Big Data Phenomenon Research Articles

A distributed evolutionary multivariate discretizer for Big Data processing on Apache Spark

Abstract Nowadays the phenomenon of Big Data is overwhelming our capacity to extract relevant knowledge through classical machine learning techniques. Discretization (as part of data reduction) is presented as a real solution to reduce this complexity. However, standard discretizers are not designed to perform well with such amounts of data. This paper proposes a distributed discretization algorithm for Big Data analytics based on evolutionary optimization. After comparing with a distributed discretizer based on the Minimum Description Length Principle, we have found that our solution yields more accurate and simpler solutions in reasonable time.

Big Data Regulatory Debates in the EU

In the phenomenon of big data intertwined with cloud services, the traditional way of collecting, processing and storing data has been changing. This new phenomenon challenges traditional legal principles and increases legal uncertainty of various rights protection in the information society. Such challenges have become greater and greater in response to the emerging technologies embedded with machine intelligence. This paper seeks to remove legal obstacles in the era of big data by defining big data from a legal perspective and finding solutions to the key legal challenges of big data such as data privacy and security; intellectual property protection and jurisdictional issues. It proposes a feasible interpretation and application of the traditional concepts to the new big data phenomenon. Overall, it intends to contribute to the review and reform of the current regulatory framework of the data-driven society in the EU and promote legal certainty and a healthy economic and societal growth across the globe.

More than just a resource?Theoretical foundations for the big data phenomenon

The big data phenomenon is now recognized as far more than a business fad, and it has become an important element of firm investment in the modern environment. Yet, the strategy scholars have largely ignored the ever increasing role that big data, and those involved in the firm’s data activities, play in strategic decision making and processes at many firms. This article begins the discussion of how to investigate and systematically understand the transformative effect that big data is playing by looking at how the characteristics of big data (the 8 Vs) connect to two significant schools of thought in strategy: Resource-based Theory and organizational learning. The authors then develop four avenues for further investigation into this phenomenon.

Law and Legal Science in the Age of Big Data

The paper aims to contribute to the understanding of the connection of law and legal science, on the one hand, and the Big Data phenomenon, on the other. The connection of Big Data and law can be thematised in several ways. This article makes a distinction whereby there are two levels of interplay between Big Data and the law (and legal science). Big Data on the one hand can be the subject of legal regulation and legal science, but it also can be a tool for better, ‘predictive’ law making and lawyering. This latter is also true for legal science: Big Data opens a whole range of possibilities as a new tool. Thus, this article discusses three fields and questions in three sections: 1. Big Data as the subject of legal regulation. What kind of moral questions does Big Data, and the predictive potential it has, raise? How does law recently frame, define and regulate the Big Data phenomenon? How does Big Data affect existing legal framework rules regarding privacy, data protection, competition, business regulatory, etc.? What will the new rules, regulating Big Data look like? 2. Big Data as a tool in the regulator’s and the lawyer’s hand. How can we exploit the new possibilities provided by Big Data in law making, policy creation and the application of law? How can we design new ways of ‘Big Data-based social engineering’? How can we create predictive tools and inferencing techniques based on Big Data in policing, law enforcement and litigation? Finally in part 3. I discuss the impact of Big Data on legal science. How can Big Data, as a research tool help legal science? How do we use legal data-sets and textual corpuses as BD? How will these ‘super-empirical’ research methods affect legal scholarship? What is the relationship between traditional doctrinal scholarship and the new types of BD-based research? How can we use statistical analysis, natural language processing, content analysis, machine learning, behavioural prediction, etc. in legal science?

