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Journal articles on the topic 'Mobile analytics big data pl'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Hui, Pan, Yong Li, Jorg Ott, Steve Uhlig, Bo Han, and Kun Tan. "Mobile Big Data for Urban Analytics." IEEE Communications Magazine 56, no. 11 (2018): 12. http://dx.doi.org/10.1109/mcom.2018.8539013.

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Asri, Hiba, Hajar Mousannif, and Hassan Al Moatassime. "Big Data Analytics in Healthcare." International Journal of Distributed Systems and Technologies 10, no. 4 (2019): 45–58. http://dx.doi.org/10.4018/ijdst.2019100104.

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Sensors and mobile phones shine in the Big Data area due to their capabilities to retrieve a huge amount of real-time data; which was not possible previously. In the specific field of healthcare, we can now collect data related to human behavior and lifestyle for better understanding. This pushed us to benefit from such technologies for early miscarriage prediction. This research study proposes to combine the use of Big Data analytics and data mining models applied to smartphones real-time generated data. A K-means data mining algorithm is used for clustering the dataset and results are transm

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He, Ying, Fei Richard Yu, Nan Zhao, Hongxi Yin, Haipeng Yao, and Robert C. Qiu. "Big Data Analytics in Mobile Cellular Networks." IEEE Access 4 (2016): 1985–96. http://dx.doi.org/10.1109/access.2016.2540520.

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Shankar, Venky. "Big Data and Analytics in Retailing." NIM Marketing Intelligence Review 11, no. 1 (2019): 36–40. http://dx.doi.org/10.2478/nimmir-2019-0006.

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AbstractBig data are taking center stage for decision-making in many retail organizations. Customer data on attitudes and behavior across channels, touchpoints, devices and platforms are often readily available and constantly recorded. These data are integrated from multiple sources and stored or warehoused, often in a cloud-based environment. Statistical, econometric and data science models are developed for enabling appropriate decisions. Computer algorithms and programs are created for these models. Machine learning based models, are particularly useful for learning from the data and making

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Chen, Tingting, Fan Wu, Tony T. Luo, Mea Wang, and Qirong Ho. "Big Data Management and Analytics for Mobile Crowd Sensing." Mobile Information Systems 2016 (2016): 1–2. http://dx.doi.org/10.1155/2016/8731802.

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Garcia, Antonio J., Matias Toril, Pablo Oliver, Salvador Luna-Ramirez, and Rafael Garcia. "Big Data Analytics for Automated QoE Management in Mobile Networks." IEEE Communications Magazine 57, no. 8 (2019): 91–97. http://dx.doi.org/10.1109/mcom.2019.1800374.

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Alsheikh, Mohammad Abu, Dusit Niyato, Shaowei Lin, Hwee-pink Tan, and Zhu Han. "Mobile big data analytics using deep learning and apache spark." IEEE Network 30, no. 3 (2016): 22–29. http://dx.doi.org/10.1109/mnet.2016.7474340.

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Ma, Xiao, Zie Wang, Sheng Zhou, Haoyu Wen, and Yin Zhang. "Intelligent Healthcare Systems Assisted by Data Analytics and Mobile Computing." Wireless Communications and Mobile Computing 2018 (July 3, 2018): 1–16. http://dx.doi.org/10.1155/2018/3928080.

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It is entering an era of big data, which facilitated great improvement in various sectors. Particularly, assisted by wireless communications and mobile computing, mobile devices have emerged with a great potential to renovate the healthcare industry. Although the advanced techniques will make it possible to understand what is happening in our body more deeply, it is extremely difficult to handle and process the big health data anytime and anywhere. Therefore, data analytics and mobile computing are significant for the healthcare systems to meet many technical challenges and problems that need

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Borodo, Salisu Musa, Siti Mariyam Shamsuddin, and Shafaatunnur Hasan. "Big Data Platforms and Techniques." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 1 (2016): 191. http://dx.doi.org/10.11591/ijeecs.v1.i1.pp191-200.

