CRITICAL QUESTIONS FOR BIG DATA

The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and many others are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing information from Twitter, Google, Verizon, 23andMe, Facebook, Wikipedia, and every space where large groups of people leave digital traces and deposit data. Significant questions emerge. Will large-scale analysis of DNA help cure diseases? Or will it usher in a new wave of medical inequality? Will data analytics help make people’s access to information more efficient and effective? Or will it be used to track protesters in the streets of major cities? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Some or all of the above?This essay offers six provocations that we hope can spark conversations about the issues of Big Data. Given the rise of Big Data as both a phenomenon and a methodological persuasion, we believe that it is time to start critically interrogating this phenomenon, its assumptions, and its biases.(This paper was presented at Oxford Internet Institute’s “A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society” on September 21, 2011.)

The phenomenon now commonly referred to as “Big Data” holds great promise and opportunity as a potential source of solutions to many societal ills ranging from cancer to terrorism; but it might also end up as “…a troubling manifestation of Big Brother, enabling invasions of privacy, decreased civil freedoms (and) increased state and corporate control” (Boyd & Crawford, 2012, p. 664). Discussions about the use of Big Data are widespread as “(d)iverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people” (Boyd & Crawford, 2012, p. 662). This chapter attempts to establish guidelines for the discussion and analysis of ethical issues related to Big Data in research, particularly with respect to privacy. In doing so, it adds new dimensions to the agenda setting goal of this volume. It is intended to help researchers in all fields, as well as policy-makers, to articulate their concerns in an organized way, and to specify relevant issues for discussion, policy-making and action with respect to the ethics of Big Data. On the basis of our review of scholarly literature and our own investigations with big and small data, we have come to recognize that privacy and the great potential for privacy violations constitute major concerns in the debate about Big Data. Furthermore, our approach and our recommendations are generalizable to other ethical considerations inherent in Big Data as we illustrate in the final section of the chapter.

It is estimated that, by 2020, 40 zettabytes of data will be created. The convergence of pervasive sensing with locationaware and social media technologies, along with infrastructure-based sensors, will lead to the production and collection of “big data” in many areas such as transportation, healthcare, and energy. For example, today, there are 6 billion cell phone users in the world. Cell phones equipped with multiple sensors are producing large volumes of data each day. The data obtained may be structured or unstructured, ranging from GPS trajectories to text, video, still images, and others. This opens up new challenges and opportunities to address the key aspects of sensor-based big data, namely, volume, velocity, variety, and veracity. This special issue aims to foster the dissemination of knowledge for advanced issues in big data management and analytics for ubiquitous sensors. This special issue will be an open international forum for researchers to summarize their latest research results. The call for papers included a number of related topics such as distributed/parallel processing of streaming data, privacy protection and security issues in sensor-based big data, and data fusion techniques for distributed big data. The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. The paper by I. Ha et al. proposes a parallel approach usingMapReduce for sentiment analysis of big data in social media. The paper by Y. Yu et al. presents a parallel approach using Hadoop for density-based clustering of big data. The paper by K. Omote and T. P. Thao describes a light-weight network coding scheme to provide integrity of the data when stored in cloud servers. The paper by H.-J. Jo and J. W. Yoon presents a countermeasure to prevent bruteforce attacks in high-performance computing platforms for big data analytics. The papers by H. Kang et al. and Y. Ki et al. propose new analysis-based approaches to detect malware in mobile/smart devices. Finally, the paper by S.-W. Jang G.Y. Kim presents a multiple feature-based image switching strategy in visual sensor networks.

Addressing the policy challenges and opportunities of “Big data”

Although smart meter data analytics has received extensive attention and rich literature studies related to this area have been published, developments in computer science and the energy system itself will certainly lead to new problems or opportunities. In this chapter, we discuss some research trends for smart meter data analytics, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security. By the end of this book, we hope this chapter can help readers identify new issues and works on smart meter data analytics in the future smart grid.

Ask a Political Scientist: A Conversation with Catharine A. MacKinnon about Power, Politics, and Political Science

The fields of Astrostatistics and Astroinformatics are vital for dealing with the big data issues now faced by astronomy. Like other disciplines in the big data era, astronomy has many V characteristics. In this paper, we list the different data mining algorithms used in astronomy, along with data mining software and tools related to astronomical applications. We present SDSS, a project often referred to by other astronomical projects, as the most successful sky survey in the history of astronomy and describe the factors influencing its success. We also discuss the success of Astrostatistics and Astroinformatics organizations and the conferences and summer schools on these issues that are held annually. All the above indicates that astronomers and scientists from other areas are ready to face the challenges and opportunities provided by massive data volume.

Risks exist in every aspect of our lives, and can mean different things to different people, while negatively in general they always cause a great deal of potential damage and inconvenience for the enterprise stakeholders. Investigation of risk analytics tools in today’s big data era is beneficial to both practitioners and academic researchers from industrial systems. The current special issue provides some view of how risk-based business intelligence can be applied to industrial systems faced with big data issues.

