Definition Of Big Data Research Articles

Big Data and the Little Big Bang: An Epistemological (R)evolution.

Starting from an analysis of frequently employed definitions of big data, it will be argued that, to overcome the intrinsic weaknesses of big data, it is more appropriate to define the object in relational terms. The excessive emphasis on volume and technological aspects of big data, derived from their current definitions, combined with neglected epistemological issues gave birth to an objectivistic rhetoric surrounding big data as implicitly neutral, omni-comprehensive, and theory-free. This rhetoric contradicts the empirical reality that embraces big data: (1) data collection is not neutral nor objective; (2) exhaustivity is a mathematical limit; and (3) interpretation and knowledge production remain both theoretically informed and subjective. Addressing these issues, big data will be interpreted as a methodological revolution carried over by evolutionary processes in technology and epistemology. By distinguishing between forms of nominal and actual access, we claim that big data promoted a new digital divide changing stakeholders, gatekeepers, and the basic rules of knowledge discovery by radically shaping the power dynamics involved in the processes of production and analysis of data.

Data Science and Big Data Technologies Role in the Digital Economy

This article explores the role of Data Science and Big Data technology in the modern digital economy. The author states that large and medium companies from retail trade and service sector show increased interest in using them. These technologies are actively used by banks, mobile operators and large manufacturing companies to analyze data on equipment failures and to reduce downtime, which allows reducing costs. The role of Big Data technology is to be a liquid product and a necessary condition to increase the profitability of enterprises through personalized customer service and predictive analytics. For today's Russian digital economy, it is very important to legalize a single definition of Big Data and to achieve the emergence of special data exchanges.

Toward Data Sense-Making in Digital Health Communication Research: Why Theory Matters in the Age of Big Data

The rapidly increasing volume of health data generated from digital technologies have ushered in an unprecedented opportunity for health research. Despite its promises, big data approaches in understanding human behavior often do not consider conceptual premises that provide meaning to social and behavioral data. In this paper, we update the definition of big data, and review different types and sources of health data that researchers need to grapple with. We highlight three problems in big data approaches — data deluge, data hubris, and data opacity — that are associated with the blind use of computational analysis. Third, we lay out the importance of cultivating health data sense-making—the ability to integrate theory-led and data-driven approaches to process different types of health data and translating findings into tangible health outcomes — and illustrate how theorizing can matter in the age of big data.

What is your definition of Big Data? Researchers' understanding of the phenomenon of the decade.

The term Big Data is commonly used to describe a range of different concepts: from the collection and aggregation of vast amounts of data, to a plethora of advanced digital techniques designed to reveal patterns related to human behavior. In spite of its widespread use, the term is still loaded with conceptual vagueness. The aim of this study is to examine the understanding of the meaning of Big Data from the perspectives of researchers in the fields of psychology and sociology in order to examine whether researchers consider currently existing definitions to be adequate and investigate if a standard discipline centric definition is possible.MethodsThirty-nine interviews were performed with Swiss and American researchers involved in Big Data research in relevant fields. The interviews were analyzed using thematic coding.ResultsNo univocal definition of Big Data was found among the respondents and many participants admitted uncertainty towards giving a definition of Big Data. A few participants described Big Data with the traditional “Vs” definition—although they could not agree on the number of Vs. However, most of the researchers preferred a more practical definition, linking it to processes such as data collection and data processing.ConclusionThe study identified an overall uncertainty or uneasiness among researchers towards the use of the term Big Data which might derive from the tendency to recognize Big Data as a shifting and evolving cultural phenomenon. Moreover, the currently enacted use of the term as a hyped-up buzzword might further aggravate the conceptual vagueness of Big Data.

Big Data− Análisis informétrico de documentos indexados en Scopus y Web of Science

El fenómeno Big Data es reciente, como lo demuestran las escasas publicaciones sobre el tema, lo cual incentiva esta investigación cuyos objetivos son compilar y referenciar documentos académicos incluidos en las bases de datos Scopus y Web of Science y analizar los contenidos. El método empleado es la investigación descriptiva, de primera aproximación, que consistió en la búsqueda y recuperación de información en Scopus y Web of Science en el periodo 2008-2018. Se analizaron 39 documentos, los cuales corresponden a 70 autores distribuidos en 14 títulos de revistas científicas, cuyo tipo de contribución se distribuye en 19 artículos, 10 comentarios, seis cartas al editor y cuatro reseñas. Otro de los resultados relevantes es que hay una alta concentración de publicaciones en Science y Nature. Los fenómenos Big Data y la CI son de reciente cuño y se encuentran en redefiniciones y conformaciones de dominios de estudios constantes. Encontramos un interés por las investigaciones Big Data; por otra parte, después de un análisis conceptual, proponemos una definición de Big Data.

