scholarly journals Datos, minería e innovación: qvo vadis, Europa? Análisis sobre las nuevas excepciones para la mineria de textos y datos = Data, mining and innovation: qvo vadis, Europe? Analysis on the new exceptions for text and data mining

2020 ◽  
Vol 12 (1) ◽  
pp. 247
Author(s):  
Vanessa Jiménez Serranía
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Critical Review ◽  
A Priori ◽  
European Parliament ◽  
Single Market ◽  
Fair Use ◽  
Text And Data Mining

 Resumen: La Directiva 2019/790 del Parlamento Europeo y del Consejo de 17 de abril de 2019 sobre los derechos de autor y derechos afines en el mercado único digital ha implementado ciertas excepciones sobre la minería de textos y datos. Pese a que, a priori, podría parecer que se ofrece un impulso importante a este tipo de actividades sus efectos en la práctica quedan mitigados por el encorsetamiento de su formulación que, incluso, es susceptible de generar distorsiones competitivas. Este artículo pretende dar una visión sucinta y crítica sobre estas nuevas excepciones y plantear ciertas vías de mejora futura.Palabras clave: Big Data, minería de textos y datos, Internet de las cosas, Inteligencia Artificial, Mercado Único Digital, Directiva 2019/790, excepciones al derecho de autor, “uso justo”, regla de los tres pasos, doctrina de las facilidades esenciales, competencia, innovación.Abstract: Directive 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the digital single market has implemented certain exceptions on text and data mining. Although these exceptions might seem to provide a significant boost to this type of activities, their effects in practice are mitigated by the tightening of its wording, which is even likely to generate competitive distortions. This article aims to give a succinct and critical review of these new exceptions and to suggest some ways of improvement for the near future.Keywords: Big Data, Text and Data Mining (TDM), IoT, AI, Single Digital Market, Directive 2019/790, copyright limitations, “fair use”, “three-steps doctrine”, esencial facilities doctrine, competition, innovation.

scholarly journals Big data: Hell or heaven? Digital platforms and market power in the data-driven economy

2018 ◽  
Vol 23 (3) ◽  
pp. 312-328 ◽  
Author(s):  
Massimiliano Nuccio ◽  
Marco Guerzoni
Keyword(s):  
Big Data ◽  
Economies Of Scale ◽  
A Priori ◽  
Information Economics ◽  
Market Concentration ◽  
Fair Use ◽  
Policy Debate ◽  
Digital Platforms ◽  
Continuous Innovation ◽  
The One

Digital transformation has triggered a process of concentration in several markets for information goods with digital platforms rising to dominate key industries by leveraging on network externalities and economies of scale in the use of consumer data. The policy debate, therefore, focuses on the market control allegedly held by incumbents who build their competitive advantage on big data. In this paper, we evaluate the risk of abuse of a dominant position by analysing three major aspects highlighted in economic theory: entry barriers, price discrimination, and potential for technological improvement. Drawing on industrial and information economics, we argue that the very nature of big data, on the one hand, prompts market concentration and, on the other, limits the possibility of abuse. This claim is not an a-priori apologia of large incumbents in digital markets, but rather an attempt to argue that market concentration is not necessarily detrimental when it stimulates continuous innovation. Nonetheless, the concentration of power in a few global players should raise other concerns linked with the supranational nature of these firms, which can easily cherry-pick locations to exploit tax competition among countries or more favourable privacy legislation and the fair use of data.

scholarly journals „From Big Data to Smart Knowledge – Text and Data Mining in Science and Economy“ (Köln, 23.–24. Februar 2015)

2019 ◽  
Vol 68 (2) ◽  
pp. 351-356
Author(s):  
Bruno Bauer
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Text And Data Mining

Bericht über „From Big Data to Smart Knowledge – Text and Data Mining in Science and Economy“ (Köln, 23.–24. Februar 2015).

Article 3—Text and Data Mining for the Purposes of Scientific Research

2021 ◽  
pp. 25-59
Author(s):  
Eleonora Rosati
Keyword(s):  
Data Mining ◽  
Best Practices ◽  
Cultural Heritage ◽  
Subject Matter ◽  
Scientific Research ◽  
Single Market ◽  
Member States ◽  
Research Organisations ◽  
Text And Data Mining ◽  
Copyright Directive

This chapter focuses on the laws about text and data mining for scientific researchstipulated under Article 3 of the Directive 2019/790 or copyright directive of the Digital Single Market in Europe. It examines the legislation that require Member States to provide an exception for reproductions and extractions made by research organisations and cultural heritage institutions on text and data mining of works or other subject matter for the purposes of scientific research. It also stresses that copies of works or other subject matter on text and data mining will be stored with an appropriate level of security and retained for the purposes of scientific research. The chapter talks about rightholders, which are allowed to apply measures to ensure the security and integrity of the networks and databases. It mentions Member States that encourage rightholders, research organisations, and cultural heritage institutions to define commonly agreed best practices concerning the application of the obligation and measures on text and data mining.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

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