A Comprehensive Review and Open Challenges of Stream Big Data

Author(s):  
Bharat Tidke ◽  
Rupa Mehta
Author(s):  
Snigdha Sen ◽  
Sonali Agarwal ◽  
Pavan Chakraborty ◽  
Krishna Pratap Singh

Author(s):  
Allison Schnable ◽  
Anthony J. DeMattee ◽  
Rachel Sullivan Robinson ◽  
Jennifer N. Brass ◽  
Wesley Longhofer

AbstractThis article presents a new strategy for reviewing large, multidisciplinary academic literatures: a multi-method comprehensive review (MCR). We present this approach and demonstrate its use by the NGO Knowledge Collective, which aims to aggregate knowledge on NGOs in international development. We explain the process by which scholars can identify, analyze, and synthesize a population of hundreds or thousands of articles. MCRs facilitate cross-disciplinary synthesis, systematically identify gaps in a literature, and can create data for further scholarly use. The main drawback is the significant resources needed to manage the volume of text to review, although such obstacles may be mitigated through advances in “big data” methodologies over time.


2019 ◽  
Vol 16 (8) ◽  
pp. 3587-3590
Author(s):  
Raheem Mafas ◽  
Manoj Jayabalan

In this era, big data is the most common buzzword across different industries due to its capabilities of collecting, processing, storing and analysing data. The advancement of the E-Commerce paved the way for merchants and customers to meet online to satisfy their requirements by exchanging goods and services at a reasonable cost. The challenges and opportunities for big data on the emphasis of data privacy and security is a widely discussed topic among businesses especially E-Commerce merchants. There are several reviews available on emphasizing big data opportunities and challenges with regard to privacy and security. However, a comprehensive review on E-Commerce highlighting thematically on the tools and technologies is not given enough consideration. Therefore, the purpose of this study is to review the state-of-the-art technologies towards privacy and security in the E-Commerce platforms. The identified cryptographic technologies were also discussed with the rational standpoint to understand the viability to apply in the E-Commerce operations. The study concludes with an enlightening path from which the E-Commerce merchants can be vigilant on data privacy and security in future.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dingyi Xiang ◽  
Wei Cai

Health big data has already been the most important big data for its serious privacy disclosure concerns and huge potential value of secondary use. Measurements must be taken to balance and compromise both the two serious challenges. One holistic solution or strategy is regarded as the preferred direction, by which the risk of reidentification from records should be kept as low as possible and data be shared with the principle of minimum necessary. In this article, we present a comprehensive review about privacy protection of health data from four aspects: health data, related regulations, three strategies for data sharing, and three types of methods with progressive levels. Finally, we summarize this review and identify future research directions.


Author(s):  
Katerina Stylianou ◽  
Loukas Dimitriou ◽  
Mohamed Abdel-Aty

2019 ◽  
Vol 11 (20) ◽  
pp. 5641 ◽  
Author(s):  
Yamilé Pérez Guilarte ◽  
Daniel Barreiro Quintáns

The concern about the production of international standards to measure the sustainability of tourism is present today, especially the discourse on the introduction of new sources. This article aims to survey and describe the main approaches and methodologies to use big data to measure tourism sustainability. Successful cases are addressed by explaining the main opportunities and challenges for the creation of official tourist statistics. A comprehensive review of publications regarding this field was carried out by applying the systematic literature review technique. This contributes a knowledge base to destination management organisations to encourage the implementation of official tourism statistics systems using big data.


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