OntoDIVE: An Ontology for Representing Data Science Initiatives upon Big Data Technologies

2020 ◽  
Author(s):  
Vitor Pinto ◽  
Fernando Parreiras
Keyword(s):  
Big Data ◽  
Data Science ◽  
Big Data Technologies

scholarly journals Music Industry in the Big Data Era: Sociological and Marketing Research

2018 ◽  
Vol 6 (4) ◽  
pp. 161-172
Author(s):  
Marina G. Snezhinskaya
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Music Industry ◽  
Marketing Research ◽  
Professional Competencies ◽  
Data Usage ◽  
Big Data Technologies ◽  
Music Market ◽  
Musical Preferences

The Big Data technologies and the potential of their application in the music industry are reviewed in the article. The main questions raised concern the perspectives of the Big Data usage in the sociological and marketing research, the audience data analysis and the musical preferences of the audience. The Big Data allow to discover new artists and to find new ways of stimulating the audience’s loyalty. The author attempts to answer the question: how does the Big Data change the music industry? The possibility of using the Big Data to forecast the audience’s behavior is being reviewed. The examples of the Big Data technologies usage in the marketing research for the music industry are exposed. The author underlines the importance of this technology for sociologists and market researchers and brings into the light the problems of the Big Data usage. The attention is drawn to the development of the new sphere of musical data science and to the necessity of broadening the professional competencies of sociologists and music market researchers.

scholarly journals Enablers to supply chain performance on the basis of digitization technologies

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Gupta ◽  
Sarangdhar Kumar ◽  
Simonov Kusi-Sarpong ◽  
Charbel Jose Chiappetta Jabbour ◽  
Martin Agyemang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Information Technologies ◽  
Data Science ◽  
Content Type ◽  
Big Data Technologies ◽  
Fierce Competition ◽  
Practical Implications ◽  
Improve Supply Chain

PurposeThe aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.Design/methodology/approachThe Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.FindingsThe total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.Practical implicationsThe findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.Originality/valueThis study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.

scholarly journals Why Personal Dreams Matter: How professionals affectively engage with the promises surrounding data-driven healthcare in Europe

2022 ◽  
Vol 9 (1) ◽  
pp. 205395172110706
Author(s):  
Marthe Stevens ◽  
Rik Wehrens ◽  
Johanna Kostenzer ◽  
Anne Marie Weggelaar-Jansen ◽  
Antoinette de Bont
Keyword(s):  
Big Data ◽  
Science And Technology Studies ◽  
Data Science ◽  
Data Driven ◽  
Grand Narrative ◽  
Healthcare Staff ◽  
Critical Data ◽  
Key Players ◽  
Big Data Technologies ◽  
Critical Policy

Recent buzzes around big data, data science and artificial intelligence portray a data-driven future for healthcare. As a response, Europe's key players have stimulated the use of big data technologies to make healthcare more efficient and effective. Critical Data Studies and Science and Technology Studies have developed many concepts to reflect on such overly positive narratives and conduct critical policy evaluations. In this study, we argue that there is also much to be learned from studying how professionals in the healthcare field affectively engage with this strong European narrative in concrete big data projects. We followed twelve hospital-based big data pilots in eight European countries and interviewed 145 professionals (including legal, governance and ethical experts, healthcare staff and data scientists) between 2018 and 2020. In this study, we introduce the metaphor of dreams to describe how professionals link the big data promises to their own frustrations, ideas, values and experiences with healthcare. Our research answers the question: how do professionals in concrete data-driven initiatives affectively engage with European Union's data hopes in their ‘dreams’ – and with what consequences? We describe the dreams of being seen, of timeliness, of connectedness and of being in control. Each of these dreams emphasizes certain aspects of the grand narrative of big data in Europe, makes particular assumptions and has different consequences. We argue that including attention to these dreams in our work could help shine an additional critical light on the big data developments and stimulate the development of responsible data-driven healthcare.

Advanced Threat Detection Based on Big Data Technologies

2018 ◽  
pp. 1-19
Author(s):  
Madhvaraj M. Shetty ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Threat Detection ◽  
Cyber Threats ◽  
Threat Intelligence ◽  
Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

Advanced Threat Detection Based on Big Data Technologies

2020 ◽  
pp. 808-822
Author(s):  
Madhvaraj M. Shetty ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Threat Detection ◽  
Cyber Threats ◽  
Threat Intelligence ◽  
Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

scholarly journals Computing Academics into New Age Programs and Fields: Big Data Analytics & Data Sciences in Indian Academics—An Academic Investigation of Private Universities

2018 ◽  
Vol 10 (3) ◽  
pp. 107
Author(s):  
P. K. Paul ◽  
P. S. Aithal
Keyword(s):  
Big Data ◽  
Data Science ◽  
Private Universities ◽  
Big Data Analytics ◽  
New Age ◽  
Complex Data ◽  
Developed Country ◽  
Skilled Manpower ◽  
The Status ◽  
Big Data Technologies

