior of end users of mobile applications and the mobile application itself. These mobile applications, being an important part of the various businesses products, need to be monitored and the usage patterns are to be analyzed. The data collected from these apps can help to drive important business strategies by identifying the usage patterns. Enriching the data with information available from other sources, like sales/service information, provides holistic view about the solution. Thus, here we aim at exploring some set of tools that give capabilities as event trailing with higher extraction of its linguistics. If the application is used worldwide, the data generated out of it is Big Data, which traditional systems cannot handle. We therefore propose a special framework for efficient data collection, storage and processing at Big Data scale on cloud platform.  


2017 ◽  
Vol 10 (3) ◽  
pp. 229-251 ◽  
Author(s):  
Sarah Cheah ◽  
Shenghui Wang

Purpose This study aims to construct mechanisms of big data-driven business model innovation from the market, strategic and economic perspectives and core logic of business model innovation. Design/methodology/approach The authors applied deductive reasoning and case analysis method on manufacturing firms in China to validate the mechanisms. Findings The authors have developed an integrated framework to deduce the elements of big data-driven business model innovation. The framework comprises three elements: perspectives, business model processes and big data-driven business model innovations. As we apply the framework on to three Chinese companies, it is evident that the mechanisms of business model innovation based on big data is a progressive and dynamic process. Research limitations/implications The case sample is relatively small, which is a typical trade-off in qualitative research. Practical implications A robust infrastructure that seamlessly integrates internet of things, front-end customer systems and back-end production systems is pivotal for companies. The management has to ensure its organization structure, climate and human resources are well prepared for the transformation. Social implications When provided with a convenient crowdsourcing platform to provide feedback and witness their suggestions being implemented, users are more likely to share insights about their use experience. Originality/value Extant studies of big data and business model innovation remain disparate. By adding a new dimension of intellectual and economic resource to the resource-based view, this paper posits an important link between big data and business model innovation. In addition, this study has contributed to the theoretical lens of value by contextualizing the value components of a business model and providing an integrated framework.


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