Thinking Big Data in Geography: New Regimes, New Research. Edited by Jim Thatcher, Josef Eckert, and Andrew Shears (Lincoln, University of Nebraska Press, 2018) 296 pp. $75.00 cloth $30.00 paper and e-book

2019 ◽  
Vol 50 (1) ◽  
pp. 118-120
Author(s):  
Jeremy Crampton
Keyword(s):  
Big Data ◽  
New Research ◽  
Lincoln University

THE RELATIONSHIP OF SOCIOTECHNICAL AND TECHNOLOGICAL ASPECTS OF THE INTRODUCTION AND APPLICATION OF BUSINESS INTELLIGENCE SYSTEMS

2021 ◽  
Vol 5 (12) ◽  
pp. 30-35
Author(s):  
Edward N. Ozhiganov ◽  
◽  
Alexander A. Chursin ◽  
Alexey D. Linkov ◽  
◽  
...  
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
New Technologies ◽  
Business Analytics ◽  
Scientific Publications ◽  
Business Decisions ◽  
Business Decision ◽  
Business Intelligence Systems ◽  
Demand Fluctuations ◽  
New Research

This article describes a relation between sociotechnical and technological factors involved in launching and implementing Business Intelligence systems. Advanced BI systems include business analytics, data mining, data visualization, data tools and infrastructure, and advanced IT solutions to support business decisions based on big data. Various industries and businesses handle large amounts of data to adapt to changing markets and demand fluctuations, push new technologies, and repair ineffective strategies, etc. With an upsurge in data sizes, more and more new research papers are published today to describe BI implemen-tation, use and results. However, today most studies and scientific publications focus on Business Intelligence technological challenges, while sociotechnical aspects – that is processes involved in business decision mak-ing based on big data – are studied in much rarer cases.

scholarly journals The Rise of Big Data Science: A Survey of Techniques, Methods and Approaches in the Field of Natural Language Processing and Network Theory

2018 ◽  
Vol 2 (3) ◽  
pp. 22 ◽  
Author(s):  
Jeffrey Ray ◽  
Olayinka Johnny ◽  
Marcello Trovati ◽  
Stelios Sotiriadis ◽  
Nik Bessis
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Network Theory ◽  
Data Science ◽  
Theoretical Foundation ◽  
Scientific Field ◽  
Research Challenges ◽  
New Research

The continuous creation of data has posed new research challenges due to its complexity, diversity and volume. Consequently, Big Data has increasingly become a fully recognised scientific field. This article provides an overview of the current research efforts in Big Data science, with particular emphasis on its applications, as well as theoretical foundation.

scholarly journals Use cases and challenges in telecom big data analytics

2016 ◽  
Vol 5 ◽  
Author(s):  
Chung-Min Chen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Driving Forces ◽  
Big Data Analytics ◽  
Use Cases ◽  
Telecommunication Industry ◽  
New Research

This paper examines the driving forces of big data analytics in the telecom domain and the benefits it offers. We provide example use cases of big data analytics and the associated challenges, with the hope to inspire new research ideas that can eventually benefit the practice of the telecommunication industry.

scholarly journals Pricing Strategies of a Three-Stage Supply Chain: A New Research in the Big Data Era

2017 ◽  
Vol 2017 ◽  
pp. 1-16
Author(s):  
Pan Liu
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Stackelberg Game ◽  
Chain Model ◽  
Pricing Strategies ◽  
Supply Chain Model ◽  
Investment Cost ◽  
New Research ◽  
New Situation ◽  
Big Data Technology

In the Big Data era, Data Company as the Big Data information (BDI) supplier should be included in a supply chain. In the new situation, to research the pricing strategies of supply chain, a three-stage supply chain with one manufacturer, one retailer, and one Data Company was chosen. Meanwhile, considering the manufacturer contained the internal and external BDI, four benefit models about BDI investment were proposed and analyzed in both decentralized and centralized supply chain using Stackelberg game. Meanwhile, the optimal retail price and benefits in the four models were compared. Findings are as follows. (1) The industry cost improvement coefficient, the internal BDI investment cost of the manufacturer, and the added cost of the Data Company on using Big Data technology have different relationships with the optimal prices of supply chain members in different models. (2) In the retailer-dominated supply chain model, the optimal benefits of the retailer and the manufacturer are the same, and the optimal benefits of the Data Company are biggest in all the members.

From Business Intelligence to Big Data

2021 ◽  
pp. 44-62
Author(s):  
Mouhib Alnoukari
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Business Intelligence ◽  
Corporate Strategy ◽  
Big Data Analytics ◽  
Organizational Capability ◽  
Strategy Transformation ◽  
Executive Managers ◽  
New Research ◽  
Research Challenge

Boundaries between business intelligence (BI), big data (BD), and big data analytics (BDA) are often unclear and ambiguous for companies. BD is a new research challenge; it is becoming a subject of growing importance. Notably, BD was one of the big buzzwords during the last decade. BDA can help executive managers to plan an organization's short-term and long-term goals. Furthermore, BI is considered as a kind of decision support system (DSS) that can help organizations achieving their goals, creating corporate value and improving organizational performance. This chapter provides a comprehensive view about the interrelationships between BI, BD, and BDA. Moreover, the chapter highlights the power of analytics that make them considered as one of the highly impact's organizational capability. Additionally, the chapter can help executive managers to decide the way to integrate BD initiatives as a tool, or as an industry, or as a corporate strategy transformation.

scholarly journals Big Data in Finance

2021 ◽  
Author(s):  
Itay Goldstein ◽  
Chester S Spatt ◽  
Mao Ye
Keyword(s):  
Big Data ◽  
Asset Pricing ◽  
Complex Structure ◽  
Future Research ◽  
Special Issue ◽  
Research Directions ◽  
Finance Industry ◽  
Large Size ◽  
Future Research Directions ◽  
New Research

Abstract Big data is revolutionizing the finance industry and has the potential to significantly shape future research in finance. This special issue contains papers following the 2019 NBER-RFS Conference on Big Data. In this introduction to the special issue, we define the “big data” phenomenon as a combination of three features: large size, high dimension, and complex structure. Using the papers in the special issue, we discuss how new research builds on these features to push the frontier on fundamental questions across areas in finance—including corporate finance, market microstructure, and asset pricing. Finally, we offer some thoughts for future research directions.

