Journal articles: 'Big Data analytics applications'

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K., Sangeetha, Poongothai T., Anguraj S., and Nithya Kalyani S. "An Overview of Applications of Big Data Analytics." Bonfring International Journal of Software Engineering and Soft Computing 8, no. 1 (March 30, 2018): 06–11. http://dx.doi.org/10.9756/bijsesc.8381.

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Bi, Zhuming, and David Cochran. "Big data analytics with applications." Journal of Management Analytics 1, no. 4 (October 2, 2014): 249–65. http://dx.doi.org/10.1080/23270012.2014.992985.

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Memon, Mashooque A., Safeeullah Soomro, Awais K. Jumani, and Muneer A. Kartio. "Big Data Analytics and Its Applications." Annals of Emerging Technologies in Computing 1, no. 1 (October 1, 2017): 45–54. http://dx.doi.org/10.33166/aetic.2017.01.006.

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The term, Big Data, has been authored to refer to the extensive heave of data that can't be managed by traditional data handling methods or techniques. The field of Big Data plays an indispensable role in various fields, such as agriculture, banking, data mining, education, chemistry, finance, cloud computing, marketing, health care stocks. Big data analytics is the method for looking at big data to reveal hidden patterns, incomprehensible relationship and other important data that can be utilize to resolve on enhanced decisions. There has been a perpetually expanding interest for big data because of its fast development and since it covers different areas of applications. Apache Hadoop open source technology created in Java and keeps running on Linux working framework was used. The primary commitment of this exploration is to display an effective and free solution for big data application in a distributed environment, with its advantages and indicating its easy use. Later on, there emerge to be a required for an analytical review of new developments in the big data technology. Healthcare is one of the best concerns of the world. Big data in healthcare imply to electronic health data sets that are identified with patient healthcare and prosperity. Data in the healthcare area is developing past managing limit of the healthcare associations and is relied upon to increment fundamentally in the coming years.

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Das, Nivedita, Leena Das, Siddharth Swarup Rautaray, and Manjusha Pandey. "Big Data Analytics for Medical Applications." International Journal of Modern Education and Computer Science 10, no. 2 (February 8, 2018): 35–42. http://dx.doi.org/10.5815/ijmecs.2018.02.04.

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Al-Sai, Zaher Ali, Mohd Heikal Husin, Sharifah Mashita Syed-Mohamad, Rasha Moh’d Sadeq Abdin, Nour Damer, Laith Abualigah, and Amir H. Gandomi. "Explore Big Data Analytics Applications and Opportunities: A Review." Big Data and Cognitive Computing 6, no. 4 (December 14, 2022): 157. http://dx.doi.org/10.3390/bdcc6040157.

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Big data applications and analytics are vital in proposing ultimate strategic decisions. The existing literature emphasizes that big data applications and analytics can empower those who apply Big Data Analytics during the COVID-19 pandemic. This paper reviews the existing literature specializing in big data applications pre and peri-COVID-19. A comparison between Pre and Peri of the pandemic for using Big Data applications is presented. The comparison is expanded to four highly recognized industry fields: Healthcare, Education, Transportation, and Banking. A discussion on the effectiveness of the four major types of data analytics across the mentioned industries is highlighted. Hence, this paper provides an illustrative description of the importance of big data applications in the era of COVID-19, as well as aligning the applications to their relevant big data analytics models. This review paper concludes that applying the ultimate big data applications and their associated data analytics models can harness the significant limitations faced by organizations during one of the most fateful pandemics worldwide. Future work will conduct a systematic literature review and a comparative analysis of the existing Big Data Systems and models. Moreover, future work will investigate the critical challenges of Big Data Analytics and applications during the COVID-19 pandemic.

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Ravada, Siva. "Big data spatial analytics for enterprise applications." SIGSPATIAL Special 6, no. 2 (March 10, 2015): 34–41. http://dx.doi.org/10.1145/2744700.2744705.

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Islam, Akinul. "Applications of Real-Time Big Data Analytics." International Journal of Computer Applications 144, no. 5 (June 17, 2016): 1–5. http://dx.doi.org/10.5120/ijca2016910208.

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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 (May 8, 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 already in use in Africa, thereby making big data applications to be emerging. Hence, the key research question we address is whether big data applications can improve healthcare in Africa especially during epidemics and through the public health system. In this study, a literature review is carried out, firstly to present cases of big data applications in healthcare in Africa, and secondly, to explore potential ethical challenges of such applications. This review will provide an update on the application of big data in the health sector in Africa that can be useful for future researchers and health care practitioners in Africa.

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Woodard, Joshua. "Big data and Ag-Analytics." Agricultural Finance Review 76, no. 1 (May 3, 2016): 15–26. http://dx.doi.org/10.1108/afr-03-2016-0018.