<p>India is moving towards a developed country and thus knowledge cultivation is very much required and in this context introducing new age programs and degrees are essential. This is required for the creation of next generation skills and knowledge as per industrial and organizational demands including Government etc. Data Science is about managing the large amount and complex data, moreover, it is also known as Big Data Technologies. Due to the wider requirement in different universities and organizations, many international universities have started academic programs on Data Sciences and Big Data. However, a large number of institutes are located in India but only a few offer programs on Data Science and Allied Technologies. Importantly most these are listed in Engineering Colleges and few Private Universities. Universities in this regard adopted both full-fledged degrees and specialization methods to offer the Data Sciences and Allied Technologies. This paper is theoretical and contextual in nature, but also purely interdisciplinary in nature combines with education, information technology, and managerial science to learn about the status and future of qualified and skilled manpower. However, the paper is specially focused on private universities only with a brief overview of the technologies in international universities. </p>

scholarly journals Managing reputational risk associated with big data research and development: an interdisciplinary perspective

2020 ◽  
Author(s):  
Cara Stitzlein ◽  
Simon Fielke ◽  
François Waldner ◽  
Todd Sanderson
Keyword(s):  
Big Data ◽  
Research And Development ◽  
Technology Acceptance ◽  
Data Science ◽  
Technology Development ◽  
Reputational Risk ◽  
Individual Privacy ◽  
Big Data Technologies ◽  
Value Propositions ◽  
Private And Public

Many private and public actors are incentivized by big data technologies: digital tools underpinned by capabilities such as artificial intelligence and machine learning. While many shared value propositions exist about what these technologies afford, public facing concerns related to individual privacy, algorithm fairness, and access to insights require attention if the widespread use and subsequent value of these technologies are to be fully realized. Drawing from perspectives of data science, social science and technology acceptance, we present an interdisciplinary analysis that reveals the connections between these concerns and traditional research and development (R&amp;D) activities of data collection, technology development and implementation. Given the behaviors associated with digital transformation opportunities, we suggest a reframing of the public-facing R&amp;D ‘brand’ that responds to legitimate concerns related to individual privacy, fairness, and social equity. We offer as a case study Australian agriculture, which is currently undergoing such digitalisation and where concerns have been raised by landholders and the research community. With seemingly limitless possibilities, an updated account of responsible R&amp;D in an increasing digitalized world may accelerate how we might realize benefits of big data and mitigate harmful social and environmental costs.

Real-Time Predictive Analytics, Big Data & Energy Market Efficiency: Key to Efficient Markets and Lower Prices for Consumers

2014 ◽  
Vol 704 ◽  
pp. 453-458 ◽  
Author(s):  
Benjamin Schmidt ◽  
Patrick Flannery ◽  
Mark DeSantis
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Science ◽  
Predictive Analytics ◽  
Energy Market ◽  
Optimal Pricing ◽  
Energy Data ◽  
Main Challenge ◽  
The Us ◽  
Big Data Technologies

The combination of evolving deregulation of the US and EU energy markets together with recent advances in data analytics and so called 'Big Data' technologies now offers an unprecedented opportunity for optimal real-time energy pricing for buyers and sellers alike. The main challenge to date for optimal pricing has been optimal real-time bidding and variety of traditional data analysis tools have been applied to this challenge. Yet inefficiencies remain due to the volatile nature of the real-time market. Energy data science is the best solution to protect consumers against the electricity market's inefficiencies. This field is the meeting point between computer programming, machine learning, big data, quantitative analysis and economics. Energy data science is used to help the consumer predict what price they should offer to buy at each hour and closes gaps in the electric markets.

scholarly journals Reputational Risk Associated with Big Data Research and Development: An Interdisciplinary Perspective

2021 ◽  
Vol 13 (16) ◽  
pp. 9280
Author(s):  
Cara Stitzlein ◽  
Simon Fielke ◽  
François Waldner ◽  
Todd Sanderson
Keyword(s):  
Big Data ◽  
Research And Development ◽  
Technology Acceptance ◽  
Data Science ◽  
Reputational Risk ◽  
Individual Privacy ◽  
Traditional Research ◽  
Big Data Technologies ◽  
Value Propositions ◽  
Private And Public

Many private and public actors are incentivized by the promises of big data technologies: digital tools underpinned by capabilities like artificial intelligence and machine learning. While many shared value propositions exist regarding what these technologies afford, public-facing concerns related to individual privacy, algorithm fairness, and the access to insights requires attention if the widespread use and subsequent value of these technologies are to be fully realized. Drawing from perspectives of data science, social science and technology acceptance, we present an interdisciplinary analysis that links these concerns with traditional research and development (R&D) activities. We suggest a reframing of the public R&D ‘brand’ that responds to legitimate concerns related to data collection, development, and the implementation of big data technologies. We offer as a case study Australian agriculture, which is currently undergoing such digitalization, and where concerns have been raised by landholders and the research community. With seemingly limitless possibilities, an updated account of responsible R&D in an increasingly digitalized world may accelerate the ways in which we might realize the benefits of big data and mitigate harmful social and environmental costs.

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