BigData Analytics Techniques to Obtain Valuable Knowledge

The field of public health provides a multidisciplinary area of research, and new challenges in analytics and operations management are reshaping the strategic decision-making dynamics. Big data is an emerging and promising area of research that has great potential to transform public health. Developed countries are designing big data tools and analytic techniques and using them to provide leadership. In this respect, low- and middle-income countries (LMICs) need to become more engaged in the big data ecosystem and to become more aware of how to use new analytical techniques. This chapter provides an overview of big data analytic techniques in public health and their potential to transform strategic public health management in LMICs. To achieve this goal, this chapter explains big data analytics and big data tools in public health and discusses development strategies for LMIC's big public data potential to ensure a better knowledge base for LMIC health policymakers.

Big data streams are now widespread because real-time applications that generate enormous amounts of data quickly have proliferated during the past 10 years. Big data is a treasure trove of vast quantities of data. Big data is commonly used in conjunction with other current buzzwords like data science, artificial intelligence, deep learning, and machine learning. Big data has a very promising future because of the demand for data across many technical industries. A massive amount of data has been produced as a result of the COVID-19 global epidemic. Big data analytics is helpful for evaluating, maintaining, and archiving information about COVID-19 patients for later use during the pandemic phase. Big Data Analytics (BDA), a growingly important technology, can significantly enhance sustainable tourism in terms of destination selection, traveller satisfaction, and the quality of travel experiences. Streaming Big Data is the term used to describe data that needs to be saved, processed, and analysed and that is continuously generated from various data sources, including Internet of Things (IoT) devices. The article provides information on - Big data analytics techniques in social media, COVID-19 Pandemic in the New Era of Big Data Analytics, Big data analytics and sustainable tourism, Predictive big data analytics for supply chain demand forecasting, Streaming Big Data Analytics, Big data stream analysis and Big Data Analytics in Crypto Currency. Keywords: Big Data, Streaming Big Data, Predictive Big Data, Sustainable Tourism

Big data is a Firing Term in the recent era of the modern world, due to the information exploita-tion; there is an enormous amount of data produced. Big data is a powerful momentum of infor-mation and communication technology field due to the effect of growing data in healthcare, IOT, cloud computing, online education, online businesses, and public management. The produced data is not only large but also complex. Big data has a large amount of unstructured data so that there is a need to develop advanced tools and techniques for handling big data. Machine Learning is a prominent area of Artificial Intelligence. It makes the system to make intelligent resolutions by giving the knowledge to achieve the goals. This study reviews the various challenges and innovative ideas for big data analytics with machine learning in different fields over the past ten years. This paper mainly organized to identify the research projects based on the discussions over machine learning techniques for big data analytics and provide suggestions to develop the new projects.

The purpose of this study is to review journal papers on educational big data research published from 2010 to 2018. A total of 143 papers were selected. The papers were characterized based on three dimensions: (a) educational goals; (b) educational problems addressed; and (c) big data analytical techniques used. A qualitative content analysis approach was conducted to develop a coding scheme for analyzing the selected papers. The results identified four types of educational goals, with a clear predominance of quality assurance. The identification of the most mentioned educational problems resulted in four main concerns: the lack of detecting student behavior modeling and waste of resources; inappropriate curricula and teaching strategies; oversights of quality assurance; and privacy and ethical issues. Concerning the most mentioned big data analytical techniques, the coding scheme revealed that the majority of the papers focused on the educational data mining technique followed by the learning analytics technique. The visual analytics technique was mentioned in a few papers. The results also indicated that the educational data mining technique is the most suitable technique to use for quality assurance and to provide potential solutions for the lack of detecting student behavior modeling and the waste of resources in institutions.

Big data has evolved as a most challenging area in scientific study and research. It has drawn much attention during the last few years. It influences our modern society, business, government, healthcare, research and almost every discipline. In this data-driven era, where data is continuously acquired from a verity of sources for different purposes, the ability to make timely decisions based on available data is a very critical task. The massive data size, variety, velocity, accuracy and high dimensionality presents new challenges to big data. This paper attempts to present some challenges of big data. In addition, a study on the conceptual design of big data architecture presented on specific big data applications. In this paper, we presented research work on big data analytics techniques in the areas of text, sentiment, video, social media and predictive analytics. A comparative analysis on selected big data applications has been presented in detail.