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Purpose – The purpose of this paper is to provide a brief and necessarily partial overview of the design, motivation, and use of the Ag-Analytics platform (ag-analytics.org), focussing on integration and warehousing of publicly available research data for broad communities of researchers, including those in the area of agricultural finance. Design/methodology/approach – The paper walks the reader through an overview of the layout and utilization of the Ag-Analytics platform, including a few example applications of some of the tools and web API’s. Findings – Much of the data researchers routinely use in agricultural and environmental finance and related fields are often – strictly speaking – publicly available; however the form in which they are distributed leads to great inefficiencies in data sourcing and processing which can be greatly improved. The goal of the Ag-Analytics open data/open source platform is to help researchers centralize and share in such efforts. Development of systems for disseminating, documenting, and automating the processing of such data can lead to more transparency in research, better routes for validation, and a more robust research community. Practical implications – Some of the tools and methods are discussed, as well as practical issues in data sourcing and automation for research. A few high level introductory examples and applications are illustrated. Originality/value – Development and adoption of such systems and data resources remains seriously lacking in social science research, particularly in the economics, natural resource, environmental, and agricultural finance spheres. This brief provides an overview of one such system which should be of value to researchers in this field and many others.

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Hassani, Hossein, Christina Beneki, Stephan Unger, Maedeh Taj Mazinani, and Mohammad Reza Yeganegi. "Text Mining in Big Data Analytics." Big Data and Cognitive Computing 4, no. 1 (January 16, 2020): 1. http://dx.doi.org/10.3390/bdcc4010001.

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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G, Aravind, Varun K, and Manjunath C. R. Soumya K. N. "Application of Big Data Analytics with Evidence Based Medicine." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 440–44. http://dx.doi.org/10.31142/ijtsrd12979.

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Venkatram, Kari, and Mary A. Geetha. "Review on Big Data & Analytics – Concepts, Philosophy, Process and Applications." Cybernetics and Information Technologies 17, no. 2 (June 1, 2017): 3–27. http://dx.doi.org/10.1515/cait-2017-0013.

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Abstract Big Data analytics has been the main focus in all the industries today. It is not overstating that if an enterprise is not using Big Data analytics, it will be a stray and incompetent in their businesses against their Big Data enabled competitors. Big Data analytics enables business to take proactive measure and create a competitive edge in their industry by highlighting the business insights from the past data and trends. The main aim of this review article is to quickly view the cutting-edge and state of art work being done in Big Data analytics area by different industries. Since there is an overwhelming interest from many of the academicians, researchers and practitioners, this review would quickly refresh and emphasize on how Big Data analytics can be adopted with available technologies, frameworks, methods and models to exploit the value of Big Data analytics.

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Wang, Lidong, and Cheryl Ann Alexander. "Big Data Analytics in Healthcare Systems." International Journal of Mathematical, Engineering and Management Sciences 4, no. 1 (February 1, 2019): 17–26. http://dx.doi.org/10.33889/ijmems.2019.4.1-002.

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Big Data analytics can improve patient outcomes, advance and personalize care, improve provider relationships with patients, and reduce medical spending. This paper introduces healthcare data, big data in healthcare systems, and applications and advantages of Big Data analytics in healthcare. We also present the technological progress of big data in healthcare, such as cloud computing and stream processing. Challenges of Big Data analytics in healthcare systems are also discussed.

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Kurniawan, Candra. "A Survey on Big Data Analytics Model." ITEJ (Information Technology Engineering Journals) 4, no. 1 (July 22, 2019): 1–13. http://dx.doi.org/10.24235/itej.v4i1.46.

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Topic about big data analytics have received a lot of attention and interest at this time. There are many topics can be discussed related to the analytical model, tools, and technology used. Big data analytics model involves many processes with various technologies used. Skills in handling big data, extracting mining, and developing insight are needed in applying big data analytics. Suitable analytical hardware and software also needed in decision making. Big data analytics is a key to a business strategy, but only a small portion of big data is currently used to support their business strategy. Big data analitycs can answer many questions about how to manage costs, time, and development or optimization strategies, and other decision making choices. However, there are many challenges in big data analytics technology. This survey paper addresses topics related to the analytical model, tools, and technology used. This paper also discusses the application of big data analytics in various fields.

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Nopany, Shreyas, and Prof Manonmani S. "Applications of Big Data Analytics in Healthcare Management Systems." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 19, 2021): 1167–82. http://dx.doi.org/10.51201/jusst/21/05416.

