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Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem.

The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries.

It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries.

The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data.

The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time.

According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.

Resilient distributed processing technique (RDPT), in which mapper and reducer are simplified with the Spark contexts and support distributed parallel query processing.

The proposed work is implemented with Pig Latin with Spark contexts to develop query processing in a distributed environment.

Query processing in Hadoop influences the distributed processing with the MapReduce model. MapReduce caters to the works on different nodes with the implementation of complex mappers and reducers. Its results are valid for some extent size of the data.

Pig supports the required parallel processing framework with the following constructs during the processing of queries: FOREACH; FLATTEN; COGROUP.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

Public Domain Software on File offers a great wealth of programs on subjects that range from biorhythm to physics. Many of these can be of great use to many people. However, the package also contains many programs which do not seem useful at all. If you would like to substantially increase your software collection at one fell swoop, this might be the package for you. It contains 20 floppy disks with 250 programs, each of which can be copied selectively to meet special needs. The categories covered are: Business, Education, Graphics, Home Management, Music, Utilities and Potpourri.

Cloud computing promises dependable services offered through next-generation data centres based on virtualization technologies for computation, network and storage. Big Data Applications have been made viable by cloud computing technologies due to the tremendous expansion of data. Disaster management is one of the areas where big data applications are rapidly being deployed. This study looks at how big data is being used in conjunction with cloud computing to increase disaster risk reduction (DRR). This paper aims to explore and review the existing framework for big data used in disaster management and to provide an insightful view of how cloud-based big data platform toward DRR is applied.

A systematic mapping study is conducted to answer four research questions with papers related to Big Data Analytics, cloud computing and disaster management ranging from the year 2013 to 2019. A total of 26 papers were finalised after going through five steps of systematic mapping.

Findings are based on each research question.

A specific study on big data platforms on the application of disaster management, in general is still limited. The lack of study in this field is opened for further research sources.

In terms of technology, research in DRR leverage on existing big data platform is still lacking. In terms of data, many disaster data are available, but scientists still struggle to learn and listen to the data and take more proactive disaster preparedness.

This study shows that a very famous platform selected by researchers is central processing unit based processing, namely, Apache Hadoop. Apache Spark which uses memory processing requires a big capacity of memory, therefore this is less preferred in the world of research.

Applying brand names to international markets remains a challenge to multinational corporations. Consumers’ sociolinguistic backgrounds shape their responses to brand names. This paper uses a sociolinguistic approach as a conceptual framework in understanding brand naming and translating in the Chinese market. The approach promotes that sociolinguistics a) recognizes linguistic competence, b) advances symbolic values imbedded in linguistic forms, and c) renders attached social valence to cultural scrutiny. Three brand‐naming cases in China are presented for discussion, which may benefit multinational corporations on brand decisions involving Chinese consumers.

This paper aims to determine how a strategic innovation framework that integrates the concepts of innovation function, dynamic performance management (DPM) and system-dynamics (SD) modelling can measure performance in a supply chain (SC).

The paper provides a strategic innovation framework for an SC by considering three steps. First, a systemic intervention is presented based on the innovation functions that influence SC performance. Second, an analysis of the system's performance is proposed. Third, a model SD-based simulation is designed. The developed framework is explained by employing a case study of the Colombian pig sector SC.

The results reveal that identifying and synchronising the system's performance drivers associated with the innovation functions could improve the inventory in the SC.

On the one hand, managers can use the proposed framework to evaluate the innovation investments and understand their impact on operation performance (e.g. on inventories). On the other hand, policymakers may support decision-making to improve policy design (e.g. through investment in R&D).

Few studies discuss the impacts of innovation functions on SC performance. This paper aims to fill this theoretical gap and to contribute to the literature by suggesting a novel framework which includes innovation functions.