Big Data and corporate reporting: impacts and paradoxes

PurposeThe purpose of this paper is to investigate the phenomenon of Big Data and corporate reporting, and to determine the impact of Big Data and the current Big Data state of mind with regard to corporate reporting, what accountant and non-accountant participants’ perceptions are of the phenomenon, what the accountants’ role is and will be in this regard, and what opportunities and risks are associated with Big Data and corporate reporting. Furthermore, this study seeks to identify the inherent technological paradoxes of Big Data and corporate reporting.Design/methodology/approachThe current study is qualitative in nature and assumes an interpretive stance, investigating participants’ perceptions of the phenomenon of Big Data and corporate reporting. To this end, interview data from 25 participants, video and text material, were analysed to enhance and triangulate findings. A four-fold sampling strategy was employed to ensure that any collected data would contribute to the findings. Data were analysed on the basis of open and selective coding stages. Data collection and analysis took place in two stages, in 2014 and in 2016.FindingsThree topics, or categories, emerged from the data analysis, which have sufficient explanatory power to illustrate the phenomenon of Big Data and corporate reporting, namely the Big Data state of mind and corporate reporting, accountants’ role and future related to Big Data, and perceived opportunities and risks of Big Data. Features of a new approach to corporate reporting were identified and discussed. Furthermore, four paradoxes emerged to express inherent opposing positions of Big Data and corporate reporting, namely empowerment vs enslavement, fulfilling vs creating needs, reliability vs timeliness and simplicity vs complexity.Originality/valueThe original contribution of the study lies in the empirical investigation of the phenomenon of Big Data and corporate reporting as one of the most recent and praised developments in the accounting context. The dual communication flows of corporate reporting with Big Data is an important element of the findings, which can enhance the prospective financial statements significantly. Finally, technological paradoxes of Big Data and corporate reporting are discussed for the first time, two of which are based on the literature and the remaining two are inherent in the phenomenon of Big Data and corporate reporting.

Big data and privacy: The study of privacy invasion acceptance in the world of big data

The phenomenon of big data includes technological (new opportunities), business (application), and social aspect. The social aspect applies to the social consequences of the use of big data methods, in particular, those related to the processing of personal and other private data , as well as the danger of privacy violation. In the context of the big data phenomenon, this study presents the results of a survey on the level of acceptance of privacy violation resulting from mass data processing. The different objectives of processing were taken into account, including general, social and commercial. This study helps to draw conclusions concerning commercial and non-commercial use of private data, as well as the legal regulations on personal data processing.

Big Data is watching YOU: opportunities and challenges from the perspective of young adult consumers in Russia

ABSTRACTThe extent to which Big Data technologies are integrated into a society is a reflection of national progress, showcasing political and economic modernity. In the data-driven world, individuals are required to divulge private and financial information in order to ‘consume’ the Internet. While sharing information is a tolerated yet undesirable facet of modern society, netizens are alarmed by the recent release to third parties of sensitive personal data. The (mis)use of Big Data is affecting perceptions of new technologies by heightening uncertainty. Using a mixed methods approach, the authors explore the factors that influence consumer perceptions of Big Data, from the perspective of the individual Web user in an overtly censored society. The study contributes the literature on the Big Data phenomenon by presenting the viewpoint of the individual.

Big data and learning analytics: Singular or plural?

Recent critiques of both the uses of and discourse surrounding big data have raised important questions as to the extent to which big data and big data techniques should be embraced. However, while the context-dependence of data has been recognized, there remains a tendency among social theorists and other commentators to treat certain aspects of the big data phenomenon, including not only the data but also the methods and tools used to move from data as database to data that can be interpreted and assigned meaning, in a homogenizing way. In this paper, we seek to challenge this tendency, and to explore the ways in which explicit consideration of the plurality of big data might inform particular instances of its exploitation. We compare one currently popular big data-inspired innovation — learning analytics — with three other big data contexts — the physical sciences, business intelligence and public health. Through these comparisons, we highlight some dangers of learning analytics implemented without substantial theoretical, ethical and design effort. In so doing, we also highlight just how plural data, analytical approaches and intentions are, and suggest that each new big data context needs to be recognized in its own singularity.

Большие данные в биоинформатике

Sequencing of the human genome began in 1994. It took 10 years of collaborative work of many research groups from different countries in order to provide a draft of the human DNA. Modern technologies allow sequencing of a whole genome in a few days. We discuss here the advances in modern bioinformatics related to the emergence of high-performance sequencing platforms, which not only contributed to the expansion of capabilities of biology and related sciences, but also gave rise to the phenomenon of Big Data in biology. The necessity for development of new technologies and methods for organization of storage, management, analysis and visualization of big data is substantiated. Modern bioinformatics is facing not only the problem of processing enormous volumes of heterogeneous data, but also a variety of methods of interpretation and presentation of the results, the simultaneous existence of various software tools and data formats. The ways of solving the arising challenges are discussed, in particular by using experiences from other areas of modern life, such as web and business intelligence. The former is the area of scientific research and development that explores the roles and makes use of artificial intelligence and information technology (IT) for new products, services and frameworks that are empowered by the World Wide Web; the latter is the domain of IT, which addresses the issues of decision-making. New database management systems, other than relational ones, will help solve the problem of storing huge data and providing an acceptable timescale for performing search queries. New programming technologies, such as generic programming and visual programming, are designed to solve the problem of the diversity of genomic data formats and to provide the ability to quickly create one's own scripts for data processing.