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The healthcare industry has become increasingly demanding in recent years. The growing number of patients makes it difficult for doctors and staff to manage their work effectively. In order to achieve their objectives, data analysts collect a large amount of data, analyze it, and use it to derive valuable insights. Data analytics may become a promising solution as healthcare industry demands increase. The paper discusses the challenges of data analytics in the healthcare sector and the benefits of using big data for healthcare analytics. Aside from focusing on the opportunities that big data analytics has in the healthcare sector, the paper will also discuss data governance, strategy formulation, and improvements to IT infrastructure. Implementation techniques include Hadoop, HDFS, MapReduce, and Apache in Big Data Analytics. A Healthcare Management System can be categorized into five divisions, namely, Drug discovery, Disease prevention, diagnosis and treatment, Hospital operations, post-care, requiring comprehensive data management. Big Data analysis support transformation is identified as a required component in future research for the application of Big Data in HealthCare.

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Sangeetha, S., S. Kannimuthu, and P. D. "Survey on Big Data Analytics and its Applications." International Journal of Computer Applications 153, no. 12 (November 24, 2016): 9–12. http://dx.doi.org/10.5120/ijca2016912137.

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Kumar, V. D. Ambeth, Vijayakumar Varadarajan, Mukesh Kumar Gupta, Joel J. P. C. Rodrigues, and Neha Janu. "AI Empowered Big Data Analytics for Industrial Applications." JUCS - Journal of Universal Computer Science 28, no. 9 (September 28, 2022): 877–81. http://dx.doi.org/10.3897/jucs.94155.

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We proposed the idea of editing a special issue that would compile the fruitful research that resulted from the stimulating discussions that occurred during the workshop that was held during the 5th International Conference on Intelligent Computing, Chennai on 25th & 26th March 2022. The objective of this special issue is to call for high-quality papers covering the latest data analytic concepts and technologies of big data and artificial intelligence. This special issue serves as a forum for researchers across the globe to discuss their work and recent advances in this field. The best papers from Artificial intelligence and Big Data Analytics (BAM) in the domains of Product, Finance, Health, and Environment were invited, peer-reviewed. The best high-quality papers were selected based on the innovativeness and relevance of the theme. The amount of data being generated and stored in various fields such as education, energy, environment, healthcare, fraud detection, and traffic is increasing exponentially in the modern era of Big Data. Simultaneously, there is a significant paradigm shift in business and society worldwide due to rapid advancements in fields such as artificial intelligence, machine learning, deep learning, and data analytics. This creates significant challenges for decision-making and the potential for transformation in areas such as the economy, government, and industry. Artificial Intelligence tools, techniques, and technologies, in conjunction with Big Data, improve the predictive power of the systems created and allow the government, public, and private sectors to discover new patterns and trends, as well as improve public values such as accountability, safety, security, and transparency to enable better decision-making, policies, and governance. They also have a wide range of capabilities to perform complex tasks that humans cannot. They could be used to collect, organize, and analyze large, diverse data sets to discover patterns and trends that address a variety of problems related to the development of the economy, such as identifying new sources of revenue, expanding the customer base for business, product reviews, and promotion, disease prediction and prevention, climatic variation prediction, and the provision of energy solutions. The wide variety of subject areas discussed at the 5th International Conference on Intelligent Computing is reflected in the seven accepted papers presented in the following section.

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Abawajy, Jemal H., Albert Y. Zomaya, and Ivan Stojmenovic. "Network computing and applications for Big Data analytics." Journal of Network and Computer Applications 59 (January 2016): 361. http://dx.doi.org/10.1016/j.jnca.2015.11.007.

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You, Ilsun, Marek R. Ogiela, and Myunggwon Hwang. "Intelligent technologies and applications for big data analytics." Software: Practice and Experience 45, no. 8 (June 4, 2015): 1019–21. http://dx.doi.org/10.1002/spe.2331.

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Pauleen, David J., and William Y. C. Wang. "Does big data mean big knowledge? KM perspectives on big data and analytics." Journal of Knowledge Management 21, no. 1 (February 13, 2017): 1–6. http://dx.doi.org/10.1108/jkm-08-2016-0339.

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Purpose This viewpoint study aims to make the case that the field of knowledge management (KM) must respond to the significant changes that big data/analytics is bringing to operationalizing the production of organizational data and information. Design/methodology/approach This study expresses the opinions of the guest editors of “Does Big Data Mean Big Knowledge? Knowledge Management Perspectives on Big Data and Analytics”. Findings A Big Data/Analytics-Knowledge Management (BDA-KM) model is proposed that illustrates the centrality of knowledge as the guiding principle in the use of big data/analytics in organizations. Research limitations/implications This is an opinion piece, and the proposed model still needs to be empirically verified. Practical implications It is suggested that academics and practitioners in KM must be capable of controlling the application of big data/analytics, and calls for further research investigating how KM can conceptually and operationally use and integrate big data/analytics to foster organizational knowledge for better decision-making and organizational value creation. Originality/value The BDA-KM model is one of the early models placing knowledge as the primary consideration in the successful organizational use of big data/analytics.

