Big Data Characteristics, Challenges, Architectures, Analytics and Applications: A Review

Objectives: The rapid change and emergence of the technology are happening more than a decade. The current world has become almost completely a digital and computing world. The objective of this paper is to describe real impact of Big Data analytics techniques in this digital world to obtain valuable knowledge. The essential goal of this paper was to examine about the big data analytics methods and techniques. It has reviewed six of the techniques that are used by the big data researchers to pick up bits of knowledge by processing and analyzing the big data in limited time. The development of these techniques will help us to analyze the big data more effectively and subsequently delivering more productive and gainful outcomes. Methods/Statistical Analysis: We reviewed different analytics techniques for text, audio, video, and social media data, as well as predictive analytics. This paper describes the six Analytics Techniques to obtain valuable knowledge in limited time. Findings: The six analytics techniques which is discussed in this paper is very important to obtain valuable information from big data in limited time, because now a days data is growing at very fast rate. Application/Improvements: This paper presented an overview of most commonly use big data analytics techniques. Keywords: Big Data, Data, Digital, Information, Knowledge, Technology

Big Data Applications (BDAs) are used to support a decision making process, but their developers and users are often unaware of the exact uncertainties that underlie the output of BDAs. In this paper we present a System for Uncertainty Decision Support (SUDS) as a generic plug-in for BDAs that aims to give both developers and users more insight in the location, types and propagation of uncertainties within a BDA. To achieve this, SUDS receives uncertainty information directly from the BDA and provides a separate front-end where the uncertainties can be explored and queried. SUDS combines state of the art methods in knowledge modelling, natural language processing and big data architectures.

Abstract. With the wide application of Big Data, Artificial Intelligence and Internet of Things in geographic information technology and industry, geospatial big data arises at the historic moment. In addition to the traditional "5V" characteristics of big data, which are Volume, Velocity, Variety, Veracity and Valuable, geospatial big data also has the characteristics of "Location Attribute". At present, the study of geospatial big data are mainly concentrated in: knowledge mining and discovery of geospatial data, Spatiotemporal big data mining, the impact of geospatial big data on visualization, social perception and smart city, geospatial big data services for government decision-making support four aspects. Based on the connotation and extension of geospatial big data, this paper comprehensively defines geospatial big data comprehensively. The application of geospatial big data in location visualization, industrial thematic geographic information comprehensive service and geographic data science and knowledge service is introduced in detail. Furthermore, the key technologies and design indicators of the National Geospatial Big Data Platform are elaborated from the perspectives of infrastructure, functional requirements and non-functional requirements, and the design and application of the National Geospatial Public Service Big Data Platform are illustrated. The challenges and opportunities of geospatial big data are discussed from the perspectives of open resource sharing, management decision support and data security. Finally, the development trend and direction of geospatial big data are summarized and prospected, so as to build a high-quality geospatial big data platform and play a greater role in social public application services and administrative management decision-making.

The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.

Cloud computing and big data: Technologies and applications

Data in the maritime domain is growing at an unprecedented rate, e.g., terabytes of oceanographic data are collected every month, and petabytes of data are already publicly available. Big data from heterogeneous sources such as sensors, buoys, vessels, and satellites could potentially fuel a large number of interesting applications for environmental protection, security, fault prediction, shipping routes optimization, and energy production. However, because of several challenges related to big data and the high heterogeneity of the data sources, such applications are still underdeveloped and fragmented. In this paper, we analyze challenges and requirements related to big maritime data applications and propose a scalable data management solution. A big data architecture meeting these requirements is described, and examples of its implementation in concrete scenarios are provided. The related data value chain and use cases in the context of a European project, BigDataOcean, are also described.

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 applications are becoming more popular across many different fields and civil engineering is no different. The benefits of big data lie in its potential to provide valuable insights into various large datasets. Big data applications can identify patterns and trends that were previously unknown, which can help them make informed decisions and develop effective strategies. In the case of civil engineering, this could be taking large datasets that have been produced in relation to pieces of infrastructure and using them to create more efficient management strategies. One of the issues with using big data is that if the input dataset is flawed then the output resulting from any big data analysis will be compromised. Therefore, data validation is used, which is the process of ensuring that data is accurate, complete, and consistent. The consequence of not undertaking data validation may be Inaccurate or inconsistent data which can lead to incorrect insights and decisions in big data applications. This paper explores the necessity of validating data before it is used in a big data application. It outlines some of the different methods used for validating data and provides an overview of the potential issues that may arise from data validation errors. A case study is then presented showing the process of validation on data collected from four bridges and provides recommendations for implementing data validation as part of a larger big data workflow. The results of the case study show that validation of data is an important step in the big data process both for confidence in the outputs and to make big data applications more useful and more common in the civil engineering field. The discussion and presented case study in this paper highlight the necessity of validating data. It has shown some of the potential issues that may arise from not undertaking data validation.