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Data is growing at unprecedented rate and has led to huge volume generated; the data sources include mobile, internet and sensors. This voluminous data is generated and updated at high velocity by batch and streaming platforms. This data is also varied along structured and unstructured types. This volume, velocity and variety of data led to the term big data. Big data has been premised to contain untapped knowledge, its exploration and exploitation is termed big data analytics. This literature reviewed platforms such as batch processing, real time processing and interactive analytics used in b

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Vaidya, Pranav Vilas, Janaki Meena M, and Syed Ibrahim Sp. "CLOUD-BASED DATA ANALYTICS FRAMEWORK FOR MOBILE APP EVENT ANALYSIS." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 207. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19639.

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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

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Guo, Liang, Ruchi Sharma, Lei Yin, Ruodan Lu, and Ke Rong. "Automated competitor analysis using big data analytics." Business Process Management Journal 23, no. 3 (2017): 735–62. http://dx.doi.org/10.1108/bpmj-05-2015-0065.

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Purpose Competitor analysis is a key component in operations management. Most business decisions are rooted in the analysis of rival products inferred from market structure. Relative to more traditional competitor analysis methods, the purpose of this paper is to provide operations managers with an innovative tool to monitor a firm’s market position and competitors in real time at higher resolution and lower cost than more traditional competitor analysis methods. Design/methodology/approach The authors combine the techniques of Web Crawler, Natural Language Processing and Machine Learning algo

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Visuwasam, L. Maria Michael, and D. Paul Raj. "NMA: integrating big data into a novel mobile application using knowledge extraction for big data analytics." Cluster Computing 22, S6 (2018): 14287–98. http://dx.doi.org/10.1007/s10586-018-2287-8.

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13

khan, Z. Faizal, and Sultan Refa Alotaibi. "Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective." Journal of Healthcare Engineering 2020 (September 1, 2020): 1–15. http://dx.doi.org/10.1155/2020/8894694.

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Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data analytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as electronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively unorganized have been used in the modern medical research. This is an important reaso

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Saldžiūnas, Kęstutis, and Rimvydas Skyrius. "THE CHALLENGES OF BIG DATA ANALYTICS IN THE MOBILE COMMUNICATIONS SECTOR." Ekonomika 96, no. 2 (2017): 110–21. http://dx.doi.org/10.15388/ekon.2017.2.11004.

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The activities of the MNO (Mobile Network Operator) feature rapid development and business model innovations; one of their principal results is the communications infrastructure that is vital for economic growth. This dynamic and changing mode of operation (modus operandi) introduces high requirements for business decisions and overall informing to maintain competitiveness. One of the principal success factors in MNO activities is the application of contemporary information technologies, in particular technologies of business intelligence and analytics. The activities of MNO create large data

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Visuwasam, L. Maria Michael, and D. Paul Raj. "A distributed intelligent mobile application for analyzing travel big data analytics." Peer-to-Peer Networking and Applications 13, no. 6 (2019): 2036–52. http://dx.doi.org/10.1007/s12083-019-00799-z.

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Ribeiro de Almeida, Damião, Cláudio de Souza Baptista, Fabio Gomes de Andrade, and Amilcar Soares. "A Survey on Big Data for Trajectory Analytics." ISPRS International Journal of Geo-Information 9, no. 2 (2020): 88. http://dx.doi.org/10.3390/ijgi9020088.

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Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data

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17

Schatz, Bruce R. "National Surveys of Population Health: Big Data Analytics for Mobile Health Monitors." Big Data 3, no. 4 (2015): 219–29. http://dx.doi.org/10.1089/big.2015.0021.

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Akinnagbe, Akindele, K. Dharini Amitha Peiris, and Oluyemi Akinloye. "Prospects of Big Data Analytics in Africa Healthcare System." Global Journal of Health Science 10, no. 6 (2018): 114. http://dx.doi.org/10.5539/gjhs.v10n6p114.

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Big data is having a positive impact in almost every sphere of life, such as in military intelligence, space science, aviation, banking, and health. Big data is a growing force in healthcare. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa. Healthcare systems in Africa are, in relative terms, behind the rest of the world. Platforms and technologies used to amass big data such as the Internet and mobile phones are alread

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Gottumukkala, Prasanthi, and Srinivasa Rao G. "Fault Detection in Mobile Communication Networks Using Data Mining Techniques with Big Data Analytics." International Journal on Cybernetics & Informatics 5, no. 4 (2016): 81–89. http://dx.doi.org/10.5121/ijci.2016.5410.