The idea that we can get value from data has been discussed, but the main challenge is to use data effectively in order to facilitate smarter and better decision making and surpass our competitors. The change leaders in organization are now dealing with big data from both within and outside the enterprise, including structured and unstructured data, machine data, online and mobile data to supplement their organizational data pool and provide and facilitate the way through which the businesses can compete and operate successfully. Companies that invest in big data can have a distinct advantage over their competitors. Therefore, in this chapter, the concepts of big data analytics along with the relevant description of different categorization, capabilities, challenges are firstly explained, and then big data analytics techniques and methods are introduced and discussed to make the readers familiar with the way big data is applied in the enterprises.

Organizing and maintaining the big data are the two major concerns which have led to many challenges for the organization. The main objective of this research work is to give an overall idea about organizing Big data with High performance. MapReduce is one of the commonly used techniques which is used to analyze a large volume of data in an efficient manner. A common overview of Big data and the implementation of the MapReducing technique on Biomedical Big Data has been discussed in this paper with an algorithm. Discussion on performance analysis of MapReducing technique being will open the doors for further research activities in Big Data Analytics and MapReducing technique. Highlight of this research work is the data which has been selected and the output of the research work has been openly discussed to help the beginners of Big data. The proposed research work will give an insight about the implementation of Hadoop Distributed File System for small and medium sized business. Keywords: Big Data, Big Data analytics, Biomedical Data Analysis, MapReduce, Performance

The average volume of data produced daily is estimated to be over 2.5 quintillion byte. Moreover, by year 2020, it is estimated that 1.79MB of data will be created every second by each person in the world. Apparently, big datasets contain tremendous amount of valuable information that can be used for improved decision making. However, big data requires incredible amount of storage and computational resources for effective processing. Machine Learning (ML) algorithms are effective tools popularly used to analyze and extract concealed insights from datasets. However, some ML algorithms were not originally designed to handle big datasets, hence their computational complexity decreases with increase in data size. Consequently, this makes big data analytics extremely slow or unrealistic. Therefore, there is an obvious need for fast and effective techniques for big data analytics. This paper introduces an intelligent hybrid ML-based technique suitable for big data analytics (called EDISA_ML). EDISA_ML is a boundary detection and instance selection algorithm, inspired by edge detection in image processing. It was evaluated on four ML algorithms and big datasets, and the results show that it achieved a storage reduction of over 50% and simultaneously improved the training speed of the evaluated ML algorithms by over 93% (in some cases), without meaningfully affecting their prediction accuracy.

Big data analytics helps in analyzing a huge set of data whereas IoT is about data, devices and connectivity. Internet of Things (IoT) involves connecting physical objects to the Internet to build smart systems and universal mobile accessibility advanced technologies like Intelligent Transportation System (ITS). IoT solutions are playing a major role in driving the global IoT in Intelligent Transportation System. Communication between vehicles using IoT will be a new era of communication that leads to ITS. IoT is a combination of storing and processing sensor data and computing using data analytics to achieve and assist in managing the Traffic system effectively. IoT based Intelligent transportation system (IoT-ITS) helps in automating railways, roadways, airways and marine which enhance customer experience about the way goods are transported, tracked and delivered. A case study on Intelligent Traffic Management System based on IoT and big data, which will be a part of, smart traffic solutions for smarter cities. The ITS-IoT system itself forms an eco-system comprising of sensor systems, monitoring system and display system. There are several techniques and algorithms involved in full functioning of IoT-ITS. The proposed case study will examine and explain a complete design and implementation of a typical IoT-ITS system for a smart city scenario set on typical Indian subcontinent. This case study will also explain about several hardware and software components associated with the system. How concepts like Multiple regression analysis, Multiple discriminant analysis and logistic regression, Cojoint analysis, Cluster analysis and other big data analytics techniques will merge with IoT and help to build IoT-ITS will also be emphasized. The case study will also display some big data analytics results and how the results are utilized in smart transportation systems.