In the field of medicine and healthcare, giant medical and healthcare information is created by medical institutions, medical information platforms, digital medical equipments, intelligent health electronic products, wearable personal health equipment, and other health-related information platforms. The sum of these information forms the medical and healthcare big data. The development and application of this big data not only can improve the medical and health system, but also can promote the development of health industry, which has realistic and long-term strategic significance to national economic development. In this paper, the basic characteristics of medical and healthcare big data were introduced, and the problems and challenges in the application of this big data in China were analyzed. In order to improve the level of informatization and management efficiency of medical and healthcare management in China, a national medical and healthcare big data platform was proposed, which can integrate omni-directional and full-cycle health-related information of institutions and individuals in China. In addtion, the construction mode, operation mode, data source and platform function of this platform were introduced. Key words: Medical and healthcare big data; Medical and health management; Big data applications; Big data function

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

Big data management and application specialty is an emerging major in our school of information and business management. Based on the Internet plus and big data era, we mainly study the application of big data analysis theory and methods in economic management and the management and management of big data. This paper aims to explore how to improve the innovation and practical ability of students in this major through practical teaching. This paper mainly elaborates from four aspects, namely, the basic principles of practical teaching, the path of students' ability improvement, the construction of the completed practical teaching system and the application of practical teaching in this major.

The chapter showcases the awareness of big data usage among librarians in Zimbabwe. The concept of big data is new, and librarians are building capacity to move with the current trends in librarianship. This chapter assists in pointing areas where big data can be applied in libraries. It also documents the challenges that are faced when using big data applications and proffer solutions that can be applied to deal with those challenges. It answers the question of whether it is practical to utilise big data in any type of library. A qualitative study was done where an online questionnaire was administered to twenty librarians in research institutions in Zimbabwe. The findings revealed that librarians are aware of the big data concept but are not utilising the tools and techniques in data mining and analysis. The authors recommend that capacity building should be done to equip librarians with the requisite skills.

There is a plethora of both organized and haphazard data in tourism destinations. Analyzing this data appropriately is crucial for optimal engagement. This study focuses on the connection between big data analytics and big data's characteristics in Agodi Gardens, Ibadan, Nigeria. Specific objectives were to examine the characteristics of big data; as well as to examine Descriptive and predictive data analytics. Respondents were chosen purposively. Survey instrument (questionnaire) was used to elicit data. Data was collected using structured questionnaire. The collected data were analyzed descriptively and inferentially. The study revealed that significant relationship exists between the prescriptive/descriptive big data analytics and the characteristics of big data. Precisely, there is a significant relationship between prescriptive data analytics and velocity, veracity, volume as well as value. Similarly, there is a significant relationship between descriptive data analytics and volume, variety, value as well as veracity. Likewise, variety and veracity of big data could influence big data analytics. The study therefore recommends that the management of Agodi Gardens should engage thorough big data analytics, so that data elicited by customers can be appropriately analysed and topical inference could be drawn from the analysis.

A large quantity of statistics is generated because of the explosive boom inside the wide variety of devices connected to the Internet of Things. However, such data large amount of data is not useful without the power of analytics. Different IOT, big data, and analytics have enable individuals to get significant understanding into vast measure of information produced by IoT gadgets. Nonetheless, these arrangements are still in their underlying state, and the area comes up short on a far reaching overview. The contribution of this paper is to purpose a five layer architecture for big IOT data. The functionality of five different layers of big IOT data architecture is explained. The opportunities for big IoT data architecture are discussed. Further more challenges face by the architecture are also explained.

PurposeThe purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.Design/methodology/approachThis paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.FindingsAI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.Research limitations/implicationsThere is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.Social implicationsThis paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.Originality/valueThis paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.