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Stojanović, Natalija, and Dragan Stojanović. "BIG MOBILITY DATA ANALYTICS FOR TRAFFIC MONITORING AND CONTROL." Facta Universitatis, Series: Automatic Control and Robotics 19, no. 2 (2020): 087. http://dx.doi.org/10.22190/fuacr2002087s.

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With the overpopulation of large cities, the problems with citizens’ mobility, transport inefficiency, traffic congestions and environmental pollution caused by the heavy traffic require advanced ITS solutions to be overcome. Recent advances and wide proliferation of mobile and Internet of Things (IoT) devices, carried by people, built in vehicles and integrated in a road infrastructure, enable collection of large scale data related to mobility and traffic in smart cities, still with a limited use in real world applications. In this paper, we propose the traffic monitoring, control and adaptat

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21

Mylavathi, G. A., N. M. Mallika, and K. Mohanraj. "Survey of Security and Privacy Issues in Big Data Analytics." Asian Journal of Computer Science and Technology 8, S1 (2019): 33–35. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1972.

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Due to the reasons such as the rapid growth and spread of network services, mobile devices, and online users on the Internet leading to a remarkable increase in the amount of data. Almost each trade is making an attempt to address this large information. Big data phenomenon has begun to gain importance. However, it’s not solely terribly tough to store massive information and analyses them with ancient applications, however conjointly it’s difficult privacy and security issues. For this reason, this paper discusses the massive information, its scheme, considerations on massive information and p

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Limpeeticharoenchot, Santisook, Nagul Cooharojananone, Thira Chanvanakul, Nuengwong Tuaycharoen, and Kanokwan Atchariyachanvanich. "Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 18 (2020): 87. http://dx.doi.org/10.3991/ijim.v14i18.16295.

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A Big Data maturity model (BDMM) is one of the key tools for Big Data assessment and monitoring, and a guideline for maximizing the usage and opportunity of Big Data in organizations. The development of a BDMM for SMEs is a new concept and is challenging in terms of development, application, and adoption. This article aims to create the novel online adaptive BDMM via responsive web application for SMEs. We develop the BDMM API and a responsive web application for easy access via mobile phone. We developed a model by analyzing the factors impacting the success of implementing Big Data Analytics

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23

Sangeetha, S., and G. Deepalakshmi. "EMERGING TRENDS IN PERVASIVE COMPUTING ARCHITECTURE FOR BIG DATA ANALYTICS IN MOBILE DEVICE." International Journal of Engineering Applied Sciences and Technology 5, no. 1 (2020): 586–90. http://dx.doi.org/10.33564/ijeast.2020.v05i01.102.

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Wu, Jing, He Li, Ling Liu, and Haichao Zheng. "Adoption of big data and analytics in mobile healthcare market: An economic perspective." Electronic Commerce Research and Applications 22 (March 2017): 24–41. http://dx.doi.org/10.1016/j.elerap.2017.02.002.

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S, Smys. "SURVEY ON ACCURACY OF PREDICTIVE BIG DATA ANALYTICS IN HEALTHCARE." December 2019 01, no. 02 (2019): 77–86. http://dx.doi.org/10.36548/ijtdw.2019.2.003.

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The failures in the most of research area, identified that the lack of details about the actionable and the valuable data that conceived actual solutions were the core of the crisis, this was very true in case of the health care industry where even the early diagnoses of a chronic disease could not save a person’s life. This because of the impossibility in the prediction of the individual’s outcomes in the entire population. The evolving new technologies have changed this scenario leveraging the mobile devices and the internet services such as the sensor network and the smart monitors, enhanci

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S, Smys. "SURVEY ON ACCURACY OF PREDICTIVE BIG DATA ANALYTICS IN HEALTHCARE." December 2019 01, no. 02 (2019): 77–86. http://dx.doi.org/10.36548/jitdw.2019.2.003.