Entering the 'big data' era in medicinal chemistry: molecular promiscuity analysis revisited.

The ‘big data’ concept plays an increasingly important role in many scientific fields. Big data involves more than unprecedentedly large volumes of data that become available. Different criteria characterizing big data must be carefully considered in computational data mining, as we discuss herein focusing on medicinal chemistry. This is a scientific discipline where big data is beginning to emerge and provide new opportunities. For example, the ability of many drugs to specifically interact with multiple targets, termed promiscuity, forms the molecular basis of polypharmacology, a hot topic in drug discovery. Compound promiscuity analysis is an area that is much influenced by big data phenomena. Different results are obtained depending on chosen data selection and confidence criteria, as we also demonstrate.

Big data dreams: A framework for corporate strategy

The phenomenon of big data—large, diverse, complex, and/or longitudinal data sets—is having a stark influence on organizational strategy making. An increase in levels of data and technological capabilities is redefining innovation, competition, and productivity. This article contributes to both practical strategic application and academic research in the strategic management domain by presenting a framework that identifies how big data improves functional capabilities within organizations, shapes entirely new industries, and is a key component of innovative and disruptive strategies used by learning organizations to diversify and break down barriers of traditionally defined industries. This framework provides an appropriate basis for internal corporate strategy discussions that surround big data investments by explaining how firms create value through various approaches. In addition, we offer guidance for how firms might derive their own big data approach through the merits of aligning data strategy aspirations with data strategy authenticity.

Current Issue on Knowledge Management System for future research: a Systematic Literature Review

Nowadays, the number of papers on the topic of Knowledge Management and Knowledge Management System is still widely discussed. The study of Knowledge Management System (KMS) issues are based on Systematic Literature Review (SLR). It aims to analyze the state of the art, identify current popular issues on KMS, and offer directions for future research agenda. The methodology used in this paper is based on the systematic literature review to collect, synthesize and analyze numerous papers on a variety of topics that are closely related to knowledge management system issues that published in the last two decades. Based on fifty-four papers reviewed from six electronic databases, the result of this paper obtained fourteen current issues on knowledge management system. Moreover, the top three popular issues consist of the development of capabilities and features of KMS, Big Data issues on KMS, and adoption to new technology issue for KMS respectively. The conclusion of this study emphasized the big data phenomenon as the most contemporary topic for the future research area besides the growing of required KMS capabilities and features development.

Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review

With the widespread use of healthcare information systems commonly known as electronic health records, there is significant scope for improving the way healthcare is delivered by resorting to the power of big data. This has made data mining and predictive analytics an important tool for healthcare decision making. The literature has reported attempts for knowledge discovery from the big data to improve the delivery of healthcare services, however, there appears no attempt for assessing and synthesizing the available information on how the big data phenomenon has contributed to better outcomes for the delivery of healthcare services. This paper aims to achieve this by systematically reviewing the existing body of knowledge to categorize and evaluate the reported studies on healthcare operations and data mining frameworks. The outcome of this study is useful as a reference for the practitioners and as a research platform for the academia.

Leveraging Frontline Employees’ Small Data and Firm-Level Big Data in Frontline Management

The advent of new forms of data, modern technology, and advanced data analytics offer service providers both opportunities and risks. This article builds on the phenomenon of big data and offers an integrative conceptual framework that captures not only the benefits but also the costs of big data for managing the frontline employee (FLE)-customer interaction. Along the positive path, the framework explains how the “3Vs” of big data (volume, velocity, and variety) have the potential to improve service quality and reduce service costs by influencing big data value and organizational change at the firm and FLE levels. However, the 3Vs of big data also increase big data veracity, which casts doubt about the value of big data. The authors further propose that because of heterogeneity in big data absorptive capacities at the firm level, the costs of adopting big data in FLE management may outweigh the benefits. Finally, while FLEs can benefit from big data, extracting knowledge from such data does not discount knowledge derived from FLEs’ small data. Rather, combining and integrating the firm’s big data with FLEs’ small data are crucial to absorbing and applying big data knowledge. An agenda for future research concludes.