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Sbalzarini, Ivo F. "Big-Data Analytics transformiert die Lebenswissenschaften." Informatik Spektrum 42, no. 6 (November 11, 2019): 394–400. http://dx.doi.org/10.1007/s00287-019-01227-5.

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Pasupuleti, Mahesh Babu. "The Use of Big Data Analytics in Medical Applications." Malaysian Journal of Medical and Biological Research 3, no. 2 (December 31, 2016): 111–16. http://dx.doi.org/10.18034/mjmbr.v3i2.615.

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The field of Big Data Analytics does not have a linear capacity for growth. It is based on a specified structure. Big data is now most useful for data backup purposes, rather than for anything else. Big Data is a collection of data sets that are both numerous and complicated in nature, and it is becoming increasingly popular. They consist of both organized and unstructured data that is constantly changing at a rate that is inconvenient for traditional relational database systems and existing analytical tools to keep pace with. There is constantly some new information being introduced. It also contributes to the resolution of India's major concerns. It also contributes to closing the data gap. Healthcare is the preservation or advancement of health by the prevention, interpretation, and medical treatment of the disorder, ill health, abuse, and other significant physical, mental, and spiritual degeneration in the mortal body. Health care is conveyed by health professionals in the form of unified health experts, specialists, physician associates, midwives, nurses, antibiotics, pharmacy, psychology, and other health-related fields of expertise. Additionally, it has an introduction, challenging elements and concerns, Big Data Analytics in use, technical specifications, research applications, industrial applications, and future applications. This article aims to provide knowledge in the field of big data analytics and its use in the medical arena.

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Pramanik, Pijush Kanti Dutta, Saurabh Pal, and Moutan Mukhopadhyay. "Big Data and Big Data Analytics for Improved Healthcare Service and Management." International Journal of Privacy and Health Information Management 8, no. 1 (January 2020): 13–51. http://dx.doi.org/10.4018/ijphim.2020010102.

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Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

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Abdul-Jabbar, Safa S., and Alaa K. Farhan. "Data Analytics and Techniques." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 10, no. 2 (October 8, 2022): 45–55. http://dx.doi.org/10.14500/aro.10975.

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Big data of different types, such as texts and images, are rapidly generated from the internet and other applications. Dealing with this data using traditional methods is not practical since it is available in various sizes, types, and processing speed requirements. Therefore, data analytics has become an important tool because only meaningful information is analyzed and extracted, which makes it essential for big data applications to analyze and extract useful information. This paper presents several innovative methods that use data analytics techniques to improve the analysis process and data management. Furthermore, this paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide improvements for many applications. In addition, critical challenges and research issues were provided based on published paper limitations to help researchers distinguish between various analytics techniques to develop highly consistent, logical, and information-rich analyses based on valuable features. Furthermore, the findings of this paper may be used to identify the best methods in each sector used in these publications, assist future researchers in their studies for more systematic and comprehensive analysis and identify areas for developing a unique or hybrid technique for data analysis.

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Ranjan, Rajiv, Zheng Li, Massimo Villari, Yan Liu, and Dimitrios Georgeakopoulos. "Software-driven big data analytics." Computing 102, no. 6 (June 2020): 1409–17. http://dx.doi.org/10.1007/s00607-020-00822-9.

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Barba-González, Cristóbal, José García-Nieto, María del Mar Roldán-García, Ismael Navas-Delgado, Antonio J. Nebro, and José F. Aldana-Montes. "BIGOWL: Knowledge centered Big Data analytics." Expert Systems with Applications 115 (January 2019): 543–56. http://dx.doi.org/10.1016/j.eswa.2018.08.026.

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Pham, Linh Manh, Truong-Thang Nguyen, and Tien-Quang Hoang. "Towards an Elastic Fog-Computing Framework for IoT Big Data Analytics Applications." Wireless Communications and Mobile Computing 2021 (August 15, 2021): 1–16. http://dx.doi.org/10.1155/2021/3833644.

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IoT applications have been being moved to the cloud during the last decade in order to reduce operating costs and provide more scalable services to users. However, IoT latency-sensitive big data streaming systems (e.g., smart home application) is not suitable with the cloud and needs another model to fit in. Fog computing, aiming at bringing computation, communication, and storage resources from “cloud to ground” closest to smart end-devices, seems to be a complementary appropriate proposal for such type of application. Although there are various research efforts and solutions for deploying and conducting elasticity of IoT big data analytics applications on the cloud, similar work on fog computing is not many. This article firstly introduces AutoFog, a fog-computing framework, which provides holistic deployment and an elasticity solution for fog-based IoT big data analytics applications including a novel mechanism for elasticity provision. Secondly, the article also points out requirements that a framework of IoT big data analytics application on fog environment should support. Finally, through a realistic smart home use case, extensive experiments were conducted to validate typical aspects of our proposed framework.