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The failures in the most of research area, identified that the lack of details about the actionable and the valuable data that conceived actual solutions were the core of the crisis, this was very true in case of the health care industry where even the early diagnoses of a chronic disease could not save a person’s life. This because of the impossibility in the prediction of the individual’s outcomes in the entire population. The evolving new technologies have changed this scenario leveraging the mobile devices and the internet services such as the sensor network and the smart monitors, enhanci

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27

SELVI, CHEMMALAR, and LAKSHMI PRIYA. "SMSS : Does Social,Mobile,Spatial and Sensor data have high impact on big data analytics." International Journal of Intelligent Enterprise 6, no. 3 (2019): 1. http://dx.doi.org/10.1504/ijie.2019.10023849.

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Govardanan, Chemmalar Selvi, and Lakshmi Priya Gopalsamy Gnanapandithan. "SMSS: does social, mobile, spatial and sensor data have high impact on big data analytics." International Journal of Intelligent Enterprise 7, no. 1/2/3 (2020): 215. http://dx.doi.org/10.1504/ijie.2020.104657.

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Qin, Siyang, Jie Man, Xuzhao Wang, Can Li, Honghui Dong, and Xinquan Ge. "Applying Big Data Analytics to Monitor Tourist Flow for the Scenic Area Operation Management." Discrete Dynamics in Nature and Society 2019 (January 1, 2019): 1–11. http://dx.doi.org/10.1155/2019/8239047.

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Considering the rapid development of the tourist leisure industry and the surge of tourist quantity, insufficient information regarding tourists has placed tremendous pressure on traffic in scenic areas. In this paper, the author uses the Big Data technology and Call Detail Record (CDR) data with the mobile phone real-time location information to monitor the tourist flow and analyse the travel behaviour of tourists in scenic areas. By collecting CDR data and implementing a modelling analysis of the data to simultaneously reflect the distribution of tourist hot spots in Beijing, tourist locatio

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Wang, Yibo, Mingming Wang, and Wei Xu. "A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/8263704.

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Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list

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Shorfuzzaman, Mohammad, M. Shamim Hossain, Amril Nazir, Ghulam Muhammad, and Atif Alamri. "Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment." Computers in Human Behavior 92 (March 2019): 578–88. http://dx.doi.org/10.1016/j.chb.2018.07.002.

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32

López, Lorena Herrera. "A Closer Look at Direct Carrier Billing." International Journal of Online Marketing 10, no. 4 (2020): 18–40. http://dx.doi.org/10.4018/ijom.2020100102.

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The impulse to digitalization by telecom operators requires the commercialization of over-the-top services (OTT) based on the fine understanding and prediction of customer behaviour through pattern recognition involving big data, resulting in an essential part of web analytics and digital marketing. The objective of this research is to analyse factors influencing the purchase and use of a mobile game commercialized by a mobile network operator (MNO), through different digital marketing channels and using direct carrier billing (DCB) as payment channel. The novelty contribution of this study is

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Kchaou, Hamdi, Zied Kechaou, and Adel M. Alimi. "Towards an Offloading Framework based on Big Data Analytics in Mobile Cloud Computing Environments." Procedia Computer Science 53 (2015): 292–97. http://dx.doi.org/10.1016/j.procs.2015.07.306.

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Istepanian, Robert S. H., and Turki Al-Anzi. "m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics." Methods 151 (December 2018): 34–40. http://dx.doi.org/10.1016/j.ymeth.2018.05.015.

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Pustokhin, Denis A., Irina V. Pustokhina, Poonam Rani, et al. "Optimal deep learning approaches and healthcare big data analytics for mobile networks toward 5G." Computers & Electrical Engineering 95 (October 2021): 107376. http://dx.doi.org/10.1016/j.compeleceng.2021.107376.

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36

Rangra, Abhilasha, and Vivek Kumar Sehgal. "On Performance of Big Data Storage on Cloud Mechanics in Mobile Digital Healthcare." International Journal of E-Health and Medical Communications 12, no. 5 (2021): 36–49. http://dx.doi.org/10.4018/ijehmc.20210901.oa3.