A Brief Survey on Big Data in Healthcare

This article presents a brief introduction to big data and big data analytics and also their roles in the healthcare system. A definite range of scientific researches about big data analytics in the healthcare system have been reviewed. The definition of big data, the components of big data, medical big data sources, used big data technologies in present, and big data analytics in healthcare have been examined under the different titles. Also, the historical development process of big data analytics has been mentioned. As a known big data analytics technology, Apache Hadoop technology and its core components with tools have been explained briefly. Moreover, a glance of some of the big data analytics tools or platforms apart from Hadoop eco-system were given. The main goal is to help researchers or specialists with giving an opinion about the rising importance of used big data analytics in healthcare systems.

The Protection of Database Copyright in the Era of Big Data

The development of computer technology, mobile intercommunication, cloud computing and Internet of things embraces a limitless prospect for the application of big data. The paper reviews the definition of big data and summarizes the design features by proposing “the Five-Star Feature Model”. The paper then takes the protection of English corpus constructed in China as one special kind of big data to demonstrate the necessity of establishing a comprehensive protection legal system by revealing the setbacks of the Copyright Law in China. With reference to academic literature in China and other regulations in Western countries concerning copyright protection of database, the author finally recommends the complementary use of Law of the PRC against Unfair Competition for the commercial use of these corpora and further proposes to set a new special database right to guarantee the balance of copyright protection and public use of these big data.

Big data: issues, trends, problems, controversies in ASEAN perspective


 
 
 
 Big Data has a characteristics is size, new opportunities and have the potential to transform corporations and government and its interactions with the public. This paper attempts to offer a broader definition of Big Data that captures it is other unique and defining characteristics. This paper presents a consolidated description of Big Data by integrating definitions from practitioners and academics. In addition, we summarize the issues, trends, problems and controversies related to Big Data (technology, applications, and people) from infrastructure (i.e., hardware and software), technology for Big Data Analytics (BDA), management, educational and scientists, and government-related to policies perspectives in order to support the Economic Community ASEAN (AEC) era.
 
 
 

Challenges and Research Disputes and Tools in Big Data Analytics

Big Data is the era of data processing. Big Data is the Collate’s observer data sets that are complicated that traditional data-processing abilities.There are the various challenges include data analysis, capture the data, curation, search, sharing, stowage, transmission, visualization, and privacy violations. A large collections of petabytes of data is engendered day by day from the up-to-date information systems and digital era such as Internet of Things and cloud computing. Big data environs is used to attain, organize and analyse the numerous types of data. A large scale distributed file system which should be a fault tolerant, flexible and scalable. The term big data comes with the new challenges to input, process and output the data The technologies used by big data application to handle the massive data are Hadoop, Map Reduce, Pig, Apache Hive, No SQL and Spark. Initially, we extant the definition of big data and discuss big data challenges. Succeeding, The Propionate Paramour of Big Data Systems Models in the Into Prolonging Seam, Namely data Generation, data Assange, data Storage, and data Analytics. These four modules form a big data value chain. In accumulation, we present the prevalent Hadoop framework for addressing big data.

Big Data как источник социологической информации: пример анализа блога губернатора Петербурга

This article explores the possibilities of Big Data in forecasts and social networks as a source of information about society, as well as its role in security processes. The authors aim to study the possibility of obtaining socially significant information through Big Data, in particular, on one of the aspects of environmental safety. Fundamental sociological theories are considered, such as theories of the risk society by W. Beck and E. Giddens, as well as the information society (accounting for the peculiarities of the influence of the latest information technologies on society, including Big Data). Based on a number of studies systematizing approaches to the definition of Big Data, the authors derive their own. Using qualitative methods (in particular, content analysis), the authors analyzed the example of the incident-management application provided by the Committee on Informatization and Communications of the City of Saint Petersburg. The empirical base of the study includes 16,694 comments on the public page of the governor of St. Petersburg Alexander Beglov, obtained through a program in Python and using VK Api. The study analyzes user comments on environmental risks in the districts of St. Petersburg, identifies areas that are most exposed to environmental risks, as well as the sources of these risks and the subjects of responsibility for them. The results show that informal means of communication between representatives of the executive branch and citizens are of great interest as a source of sociological information investigated using algorithms and methods for analyzing Big Data. Such analysis helps in increasing security by reducing uncertainty and gaining new knowledge about society. On the other hand, the researchers have identified possible social risks associated with collecting, storing, and using Big Data. In particular, these are the risks of external interference with the application. In practice, these risks should be prevented and minimized by improving and testing applications by the developing company. Sociology, in turn, must consider this fact and avoid introducing undue errors in the research results.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

Big data in pharmacogenomics: current applications, perspectives and pitfalls.