With the advancement of technology in data science and network technology, the world has stepped into the Era of Big Data, and the medical field is rich in data suitable for analysis. Thus, in recent years, there has been much research in medical big data, mainly targeting data collection, data analysis, and visualization. However, very few works provide a full survey of the medical big data on chronic diseases and health monitoring. This review investigates recent research efforts and conducts a comprehensive overview of the work on medical big data, especially as related to chronic diseases and health monitoring. It focuses on the full cycles of the big data processing, which includes medical big data preprocessing, big data tools and algorithms, big data visualization, and security issues in big data. It also attempts to combine common big data technologies with special medical needs by analyzing in detail existing works of medical big data. To the best of our knowledge, this is the first survey that targets chronic diseases and health monitoring big data technologies.

Correlates of the internal audit function’s use of data analytics in the big data era: Global evidence

Data envelopment analysis (DEA) is a tool for identifying best-practices when multiple performance metrics or measures are present for decision-making units (DMUs). As big data issue becomes an important area of supply chain and operations management, DEA is evolving into a data-oriented data science tool for benchmarking, performance evaluation, composite index construction and others. As the number of DMUs increases, the running-time to solve the standard DEA model sharply rises. Such situations are appearing more frequently in the era of big data. This issue could be an important challenge particularly when real-time data stream in at extremely high rates and the DEA analysis needs to be performed very quickly. Therefore, there exist practical needs for developing an efficient way of solving large-scale DEA problems. In this paper, we propose a practical approach for speeding up the DEA efficiency estimation process based on machine learning. In this approach, a sample of DMUs is selected from the population as a training data set, based on which a machine is trained to predict the efficiency scores of unselected or newly streamed-in DMUs. We also suggest a data augmentation technique to enhance the learning process under severe data class imbalance. The superior performance of the proposed approach over the conventional one in terms of efficiency prediction power as well as model computation time is shown through a series of computational experiments using randomly generated data.

The issue of the supply gap of public goods (PG) between urban and rural (UR) areas is a subject of great socio-economic significance and practical research value. To a large extent, it is related to the smooth progress of my country’s socialist market economy. The gap between the supply and demand of UR PG can be well analyzed by applying data mining (DM) technology in the era of big data. Due to the unbalanced supply of UR PG, this paper studies the distribution gap of UR PG through DM. This article mainly uses experimental, comparative, and survey methods to compare and analyze the urban-rural income gap and uses modern technology to describe the relationship between the supply of PG and income. Experimental results show that the UR income gap ratio is above 2, and the gap is still large. Therefore, for the supply of PG, there are also certain differences between UR areas.

Purpose This paper aims to examine how big data can be applied in accounting and identifies the competences future accountants at organizational level need to possess to remain competitive. Design/methodology/approach This study used the association of chartered certified accountants (ACCA’s) framework and the technological, organization and environmental (TOE) theoretical framework to identify the skills and capabilities urgently needed by accounting professionals at the organizational level in the context of big data analytics. The authors conducted comprehensive content analysis of academic literature issues from professional accounting bodies, and reports of accounting practitioners to gain insights of competencies that accounting professionals at organizational level need to possess to be prominent in the big data era. Findings The findings show that in the big data era accounting professionals should acquire the seven competences to be competitive. The competencies are (1) skills in data analytic techniques and the use of statistical models; (2) knowledge of the business; (3) skills to control the risk from the emerging technologies; (4) skills and knowledge in accounting profession; (5) skills to understand the insights from big data analytics; (6) skills to test the quality, veracity and integrity of data; and (7) skills to communicate with others. The study also found that these competences do not exist alone. These competences are closely related to each other and complement each other, which means that accounting professionals cannot expect to stand out in the future competition by mastering only one or two of these seven competences. Research limitations/implications The application of big data analytics is still in its infancy and its effects have yet to be confirmed so that there is no reliable case to collect and analyse. One limitation is the absence of practical case in this paper. The findings will help accounting professionals to understand the future of work. Practical implications The findings will help accounting professionals to acquire new skills and knowledge in related areas and remain competitive in the post big data era. Social implications This study highlights skills necessary for accountants to thrive in a big data era, emphasizing the shift from traditional accounting functions to roles that integrate advanced data capabilities and strategic insight. Originality/value This is one of the few papers that seek to explore the competencies required by accounting professional at organizational level in the big data era using the ACCA’s research of accountant’s professional quotients and TOE theoretical framework. This paper seeks to contribute to the literature on accounting professionals’ competencies at organizational level in the big data era. Big data analytics in accounting is influencing the evolution of accounting professionals’ competencies and is a key interest in accounting change literature. The nature of accounting work will undergo fundamental changes due to big data. Yet we know little about this due to big data being in its infancy stage in the accounting literature.

Big Data, a popular term emerged with the trend to larger datasets, refers to a collection of datasets so large or complex that it becomes difficult to process using regular database management tools or traditional data processing applications (IDC, 2011; White, 2012; MIKE 2.0). Big Data is posing big challenges and opportunities for every field of the human societies as the Big Data era has arrived with the exponential growth, availability, and use of data and information (The Economist, 2010; Reichman, 2011). Big Data does not have to be necessarily big in data size. Big Data issues can be raised either as data volume gets so large and varied (big size of data), or datasets come in all types of formats and from many different sources (high-degree complexity of data), or data are produced and must be processed fast (e.g., near real-time data producing and processing) to meet the demand of information (high velocity of data). The recent discussions on Big Data and how to utilize it as the basis for innovation, differentiation, and growth reflect a wide recognition of Big Data opportunities and challenges.

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