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In recent years, the concept of cloud computing and big data analysis are considered as two major problems. It empowers the resources of computing to be maintained as the service of information technology with high effectiveness and efficiency. In the present scenario, big data is treated as one of the issues that the experts are trying to solve and finding ways to tackle the problem of handling big data analytics, how it could be managed with the technology of cloud computing and handled in the recent systems, and apart from this, the most significant issue is how to have perfect safety of bi

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Atasoy, Eda, Harun Bozna, Abdulvahap Sönmez, Ayşe Aydın Akkurt, Gamze Tuna Büyükköse, and Mehmet Fırat. "Active learning analytics in mobile: visions from PhD students." Asian Association of Open Universities Journal 15, no. 2 (2020): 145–66. http://dx.doi.org/10.1108/aaouj-11-2019-0055.

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PurposeThis study aims to investigate the futuristic visions of PhD students at Distance Education department of Anadolu University on the use of learning analytics (LA) and mobile technologies together.Design/methodology/approachThis qualitative research study, designed in the single cross-section model, aimed to reveal futuristic visions of PhD students on the use of LA in mobile learning. In this respect, SCAMPER method, which is also known as a focused brainstorming technique, was used to collect data.FindingsThe findings of the study revealed that the use of LA in mobile can solve everyda

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Parwez, Md Salik, Danda B. Rawat, and Moses Garuba. "Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network." IEEE Transactions on Industrial Informatics 13, no. 4 (2017): 2058–65. http://dx.doi.org/10.1109/tii.2017.2650206.

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Moussa, Sherin. "A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures." MATEC Web of Conferences 76 (2016): 04023. http://dx.doi.org/10.1051/matecconf/20167604023.

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Jeon, Gwanggil, Awais Ahmad, Salvatore Cuomo, and Wei Wu. "Special issue on bio-medical signal processing for smarter mobile healthcare using big data analytics." Journal of Ambient Intelligence and Humanized Computing 10, no. 10 (2019): 3739–45. http://dx.doi.org/10.1007/s12652-019-01425-9.

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Kumar, Akshi, and Abhilasha Sharma. "Ontology Driven Social Big Data Analytics for Fog enabled Sentic-Social Governance." Scalable Computing: Practice and Experience 20, no. 2 (2019): 223–36. http://dx.doi.org/10.12694/scpe.v20i2.1513.

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Conventional e-government has many practical infrastructure development and implementation challenges. The recent surge of SMAC (Social media, Mobile, Analytics, Cloud) technologies re-defines the e-governance ecosystem. Cloud-based e-governance has numerous operational challenges which range from development to implementation. Moreover, the contemplation and vocalization of public opinion about any government initiative is quintessential to be cognizant of how citizens perceives and get benefitted from cloud/fog enabled governance. This research puts forward a semantic knowledge model for inv

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Chen, Chi-Mai, Hong-Wei Jyan, Shih-Chieh Chien, et al. "Containing COVID-19 Among 627,386 Persons in Contact With the Diamond Princess Cruise Ship Passengers Who Disembarked in Taiwan: Big Data Analytics." Journal of Medical Internet Research 22, no. 5 (2020): e19540. http://dx.doi.org/10.2196/19540.

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Background Low infection and case-fatality rates have been thus far observed in Taiwan. One of the reasons for this major success is better use of big data analytics in efficient contact tracing and management and surveillance of those who require quarantine and isolation. Objective We present here a unique application of big data analytics among Taiwanese people who had contact with more than 3000 passengers that disembarked at Keelung harbor in Taiwan for a 1-day tour on January 31, 2020, 5 days before the outbreak of coronavirus disease (COVID-19) on the Diamond Princess cruise ship on Febr

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Iqbal, Muhammad Munwar, Muhammad Ali, Mai Alfawair, et al. "Augmenting High-Performance Mobile Cloud Computations for Big Data in AMBER." Wireless Communications and Mobile Computing 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/4796535.

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Big data is an inspirational area of research that involves best practices used in the industry and academia. Challenging and complex systems are the core requirements for the data collation and analysis of big data. Data analysis approaches and algorithms development are the necessary and essential components of the big data analytics. Big data and high-performance computing emergent nature help to solve complex and challenging problems. High-Performance Mobile Cloud Computing (HPMCC) technology contributes to the execution of the intensive computational application at any location independen

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Ali, Twana Saeed, and Tugberk Kaya. "Big Data Analytics For Organizations: Challenges and Opportunities and Its Effect on International Business Education." Kurdistan Journal of Applied Research 4, no. 2 (2019): 137–50. http://dx.doi.org/10.24017/science.2019.2.13.