The efficiency of new generation sequencing methods and the reduction of their cost has led pharmacogenomics to gradually supplant pharmacogenetics, leading to new applications in personalized medicine along with new perspectives in drug design or identification of drug response factors. The amount of data generated in genomics fits the definition of big data, and need a specific bioinformatics processing following standard steps: data collection, processing, analysis and interpretation. Pitfalls of pharmacogenomics studies are directly related to these steps. This review aims to describe these steps from a pharmacogenomic point of view, focusing on bioinformatics aspects.

Command and Control System Construction in Big Data Era

The definition and characteristics of big data are summarized. From the era of big data, the production of command and control system data leads to 3 issues of data storage and processing, situation generation and decision-making, and data visualization. It takes the construction of a big data center and discusses the basics. The accusation framework and the coping strategies adopted by cloud computing under big data conditions provide reference for the application of big data technology in command and control systems, and point out the development trend of the next instruction system.

A Delphi study to build consensus on the definition and use of big data in obesity research

Background‘Big data’ has great potential to help address the global health challenge of obesity. However, lack of clarity with regard to the definition of big data and frameworks for effectively using big data in the context of obesity research may be hindering progress. The aim of this study was to establish agreed approaches for the use of big data in obesity-related research.MethodsA Delphi method of consensus development was used, comprising three survey rounds. In Round 1, participants were asked to rate agreement/disagreement with 77 statements across seven domains relating to definitions of, and approaches to, using big data in the context of obesity research. Participants were also asked to contribute further ideas in relation to these topics, which were incorporated as new statements (n = 8) in Round 2. In Rounds 2 and 3 participants re-appraised their ratings in view of the group consensus.ResultsNinety-six experts active in obesity-related research were invited to participate. Of these, 36/96 completed Round 1 (37.5% response rate), 29/36 completed Round 2 (80.6% response rate) and 26/29 completed Round 3 (89.7% response rate). Consensus (defined as > 70% agreement) was achieved for 90.6% (n = 77) of statements, with 100% consensus achieved for the Definition of Big Data, Data Governance, and Quality and Inference domains.ConclusionsExperts agreed that big data was more nuanced than the oft-cited definition of ‘volume, variety and velocity’, and includes quantitative, qualitative, observational or intervention data from a range of sources that have been collected for research or other purposes. Experts repeatedly called for third party action, for example to develop frameworks for reporting and ethics, to clarify data governance requirements, to support training and skill development and to facilitate sharing of big data. Further advocacy will be required to encourage organisations to adopt these roles.

On Definition of BigData

The term Big Data (or BigData) is widely used in scientific, educational, and business literature; however, there does not exist a single definition that can be unreservedly called “canonical”. A careless use of Big Data term to promote commercial software further emphasizes the importance of this issue. In this paper, we have performed a review of definitions of Big Data and highlighted the principal features that are attributed to Big Data. We compared all these principal features with features of databases compiled using Edgar F. Codd’s publications, and showed that they are not unique and can also be attributed to the databases. Having studied C. Lynch original work, we proposed the definition of Big Data based on the so-called conservation institution. The key point of this definition is a shift from purely technical attitude towards public institutions. Since the current use of the Big Data term may lead to a loss of meaning. There is a need not only to spread out best practices but also to eliminate or minimize the use of dubious or misleading ones.

Opening the Black Box: Understanding the Science Behind Big Data and Predictive Analytics.

Big data, smart data, predictive analytics, and other similar terms are ubiquitous in the lay and scientific literature. However, despite the frequency of usage, these terms are often poorly understood, and evidence of their disruption to clinical care is hard to find. This article aims to address these issues by first defining and elucidating the term big data, exploring the ways in which modern medical data, both inside and outside the electronic medical record, meet the established definitions of big data. We then define the term smart data and discuss the transformations necessary to make big data into smart data. Finally, we examine the ways in which this transition from big to smart data will affect what we do in research, retrospective work, and ultimately patient care.

Big Data Analytics for Healthcare Organizations a Case Study of the Iraqi Healthcare Sector