Thinking and Modeling for Big Data from the Perspective of the I Ching

2017 ◽  
Vol 16 (06) ◽  
pp. 1451-1463 ◽  
Author(s):  
Chuang Lin ◽  
Guoliang Li ◽  
Zhiguang Shan ◽  
Yong Shi
Keyword(s):  
New York ◽  
Big Data ◽  
Data Science ◽  
New York Times ◽  
Health Science ◽  
Distributed Data ◽  
I Ching ◽  
Management Tools ◽  
Distributed Data Streams ◽  
New Research

Data is growing faster than ever before and is changing our daily life. However it is rather challenging to manage the big data [F. H. Cate, The big debate, Science 346 (2014) 810, J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh and A. H. Byers, Big Data: The Next Frontier for Innovation, Competition, and Productivity (Mckinsey global Institute, 2011), S. Lohr, The Age of Big Data (New York Times, 2012), p. 11, L. Einav and J. Levin, Economics in the age of big data, Science 345 (2014) 715, M. J. Khoury and J. P. A. Ioannidis, Big data meets public health, Science 346 (2014) 1054–1055, V. Marx, Biology: The big challenges of big data, Nature 498(7453) (2013) 255–260.]. In this paper, we propose the big data thinking and modeling techniques from the perspective of the I Ching, which is a very famous imaginal thinking theory in China with 3,000 years history. The I Ching has been proven to be very useful and practical in many domains, e.g., 36 stratagems. Firstly, inspired from the three components of the I Ching, image, number and principle, we propose a new three-cycle big data thinking way, from data to phenomenon, from phenomenon to correlation, and from correlation to knowledge, which is a generalization of the fourth paradigm (from causality to correlation) proposed by Jim Gray. Secondly, inspired from the three entities of the I Ching, heaven, earth and human, we propose a new big data modeling method. We use the tree entities to represent the big data. We map the 4[Formula: see text]V of big data (volume, variety, velocity, veracity) to four opposition and uniform relations in the I Ching, and generate the eight diagrams. By capturing the relationships between eight diagrams, we generate the 64 hexagrams, and use 64 hexagrams to model big data. We also provide the principle rules to understand the knowledge generated by the model. Thirdly, we discuss how to utilize our model to describe big-data management tools, including, MapReduce, Spark, Storm. We also provide a new model for handling distributed data streams. We do think that we provide a new practical way of thinking and modeling for big data. We also believe that this will open up many new research directions on big data.

Breaching intellectual capital: critical reflections on Big Data security

2018 ◽  
Vol 26 (3) ◽  
pp. 463-482 ◽  
Author(s):  
Matteo La Torre ◽  
John Dumay ◽  
Michele Antonio Rea
Keyword(s):  
Big Data ◽  
Intellectual Capital ◽  
Data Security ◽  
Economic Value ◽  
Public Information ◽  
Dark Side ◽  
Content Type ◽  
Security Breaches ◽  
Managerial Implications ◽  
New Research

PurposeReflecting on Big Data’s assumed benefits, this study aims to identify the risks and challenges of data security underpinning Big Data’s socio-economic value and intellectual capital (IC).Design/methodology/approachThe study reviews academic literature, professional documents and public information to provide insights, critique and projections for IC and Big Data research and practice.FindingsThe “voracity” for data represents a further “V” of Big Data, which results in a continuous hunt for data beyond legal and ethical boundaries. Cybercrimes, data security breaches and privacy violations reflect voracity and represent the dark side of the Big Data ecosystem. Losing the confidentiality, integrity or availability of data because of a data security breach poses threat to IC and value creation. Thus, cyberthreats compromise the social value of Big Data, impacting on stakeholders’ and society’s interests.Research limitations/implicationsBecause of the interpretative nature of this study, other researchers may not draw the same conclusions from the evidence provided. It leaves some open questions for a wide research agenda about the societal, ethical and managerial implications of Big Data.Originality/valueThis paper introduces the risks of data security and the challenges of Big Data to stimulate new research paths for IC and accounting research.

scholarly journals Towards Big Data Analytics in the e-Learning Space

2019 ◽  
Vol 19 (3) ◽  
pp. 16-24 ◽  
Author(s):  
Ivan P. Popchev ◽  
Daniela A. Orozova
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Students At Risk ◽  
Learning Space ◽  
E Learning ◽  
New Research ◽  
Big Data Storage

Abstract The issues related to the analysis and management of Big Data, aspects of the security, stability and quality of the data, represent a new research, and engineering challenge. In the present paper, techniques for Big Data storage, search, analysis and management in the area of the virtual e-Learning space and the problems in front of them are considered. A numerical example for explorative analysis of data about the students from Burgas Free University is applied, using instrument for Data Mining of Orange. The analysis is a base for a system for localization of students at risk.

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