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Christozov, Dimitar, and Katia Rasheva-Yordanova. "Data Literacy." International Journal of Digital Literacy and Digital Competence 8, no. 2 (April 2017): 14–38. http://dx.doi.org/10.4018/ijdldc.2017040102.

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The article shares the authors' experiences in training bachelor-level students to explore Big Data applications in solving nowadays problems. The article discusses curriculum issues and pedagogical techniques connected to developing Big Data competencies. The following objectives are targeted: The importance and impact of making rational, data driven decisions in the Big Data era; Complexity of developing and exploring a Big Data Application in solving real life problems; Learning skills to adopt and explore emerging technologies; and Knowledge and skills to interpret and communicate results of data analysis via combining domain knowledge with system expertise. The curriculum covers: The two general uses of Big Data Analytics Applications, which are well distinguished from the point of view of end-user's objectives (presenting and visualizing data via aggregation and summarization [data warehousing: data cubes, dash boards, etc.] and learning from Data [data mining techniques]); Organization of Data Sources: distinction of Master Data from Operational Data, in particular; Extract-Transform-Load (ETL) process; and Informing vs. Misinforming, including the issue of over-trust vs. under-trust of obtained analytical results.

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Alkhalil, Adel, Magdy Abd Elrahman Abdallah, Azizah Alogali, and Abdulaziz Aljaloud. "Applying Big Data Analytics in Higher Education." International Journal of Information and Communication Technology Education 17, no. 3 (July 2021): 29–51. http://dx.doi.org/10.4018/ijicte.20210701.oa3.

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Higher education systems (HES) have become increasingly absorbed in applying big data analytics due to competition as well as economic pressures. Many studies have been conducted that applied big data analytics in HES; however, a systematic review (SR) of the research is scarce. In this paper, the authors conducted a systematic mapping study to address this deficiency. The qualitative and quantitative analysis of the mapping study resulted in highlighting the research progression over the last decade, and identification of three major themes, 12 subthemes, 10 motivation factors, 10 major challenges, three categories of tools and support techniques, and 16 models for applying big data analytics in higher education. This result contributes to the ongoing research on applying big data analytics in HES. It provides a better understanding of the level of contribution to research as well as identifies gaps for future research direction.

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Kasten, Joseph E. "Big Data Applications in Vaccinology." International Journal of Big Data and Analytics in Healthcare 6, no. 2 (July 2021): 59–80. http://dx.doi.org/10.4018/ijbdah.20210701.oa5.

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The development of vaccines has been one of the most important medical and pharmacological breakthroughs in the history of the world. Besides saving untold lives, they have enabled the human race to live and thrive in conditions thought far too dangerous only a few centuries ago. In recent times, the development of the COVID-19 vaccine has captured the world’s attention as the primary tool to defeat the current pandemic. The tools used to develop these vaccines have changed dramatically over time, with the use of big data technologies becoming standard in many instances. This study performs a structured literature review centered on the development, distribution, and evaluation of vaccines and the role played by big data tools such as data analytics, datamining, and machine learning. Through this review, the paper identifies where these technologies have made important contributions and in what areas further research is likely to be useful.

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Cheng, Shi, Qingyu Zhang, and Quande Qin. "Big data analytics with swarm intelligence." Industrial Management & Data Systems 116, no. 4 (May 9, 2016): 646–66. http://dx.doi.org/10.1108/imds-06-2015-0222.

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Purpose – The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the potential application of SI in big data analytics is analyzed. The correspondence and association between big data analytics and SI techniques are discussed. As an example of the application of the SI algorithms in the big data processing, a commodity routing system in a port in China is introduced. Another example is the economic load dispatch problem in the planning of a modern power system. Findings – The characteristics of big data include volume, variety, velocity, veracity, and value. In the SI algorithms, these features can be, respectively, represented as large scale, high dimensions, dynamical, noise/surrogates, and fitness/objective problems, which have been effectively solved. Research limitations/implications – In current research, the example problem of the port is formulated but not solved yet given the ongoing nature of the project. The example could be understood as advanced IT or data processing technology, however, its underlying mechanism could be the SI algorithms. This paper is the first step in the research to utilize the SI algorithm to a big data analytics problem. The future research will compare the performance of the method and fit it in a dynamic real system. Originality/value – Based on the combination of SI and data mining techniques, the authors can have a better understanding of the big data analytics problems, and design more effective algorithms to solve real-world big data analytical problems.