The rapid development of the healthcare industry in recent years has yielded a huge output of clinical, medical, and public health data with immense and complex structures that cannot be processed by conventional technologies. Therefore, new technology must be developed to handle such massive data sets, appropriately called “Big Data,” for the analysis and discovery of hidden patterns with visualization. Big data analytics involves the discovery and visualization of hidden patterns, and the analysis of knowledge derived from the data to support decision making. In this paper, we present an overview the big data definition, its characteristics, and the advantages of big data analytics in healthcare. We also examine the current Iraqi healthcare system and highlight the major challenges facing the healthcare sector that cannot be handled by conventional technology for data analysis. In addition, we provide suggestions on overcoming these challenges via technologies using big data analytics. Consequently, we emphasize the value of employing big data analytics technology in the Iraqi healthcare sector. We address the main benefits and advantages that big data analytics can provide and propose a conceptual healthcare architectural framework based on big data technology to aid Iraqi healthcare organizations in shifting from traditional analysis to big data analytics. The proposed health architectural framework for the Iraqi healthcare sector, includes four components namely, raw data (data collection), data preprocessing, data analysis, and information presentation. The paper concludes with the expected advantages of utilizing the proposed architectural framework for Iraqi healthcare organizations and the recommended solutions to encourage healthcare practitioners in adopting big data technology. Such technology can be optimized to monitor patient-related records and predict diseases, thereby improving care, saving lives, and minimizing costs.

Developing a knowledge management strategy for data analytics and intellectual capital

PurposeThis paper aims to build on current analytics and Big Data definitions and strategies from the literature to develop an overall strategic model connecting knowledge management strategy (KMS) for intellectual capital (IC) acquisition and business use. It also extends the IC research stages to a fifth stage of IC research including IC strategic intent.Design/methodology/approachA literature review highlights the connections among strategic intent, firm strategy, KMS and a data analytics strategy aligned with firm and KMS strategic intent. An extended model of the interrelationships is developed from the prior research.FindingsA model framework was developed from the literature that connects Big Data to achieve the goals of a firm KMS and demonstrates how Big Data analytics (BDA) needs to shift from being a tactical tool to a strategic knowledge management tool directed by the overall strategy and strategic intent of the firm.Research limitations/implicationsThe model presented needs to be empirically tested over a sample of companies and periods to determine if performance improves using this model.Practical implicationsUse of this model proposes that strategic intent will be enhanced and improve the capture of intellectual property derived from advanced analytics and increase sustainable advantages at firm.Social implicationsThe social implications of lack of strong privacy laws coupled with the possible elimination of millions of knowledge worker jobs creates a pressing need for more research into and identification of firm’s and government’s Big Data strategic use for both good and perhaps evil.Originality/valueThe research in this paper extends current models of IC development and adds strategic intent and collective intelligence as the fifth stage of IC research and presents an overall KMS/BDA model.

Activating Strategies of Logistics Big Data through Case Studies

본 논문에서는 4차산업혁명을 이끄는 요소기술로서 각광받고 있는 빅데이터 기술을 물류분야에 접목시켜서 활성화 할 수 있는 방안을 찾고자 한다. 이를 위하여 우선 빅데이터의 기술적 정의를 살펴보고 국내외 기술동향을 분석하였다. 이후 우리나라의 공공분야에서 수집관리하고 있는 물류관련 빅데이터를 조사 분석하였다. 이와 함께 해외의 공공분야 물류 빅데이터 구축상황을 조사분석하고 국내사례와 비교하여 시사점을 도출하였다. 그리고 기업이 주도하고 있는 민간분야의 국내외 물류 빅데이터 활용실태를 조사하였다. 이를 바탕으로 시사점을 도출하고 물류 빅데이터 활성화에 대한 법제도적 애로요인을 분석하여 개인정보보호법, 빅 데이터 공개방식, 빅데이터 원천관리 등 개선방안을 제시하였다.In this paper, we try to find a way to combine Big Data technologies that are leading The Fourth Industrial Revolution into logistics. To that end, we first investigated technical definitions of big data and analyzed the technical trends at South Korea and advanced countries. After that, this paper shows variety types of data collected and managed by South Korea’s governmental institutions, which have a great possibility to be aggregated as Big Data. Along with that, we investigated overseas success cases of Big Data applications in logistics fields and provided comparative analysis between domestic cases and foreign cases. This paper also examines current utilization status of Big Data technologies in the field of logistics led by private sectors. Based on findings, we suggested ways to make betterments for a practical application of Big Data technologies. These suggestions include elaborating Personal Information Protection Act, deep considerations on a varied methods of data release, and an advanced management tool for handling Big Data sources.

ASSOCIATION RULES MINING IN BIG DATA

The paper proposes a method for Big data analyzing in the presence of different data sources and different methods of processing these data. The Big data definition is given, the main problems of data mining process are described. The concept of association rules is introduced and the method of association rules searching for working with Big Data is modified. The method of finding dependencies is developed, efficiency and possibility of its parallelization are determined. The developed algorithm makes it possible to assert that the task of detecting association dependencies in distributed databases belongs to the class of P-tasks. The algorithm for finding association dependencies is well-solved with MapReduce. The low asymptotic complexity of the developed association rules mining algorithm and a wide set of data types supported for analysis allow to apply the proposed algorithm in practically all subject areas working with association dependencies in the data domain.