Big Data Methods

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.

Big Data: las implicancias de la democratización de los métodos. Oportunidades y desafíos que los dispositivos y datos digitales plantean para la investigación social

ResumenEl presente ensayo monográfico intenta brindar una panorámica globaldel fenómeno del Big Data, entendido como un sistema de producción,procesamiento, sistematización y análisis de datos sociales a gran escala.Actualmente, una multiplicidad de entes se han posicionado como agentesrecopiladores de datos sobre conductas y prácticas sociales, gracias aavances tecnológicos que han incrementado las capacidades sistémicas parala obtención y análisis de grandes volúmenes de información. Esto suponeuna suerte de democratización de los métodos, lo cual plantea una serie dedesafíos y oportunidades para los cientistas sociales, otrora agentes exclusivosde la producción de lo social. Ante esto, el presente documento ofrece unmarco general de análisis, esbozando reflexiones temáticas y estratégicaspara la comunidad científica en general y para la sociología en particular. Elensayo se estructura a partir de tres secciones: una introducción, que ofreceun acercamiento global al tema; una sección de discusión, en la que se revisaa grandes rasgos las principales problemáticas y desafíos que presenta elBig Data para la investigación social y los científicos sociales, y una secciónde conclusiones, en la que se esbozan posibles alternativas de integraciónmetodológica entre las nuevas formas de investigación social y los métodostradicionales.Palabras clave: Big Data, tecnologías de la información, métodos deinvestigación, investigación social, empirismo.Big Data: Implications of the democratization ofmethods.Opportunities and challenges for social researchprovided by digital data and devicesAbstractThis monographic essay attempts to provide a comprehensive overviewof the phenomenon of Big Data, understood as a system of production,processing, systematization and analysis of large-scale social data. Currently,a multiplicity of entities have positioned themselves as agents collectingdata, behaviors and social practices, thanks to technological advancesthat have increased systemic capacities for collection and analysis of largevolumes of information. This is a sort of democratization of methods, whichposes a number of challenges and opportunities for social scientists, onceexclusive agents of social production. In this way, the present documentprovides a general framework for analysis outlining thematic and strategicconsiderations for the scientific community in general and sociology inparticular. The essay is structured in three sections: an introduction, whichprovides a comprehensive approach to the subject; a section of discussion,which reviews in broad terms the main problems and challenges imposed byBig Data for social research and social scientists; and a section of conclusionswith possible alternatives for methodological integration among new formsof social research and the traditional methods.Keywords: Big Data, information technology, research methods, socialresearch, empiricism.Big Data: as implicações da democratização dosmétodos.Oportunidades e desafios que os dispositivos edados digitais projetam para a pesquisa socialResumoO presente ensaio monográfico tenta oferecer uma visão panorâmicado fenómeno do Big Data, entendido como um sistema de produção,processamento, sistematização e análise dos dados sociais em grandeescala. Atualmente, uma multiplicidade de entidades têm se posicionadocomo agentes coletores de dados sobre comportamentos e práticas sociais,graças aos avanços tecnológicos que aumentaram as capacidades sistêmicaspara a obtenção e análise de grandes volumes de informação. Esta é umaespécie de democratização dos métodos, o que coloca uma série de desafiose oportunidades para os cientistas sociais, pois em outros tempos foram osagentes exclusivos de produção social. Diante disso, o presente documentoapresenta um quadro geral de análise, delineando reflexões temáticase estratégicas para a comunidade científica em geral e para a sociologiaem particular. O ensaio é estruturado em três seções: uma introdução,que oferece uma abordagem global para o tema; uma seção de discussão, onde é revistada em termos gerais, os principais problemas e desafios queapresenta a Big Data para a investigação social e os cientistas sociais, e umaseção de conclusões onde se esboçam possíveis alternativas de integraçãometodológica entre as novas formas de investigação social e os métodostradicionais.Palavras-chave: Big Data, tecnologias da informação, métodos de pesquisa,pesquisa social, empirismo.

