Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.

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Business intelligence and analytics: from big data to big impact

Objective: To describe the promise and potential of big data analytics in healthcare. Methods: The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Results: The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Conclusions: Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.

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Big data analytics in healthcare: promise and potential

Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

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Mobile Edge Computing: A Survey

Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. These useful informations for companies or organizations with the help of gaining richer and deeper insights and getting an advantage over the competition. For this reason, big data implementations need to be analyzed and executed as accurately as possible. This paper presents an overview of big data's content, scope, samples, methods, advantages and challenges and discusses privacy concern on it.

Big data: A review

Big data

In this paper, we explain the concept, characteristics & need of Big Data & different offerings available in the market to explore unstructured large data. This paper covers Big Data adoption trends, entry & exit criteria for the vendor and product selection, best practices, customer success story, benefits of Big Data analytics, summary and conclusion. Our analysis illustrates that the Big Data analytics is a fast-growing, influential practice and a key enabler for the social business. The insights gained from the user generated online contents and collaboration with customers is critical for success in the age of social media.

Big Data analytics

The Internet of Things (IoT) represents a diverse technology and usage with unprecedented business opportunities and risks. The Internet of Things is changing the dynamics of security industry & reshaping it. It allows data to be transferred seamlessly among physical devices to the Internet.

Internet of Things (IoT): Security challenges, business opportunities & reference architecture for E-commerce

Blockchain is a decentralized ledger used to securely exchange digital currency, perform deals and transactions. Each member of the network has access to the latest copy of encrypted ledger so that they can validate a new transaction. Blockchain ledger is a collection of all Bitcoin transactions executed in the past. Basically, it's a distributed database which maintains a continuously growing tamper proof data structure blocks which holds batches of individual transactions. The completed blocks are added in a linear and chronological order. Each block contains a timestamp and information link which points to a previous block. Bitcoin is peer-to-peer permission-less network which allows every user to connect to the network and send new transaction to verify and create new blocks. Satoshi Nakamoto described design of Bitcoin digital currency in his research paper posted to cryptography listserv in 2008. Nakamoto's suggestion has solved long pending problem of cryptographers and laid the foundation stone for digital currency. This paper explains the concept, characteristics, need of Blockchain and how Bitcoin works. It attempts to highlights role of Blockchain in shaping the future of banking, financial institutions and adoption of Internet of Things(IoT).

Blockchain: Future of financial and cyber security

Container-based virtualization uses single kernel to run multiple instances on an operating system and virtualization layer runs as an application within the operating system. It is also called operating system virtualization and in this approach, the kernel of operating system runs on the hardware node with different isolated guest virtual machines (VMs) called containers.

Containers & Docker: Emerging roles & future of Cloud technology

Over 300 million diagnostic radiology images are taken every year in the United States [1]. The pressure on healthcare providers will increase considerably to operate more efficiently and accurately with growing demand for diagnostic services. The National Institute of Medicine estimates that 12 million Americans are misdiagnosed every year [2]. More accurate and efficient decision support tools for doctors could greatly reduce that number. Deep learning is a technology inspired by the workings of the human brain. Networks of artificial neurons analyze large datasets to automatically discover underlying patterns, without human intervention. Deep learning excels at identifying patterns in unstructured data, which most people know as media such as medical images, sound, video, and text. This paper talks about using deep learning networks to analyze medical imaging data such as X-rays and Magnetic Resonance Imaging (MRI) to increase diagnostic accuracy in less time and at reduced cost compared to traditional diagnostic methods.

Nirmala Singh

Papers

Big Data analytics

Internet of Things (IoT): Security challenges, business opportunities & reference architecture for E-commerce

Blockchain: Future of financial and cyber security

Containers & Docker: Emerging roles & future of Cloud technology

Object classification to analyze medical imaging data using deep learning