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Jalgaonkar, Mrunmay. "INDUSTRIAL B2B BIG DATA." International Research Journal of Computer Science 9, no. 5 (May 31, 2022): 106–9. http://dx.doi.org/10.26562/irjcs.2022.v0905.002.

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Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software &for buying and selling of personal and consumer data. The use of big data analytics in managing B2B customer relationships and examines the effects of big data analytics on customer relationship performance and sales growth using a multi-industry dataset from B2B firms. The study finds that the use of customer big data significantly fosters sales growth and enhances the customer relationship performance. However, the latter effect is stronger for firms which have an analytics culture which supports marketing analytics, whereas the former effect remains unchanged regardless of the analytics culture. The study empirically confirms that customer big data analytics improves customer relationship performance and sales growth in B2B firms.

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Lidong, Lidong, and Cheryl Ann Alexander. "Additive Manufacturing and Big Data." International Journal of Mathematical, Engineering and Management Sciences 1, no. 3 (December 1, 2016): 107–21. http://dx.doi.org/10.33889/ijmems.2016.1.3-012.

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Additive manufacturing (AM) can produce parts with complex geometric shapes and reduce material use and weight. However, there are limited materials available for AM processes; the speed of production is slower compared with traditional manufacturing processes. Big Data analytics helps analyze AM processes and facilitate AM in impacting supply chains. This paper introduces advantages, applications, and technology progress of AM. Cybersecurity in AM and barriers to broad adoption of AM are discussed. Big data in AM and Big Data analytics for AM are also presented.

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Wang, Lidong. "Big Data and IT Network Data Visualization." International Journal of Mathematical, Engineering and Management Sciences 3, no. 1 (March 1, 2018): 9–16. http://dx.doi.org/10.33889/ijmems.2018.3.1-002.

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Visualization with graphs is popular in the data analysis of Information Technology (IT) networks or computer networks. An IT network is often modelled as a graph with hosts being nodes and traffic being flows on many edges. General visualization methods are introduced in this paper. Applications and technology progress of visualization in IT network analysis and big data in IT network visualization are presented. The challenges of visualization and Big Data analytics in IT network visualization are also discussed. Big Data analytics with High Performance Computing (HPC) techniques, especially Graphics Processing Units (GPUs) helps accelerate IT network analysis and visualization.

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Garg, Dr Hemant. "Big Data And Healthcare Analytics." Journal of Science & Technology 02, no. 03 (2021): 01–14. http://dx.doi.org/10.55662/jst.2021.2301.

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This paper delves into the use of mobile-based or computer-based apps for reducing patient readmission rates in the American healthcare industry. Such interventions are required for improving population health, increasing patient satisfaction, and reducing costs per capita. The aim of the quadruple aim is to simultaneously achieve its three major goals that are mentioned above. It will also investigate the concept of big data from a generalized perspective before inspecting its application in health analytics and information management systems. One potentially effective approach to introducing and marrying these two distinct concepts would involve exemplifying the adoption of digital electronic terminals in healthcare facilities. Additionally, the Quadruple Aim would be analyzed from the perspective of creating efficiency and strategic operational capacity; which is the essence of big data and health analytics.

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Henriques, Andre Coelho Vaz, Fernando De Souza Meirelles, and Maria Alexandra Viegas Cortez da Cunha. "Big data analytics: achievements, challenges, and research trends." Independent Journal of Management & Production 11, no. 4 (August 1, 2020): 1201. http://dx.doi.org/10.14807/ijmp.v11i4.1085.

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Big data applications combined with analytical tools foster prediction techniques that impact societal, economic, and political changes. After almost a decade of studies, this paper proposes to identify major debates on big data analytics, presenting its evolution over the past years and identifying its research tendencies. We limited our research to the top eight journals in information systems. Our findings suggest that big data analytics is apparently reaching a plateau, which might be confirmed by publications in the following years. The paper contributes to the current debate on big data by identifying ongoing studies in the research community. In addition, it provides a critical analysis of the field development, from its perceived benefits to its unimagined consequences. Finally, we conclude that other perspectives on big data analytics might include a new wave of studies and that new paths beyond productivity gains can be explored.

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贾, 美娟. "Research of Deep Learning Applications in Big Data Analytics." Software Engineering and Applications 11, no. 03 (2022): 549–57. http://dx.doi.org/10.12677/sea.2022.113057.

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Xu, Chenren, Zhu Han, Yanyong Zhang, and Lan Zhang. "Special issue on big data computing, analytics and applications." Personal and Ubiquitous Computing 21, no. 1 (November 16, 2016): 1–3. http://dx.doi.org/10.1007/s00779-016-0973-1.