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Big Data refers to large volumes of information. This information varies from pictures, videos, texts, audios and other heterogeneous data. In recent years, the amount of such big data has exceeded the capacity of online or cloud storage systems. The amount of data collected yearly has doubled in the past years and the concern for the volume of this data has reached its Exabyte yearly range. This paper focuses on the major issues and opportunities as well as big data storage with the aid of academic tools and researches conducted earlier by scholars for big data analysis. Modern learning envir

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Feusner, Jamie D., Reza Mohideen, Stephen Smith, et al. "Semantic Linkages of Obsessions From an International Obsessive-Compulsive Disorder Mobile App Data Set: Big Data Analytics Study." Journal of Medical Internet Research 23, no. 6 (2021): e25482. http://dx.doi.org/10.2196/25482.

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Background Obsessive-compulsive disorder (OCD) is characterized by recurrent intrusive thoughts, urges, or images (obsessions) and repetitive physical or mental behaviors (compulsions). Previous factor analytic and clustering studies suggest the presence of three or four subtypes of OCD symptoms. However, these studies have relied on predefined symptom checklists, which are limited in breadth and may be biased toward researchers’ previous conceptualizations of OCD. Objective In this study, we examine a large data set of freely reported obsession symptoms obtained from an OCD mobile app as an a

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Florence, S., C. Shyamala Kumari, and L. LeemaPriyadharshini. "Smart health monitoring system based on internet of things with big data analytics and wireless networks." International Journal of Engineering & Technology 7, no. 1.7 (2018): 109. http://dx.doi.org/10.14419/ijet.v7i1.7.9586.

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The use of mobile application technologies and smart devices in the health sector has improved a lot. In this sense health monitoring has evolved using Internet of Things. Nowadays the food habits are changed because of our machine life. This paper mainly focuses on the problems occurring related to health because of food habits. This paper gives the idea for solving this problem by collecting the attributes from the human body. The attributes like cholesterol, calories, glucose, pulse, weight, Body Mass Index, water level, temperature are to be collected from the person by fixing the wearable

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E. Youssef, Ahmed. "A Framework for Secure Healthcare Systems Based on Big Data Analytics in Mobile Cloud Computing Environments." International Journal of Ambient Systems and Applications 2, no. 2 (2014): 1–11. http://dx.doi.org/10.5121/ijasa.2014.2201.

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48

Brauer, Claudia, and Andreas Wimmer. "Der Mobile Analyst: Ein neues Berufsbild im Bereich von Business Analytics als Ausprägungsform von Big Data." HMD Praxis der Wirtschaftsinformatik 53, no. 3 (2016): 357–70. http://dx.doi.org/10.1365/s40702-016-0222-0.

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Rehman, Amjad, Khalid Haseeb, Tanzila Saba, Jaime Lloret, and Usman Tariq. "Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things." Electronics 10, no. 11 (2021): 1273. http://dx.doi.org/10.3390/electronics10111273.

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The Internet of Medical Things (IoMT) has shown incredible development with the growth of medical systems using wireless information technologies. Medical devices are biosensors that can integrate with physical things to make smarter healthcare applications that are collaborated on the Internet. In recent decades, many applications have been designed to monitor the physical health of patients and support expert teams for appropriate treatment. The medical devices are attached to patients’ bodies and connected with a cloud computing system for obtaining and analyzing healthcare data. However, s

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Beshley, Mykola, Natalia Kryvinska, Oleg Yaremko, and Halyna Beshley. "A Self-Optimizing Technique Based on Vertical Handover for Load Balancing in Heterogeneous Wireless Networks Using Big Data Analytics." Applied Sciences 11, no. 11 (2021): 4737. http://dx.doi.org/10.3390/app11114737.

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With the heterogeneity and collaboration of many wireless operators (2G/3G/4G/5G/Wi-Fi), the priority is to effectively manage shared radio resources and ensure transparent user movement, which includes mechanisms such as mobility support, handover, quality of service (QoS), security and pricing. This requires considering the transition from the current mobile network architecture to a new paradigm based on collecting and storing information in big data for further analysis and decision making. For this reason, the management of big data analytics-driven networks in a cloud environment is an u

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