Few topics have generated more discourse in recent years than big data analytics. Given their knowledge of analytical and mathematical methods, operations research (OR) scholars would seem well poised to take a lead role in this discussion. Unfortunately, some have suggested there is a misalignment between the work of OR scholars and the needs of practicing managers, especially those in the field of operations and supply chain management where data-driven decision-making is a key component of most job descriptions. In this paper, we attempt to address this misalignment. We examine both applied and scholarly applications of OR-based big data analytical tools and techniques within an operations and supply chain management context to highlight their future potential in this domain. This paper contributes by providing suggestions for scholars, educators, and practitioners that aid to illustrate how OR can be instrumental in solving big data analytics problems in support of operations and supply chain management.

Big Data Analytics (BDA) provides an important resource for businesses seeking to enhance their performance and gain a competitive advantage, although not all organizations are adopting BDA techniques, and small and medium-sized enterprises (SMEs) in Jordan have been slow in this regard, despite being key players in any healthy economy, and the fact that BDA adoption can be facilitated by using the Technology Acceptance Model (TAM). The purpose of this study is to investigate the drivers of behavioral intention among managerial-level employees in Jordanian ICT SMEs to adopt BDA through a quantitative correlational research approach. The TAM questionnaire was used to gather data from 271 online survey participants in Jordan using Google Forms. The target group included management level staff working in small and medium-sized ICT firms (SMEs). Confirmatory factor analysis (CFA) was used to evaluate the research instrument's reliability and validity, and structural equation modeling (SEM) was utilized to test the study's hypotheses. The findings revealed that perceived usefulness, perceived ease of use, and perceived “privacy and security” significantly influenced managerial-level employees' behavioral intention to use BDA in their organizations. The research findings also supported the application of TAM, and the results of the investigation indicated that managerial-level employees would be willing to use BDA techniques providing they were perceived to be useful, user-effortless, and posed little concern about privacy and security. Overall, the current study's results demonstrate that the suggested model had good predictive power, 51% of the variance in behavioral intention, and was therefore capable of predicting managers' intentions to use BDA.

A Survey on Tools and Techniques of Big Data Analytics With Privacy Concerns

Efficient Detection of Environmental Violators: A Big Data Approach

Big data analytics (BDA) methodologies and tools are used extensively for retailing at China. However, the existing insights are scattered over different literature sources and there is a lack of a structured and unbiased review methodology to systematise BDA application areas in the retail at China comprehensively covering efficiency, resilience and sustainability paradigms. In this study, we first propose a systematic review methodology for the field of BDA in retailing. Second, we use the methodology proposed for a systematic literature review on BDA techniques in the retailing fields aiming at classifying the existing BDA models/techniques employed, structuring their practical application areas, identifying the research gaps and potential future research directions. Our analysis is triangulated across efficiency, resilience, and sustainability perspectives. The developed review methodology and proposed novel classifications and categorisations can be used by researchers and practitioners alike for a structured analysis and applications of BDA in retailing at China.

The rise of personal computers, cell phones, and other smart gadgets has ushered in a new era of enormous data generation, sharing, and use. In the big data age, there has been a significant shift in consumer purchasing behavior due to the high transparency and quick flow of information. Consumers are becoming more logical in their purchasing decisions, paying more attention to the observable advantages that brands offer, and placing greater importance on the individualized services that businesses offer. With this in mind, this article explains some of the functions and analytical techniques of big data analysis in forecasting customer behavior. To be specific, the basic concepts and models of bigdata are introduced. Subsequently, the corresponding applications based on the state-of-art big data analysis techniques are demonstrated. The research of changes in customer behavior will assist businesses in promptly adjusting their marketing plans and winning the first chance in the harsh market rivalry. These results shed light on guiding further exploration of consumer behavior analysis in the new era.