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Bestak, Dr Robert, and Dr S. Smys. "BIG DATA ANALYTICS FOR SMART CLOUD-FOG BASED APPLICATIONS." Journal of Trends in Computer Science and Smart Technology 2019, no. 02 (December 3, 2019): 74–83. http://dx.doi.org/10.36548/jtcsst.2019.2.001.

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The internet connectivity extended by the internet of things to all the tangible things lying around and used by us in our day today life has convert the devices into smart objects and led to huge set of data generation that holds both the valuable and invaluable information. In order to perfectly handle the information’s generated and mine the valuables from them, the analytics are engaged by the cloud. To have a timely access, most probably the fog services are preferred than the cloud as they bring down the service of the cloud to the user edge and reduces the time complexity in accessing of the information. So the paper proposes the big data analytics for the fog assisted health care application to effectively handle the health information’s diagnosed for the aged persons. The proposed model is simulated using the IFogSim toolkit to examine the performance fogassisted smart healthcare application.

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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 (October 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 transmitted to pregnant woman to make quick decisions; with the intervention of her doctor; through an android mobile application that we created. As well, she receives recommendations based on her behavior. We used real-world data to validate the system and assess its performance and effectiveness. Experiments were made using the Big Data Platform Databricks.

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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 (November 2018): 12. http://dx.doi.org/10.1109/mcom.2018.8539013.

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Hien, Le Thi Thu, Nguyen Tuong Van, Kim Thi Phuong Oanh, Nguyen Dang Ton, Huynh Thi Thu Hue, Nguyen Thuy Duong, Pham Le Bich Hang, and Nguyen Hai Ha. "Genomics and big data: Research, development and applications." Vietnam Journal of Biotechnology 19, no. 3 (October 13, 2021): 393–410. http://dx.doi.org/10.15625/1811-4989/16158.

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Recent years, genomics and big data analytics have been widely applied and have significant impacts in various important areas of social life worldwide. The development of the next-generation sequencing (NGS) technologies, such as whole-genome sequencing (WGS), whole-exome sequencing (WES), transcriptome, and/or targeted sequencing, has enabled quickly generating the genomes of interested living organisms. Around the world many nations have invested in and promoted the development of genomics and big data analytics. A number of well-established projects on sequencing of human, animal, plant, and microorganism genomes to generate vast amounts of genomic data have been conducted independently or as collaborative efforts by national or international research networks of scientists specializing in different technical fields of genomics, bioinformatics, computational and statistical biology, automation, artificial intelligence, etc. Complicated and large genomic datasets have been effectively established, storage, managed, and used. Vietnam supports this new field of study through setting up governmental authorized institutions and conducting genomic research projects of human and other endemic organisms. In this paper, the research, development, and applications of genomic big data are reviewed with focusing on: (i) Available sequencing technologies for generating genomic datasets; (ii) Genomics and big data initiatives worldwide; (iii) Genomics and big data analytics in selected countries and Vietnam; (iv) Genomic data applications in key areas including medicine for human health care, agriculture - forestry, food safety, and environment.

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Bikakis, Nikos, George Papastefanatos, and Olga Papaemmanouil. "Big Data Exploration, Visualization and Analytics." Big Data Research 18 (December 2019): 100123. http://dx.doi.org/10.1016/j.bdr.2019.100123.

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Raghupathi, Viju, Yilu Zhou, and Wullianallur Raghupathi. "Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis." International Journal of Healthcare Information Systems and Informatics 14, no. 4 (October 2019): 1–20. http://dx.doi.org/10.4018/ijhisi.2019100101.

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In this article, the authors explore the potential of a big data analytics approach to unstructured text analytics of cancer blogs. The application is developed using Cloudera platform's Hadoop MapReduce framework. It uses several text analytics algorithms, including word count, word association, clustering, and classification, to identify and analyze the patterns and keywords in cancer blog postings. This article establishes an exploratory approach to involving big data analytics methods in developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through various means, including the development of categories or keywords contained in the blogs, the development of a taxonomy, and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It can provide insight and decision support for cancer management and facilitate efficient and relevant searches for information related to cancer.

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Ajah, Ifeyinwa Angela, and Henry Friday Nweke. "Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications." Big Data and Cognitive Computing 3, no. 2 (June 10, 2019): 32. http://dx.doi.org/10.3390/bdcc3020032.

