Journal ArticleDOI
The state of the art and taxonomy of big data analytics: view from new big data framework
Azlinah Mohamed,Maryam Khanian Najafabadi,Yap Bee Wah,Ezzatul Akmal Kamaru Zaman,Ruhaila Maskat +4 more
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TLDR
A review of the literature that analyzes the use of big data tools and big data analytics techniques in areas like health and medical care, social networking and internet, government and public sector, natural resource management, economic and business sector is presented.Abstract:
Big data has become a significant research area due to the birth of enormous data generated from various sources like social media, internet of things and multimedia applications. Big data has played critical role in many decision makings and forecasting domains such as recommendation systems, business analysis, healthcare, web display advertising, clinicians, transportation, fraud detection and tourism marketing. The rapid development of various big data tools such as Hadoop, Storm, Spark, Flink, Kafka and Pig in research and industrial communities has allowed the huge number of data to be distributed, communicated and processed. Big data applications use big data analytics techniques to efficiently analyze large amounts of data. However, choosing the suitable big data tools based on batch and stream data processing and analytics techniques for development a big data system are difficult due to the challenges in processing and applying big data. Practitioners and researchers who are developing big data systems have inadequate information about the current technology and requirement concerning the big data platform. Hence, the strengths and weaknesses of big data technologies and effective solutions for Big Data challenges are needed to be discussed. Hence, due to that, this paper presents a review of the literature that analyzes the use of big data tools and big data analytics techniques in areas like health and medical care, social networking and internet, government and public sector, natural resource management, economic and business sector. The goals of this paper are to (1) understand the trend of big data-related research and current frames of big data technologies; (2) identify trends in the use or research of big data tools based on batch and stream processing and big data analytics techniques; (3) assist and provide new researchers and practitioners to place new research activity in this domain appropriately. The findings of this study will provide insights and knowledge on the existing big data platforms and their application domains, the advantages and disadvantages of big data tools, big data analytics techniques and their use, and new research opportunities in future development of big data systems.read more
Citations
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Journal ArticleDOI
Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing.
TL;DR: A review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases and tracing contacts of infected persons focuses on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that could be adopted in the current pandemic.
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Directional correlation coefficient measures for Pythagorean fuzzy sets: their applications to medical diagnosis and cluster analysis
TL;DR: In this paper, some novel directional correlation coefficients are put forward to compute the relationship between two Pythagorean fuzzy sets by taking four parameters of the PFSs into consideration, which are the membership degree, non-membership degree, strength of commitment, and direction of commitment.
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Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications
TL;DR: The reviewed areas of big data suggest that good management and manipulation of the large data sets using the techniques and tools ofbig data can deliver actionable insights that create business values.
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Hybrid meta-heuristic algorithms for a supply chain network considering different carbon emission regulations using big data characteristics
TL;DR: A Mixed Integer Non-Linear Program (MINLP) model in the multi-products, multi-levels, and multi-periods SCN is formulated based on the minimization of the total costs and the related cost of carbon emissions and shows that H-2 is of higher efficiency.
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Promoting a revamped CRM through Internet of Things and Big Data: an AHP-based evaluation
TL;DR: An analytic hierarchy planning framework to establish criteria weights and to develop a general self-assessment model for determining the most important factors influencing the IoT and BD investment in CRM is developed.
References
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Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
TL;DR: This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies currently adopt to deal with the Big Data problems.
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Urban planning and building smart cities based on the Internet of Things using Big Data analytics
TL;DR: A combined IoT-based system for smart city development and urban planning using Big Data analytics, consisting of various types of sensor deployment, including smart home sensors, vehicular networking, weather and water sensors, smart parking sensors, and surveillance objects is proposed.
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A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system
Gunasekaran Manogaran,R. Varatharajan,Daphne Lopez,Priyan Malarvizhi Kumar,Revathi Sundarasekar,Chandu Thota +5 more
TL;DR: A new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications and uses MapReduce based prediction model to predict the heart diseases is proposed.
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On the use of MapReduce for imbalanced big data using Random Forest
TL;DR: This work analyzes the performance of several techniques used to deal with imbalanced datasets in the big data scenario using the Random Forest classifier, and shows that there is not an approach to imbalanced big data classification that outperforms the others for all the data considered when using Random Forest.
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kNN-IS
TL;DR: This work provides a new solution to perform an exact k-nearest neighbor classification based on Spark that takes advantage of its in-memory operations to classify big amounts of unseen cases against a big training dataset.