Big Data, Online Reputation and Knowledge Management in Higher Education Online

Summary: The progressive digitalization of our society has also transformed the online training. The new platforms offer more features, favour a tailored education and are compatible with the working life of the student. From the servers it is possible to measure the rate of interaction and the student’s dropout rates. For this reason, the phenomenon of Big Data requires a rethinking of the concept of reputation based on three ideas: transparency, trust and digital identity of the educational organization. This article analyzes the case of the School of Communication at the Istmo University (UNIS) in Guatemala. The use of Blackboard platform improved learning and interaction in those students. It is confirmed that the e-learning platform known as Blackboard facilitates learning and teacher-student interaction. This tool collects and updates data throughout the process, so that the educational organization can monitor the degree of interaction and student dropout.

Application of Teaching Quality Assessment Based on Parallel Genetic Support Vector Algorithm in the Cloud Computing Teaching System

With the rapid development of China's economy in recent years, the scale of students has expanded gradually, which has led to many new problems, including the problems of the quality and the quantity of teachers, and the teaching facilities being insufficient. The assessment of teaching quality is one of the most important aspects of teaching management, which come to the attention of every university. Therefore, it has become the current focus in the research of university teaching. At the same time, the traditional method of teaching quality assessment has not been able to deal with the phenomenon of big data in the field of education. As a new technology, cloud computing provides a broad space for the development of a new model in the aspects of hardware environment construction, software resource development, network teaching implementation and personal knowledge management. In order to effectively deal with the challenges of big data processing in the field of education, this paper proposes a GA-SVM teaching quality assessment algorithm which is based on MapReduce. Through the design of a map function and reduce function, this paper realizes the parallelization of the GA-SVM algorithm and the selection of the main parameters. Secondly, this paper uses a genetic algorithm to optimize the penalty coefficient and kernel parameters of SVM, and then solves the problem of difficulty in determining the parameters of support vectors. In addition, we improve the sensitivity of the search through the method of logarithmic transformation, and speed up the convergence rate of the GA model. Finally, we compare the parallel algorithm and the serial algorithm on the Hadoop platform. The results of experiments show that the GA-SVM based on MapReduce is suitable for teaching quality assessment under the environment of big data.

To do more, better, faster and more cheaply: using big data in public administration

Big data have become a game-changer for modern public administration in those areas in which they are used. Although their application is still limited in the public sector, their use develops dynamically in areas where they bring tangible results in terms of efficiency and efficacy. This article presents the concept of big data, outlines the possibilities for using big data in the public sector and methods of their utilization, highlights cases where these have been implemented, along with the results. This article discusses applications of big data methods in public policy design and implementation and in public authority internal management. It includes a summary of the benefits, disadvantages and challenges related to utilization of big data. The article also briefly addresses historical, current and predictive approaches. Research was carried out using internet-based case analysis and is limited by the confidentiality of details relating to big data methods. Although the research has had to be limited to publicly available descriptions, the findings make it possible to understand the big data phenomenon in public administration and to draw general lessons from their use. Points for practitioners Big data is a contemporary phenomenon. Properly utilized it brings astonishingly positive outcomes for public administration in terms of its efficacy, efficiency, and overall client satisfaction. These benefits are a result of significant increase in the accuracy of decision-making, a significant acceleration of performance of internal the ‘information task’ and a significant reduction in operating costs related to the decision-making process. This is possible thanks to the digitization of human life and IT developments applied in a specific way for public administration – the way of big data methods where huge amounts of data are processed in a form of reasoning by powerful IT technology to present information that helps public administration to better perform its task.

Spatial data extension for Cassandra NoSQL database

The big data phenomenon is becoming a fact. Continuous increase of digitization and connecting devices to Internet are making current solutions and services smarter, richer and more personalized. The emergence of the NoSQL databases, like Cassandra, with their massive scalability and high availability encourages us to investigate the management of the stored data within such storage system. In our present work, we harness the geohashing technique to enable spatial queries as extension to Cassandra query language capabilities while preserving the native syntax. The developed framework showed the feasibility of this approach where basic spatial queries are underpinned and the query response time is reduced by up to 70 times for a fairly large area.