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Big data and business analytics are trends that are positively impacting the business world. Past researches show that data generated in the modern world is huge and growing exponentially. These include structured and unstructured data that flood organizations daily. Unstructured data constitute the majority of the world’s digital data and these include text files, web, and social media posts, emails, images, audio, movies, etc. The unstructured data cannot be managed in the traditional relational database management system (RDBMS). Therefore, data proliferation requires a rethinking of techniques for capturing, storing, and processing the data. This is the role big data has come to play. This paper, therefore, is aimed at increasing the attention of organizations and researchers to various applications and benefits of big data technology. The paper reviews and discusses, the recent trends, opportunities and pitfalls of big data and how it has enabled organizations to create successful business strategies and remain competitive, based on available literature. Furthermore, the review presents the various applications of big data and business analytics, data sources generated in these applications and their key characteristics. Finally, the review not only outlines the challenges for successful implementation of big data projects but also highlights the current open research directions of big data analytics that require further consideration. The reviewed areas of big data suggest that good management and manipulation of the large data sets using the techniques and tools of big data can deliver actionable insights that create business values.

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Cremer, Stefan, and Claudia Loebbecke. "Artificial Intelligence Imagery Analysis Fostering Big Data Analytics." Future Internet 11, no. 8 (August 15, 2019): 178. http://dx.doi.org/10.3390/fi11080178.

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In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

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Rath, Mamata. "Real Time Analysis Based on Intelligent Applications of Big Data and IoT in Smart Health Care Systems." International Journal of Big Data and Analytics in Healthcare 3, no. 2 (July 2018): 45–61. http://dx.doi.org/10.4018/ijbdah.2018070104.

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Currently, there is an expanding interest for additional medical data from patients about their healthcare choices and related decisions, and they further need investment in their basic health issues. Big data provides patients presumptuous data to help them settle on the best choice and align with their medicinal treatment plan. One of the very advanced concepts related to the synthesis of big data sets to reveal the hidden pattern in them is big data analytics. It involves demanding techniques to mine and extract relevant data that includes the actions of piercing a database, effectively mine the data, query and inspect the data and is committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage data that can influence the business. In this way, the primary goal of big data analytics is to help business relationships to have enhanced comprehension of data, and subsequently, settle on proficient and very much educated decisions. Big data analytics empowers data diggers and researchers to examine an extensive volume of data that may not be outfit utilizing customary apparatuses. Big data analytics require advances and statistical instruments that can change a lot of organized, unstructured, and semi-organized data into more reasonable data and metadata designed for explanatory procedures. There is tremendous positive potential concerning the application of big data in human health care services and many related major applications are still in their developmental stages. The deployment of big data in health service demonstrates enhancing health care results and controlling the expenses of common people due to treatment, as proven by some developing use cases. Keeping in view such powerful processing capacity of big data analytics in various technical fields of modern civilization related to health care, the current research article presents a comprehensive study and investigation on big data analytics and its application in multiple sectors of society with significance in health care applications.

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Bumblauskas, Daniel, Herb Nold, Paul Bumblauskas, and Amy Igou. "Big data analytics: transforming data to action." Business Process Management Journal 23, no. 3 (June 5, 2017): 703–20. http://dx.doi.org/10.1108/bpmj-03-2016-0056.

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Purpose The purpose of this paper is to provide a conceptual model for the transformation of big data sets into actionable knowledge. The model introduces a framework for converting data to actionable knowledge and mitigating potential risk to the organization. A case utilizing a dashboard provides a practical application for analysis of big data. Design/methodology/approach The model can be used both by scholars and practitioners in business process management. This paper builds and extends theories in the discipline, specifically related to taking action using big data analytics with tools such as dashboards. Findings The authors’ model made use of industry experience and network resources to gain valuable insights into effective business process management related to big data analytics. Cases have been provided to highlight the use of dashboards as a visual tool within the conceptual framework. Practical implications The literature review cites articles that have used big data analytics in practice. The transitions required to reach the actionable knowledge state and dashboard visualization tools can all be deployed by practitioners. A specific case example from ESP International is provided to illustrate the applicability of the model. Social implications Information assurance, security, and the risk of large-scale data breaches are a contemporary problem in society today. These topics have been considered and addressed within the model framework. Originality/value The paper presents a unique and novel approach for parsing data into actionable knowledge items, identification of viruses, an application of visual dashboards for identification of problems, and a formal discussion of risk inherent with big data.

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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 reason for the cause of various unorganized and unstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic review is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms and frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. Findings of this paper will guide the development of techniques using the combination of AI and the big data as source for handling m-health data more effectively.

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Pramanik, Md Ileas, Raymond Y. K. Lau, Md Abul Kalam Azad, Md Sakir Hossain, Md Kamal Hossain Chowdhury, and B. K. Karmaker. "Healthcare informatics and analytics in big data." Expert Systems with Applications 152 (August 2020): 113388. http://dx.doi.org/10.1016/j.eswa.2020.113388.

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Journal articles on the topic 'Big Data analytics applications'

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