Research in Big Data News

Research in Big Data


Research in Big Data provides information about the research on applications of big data in a variety of industries including education, transportation, government, and commercial. Research in Big Data examines some of the new technologies available to improve existing systems. Some of these new technologies include cloud computing, new forms of database management systems, and developments in machine learning, artificial intelligence, data mining, and data analysis.

Data Analysis

Data Analysis

Technologies for analyzing data.

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Big Data

The management of large amounts and large variety of data.


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Dashboards and reporting of data.

Research in Big Data News

Research in Big Data

Dec 1, 2019 Issue 13

Eliminating child labor and technology

Simas, Golsteijn, Huijbregts, Wood, and Hertwich (2014) defines child labor as work performed by children under the designated minimum working age and estimate over 215 million children to be victims of child labor worldwide in 2008. Sonntag and Spiller (2018) develop a moral concerns scale for measuring and assessing production influences such as child labor. Awan, Kraslawski, and Huiskonen (2018) describe social dimensions such as exclusion from child labor practices key to sustainable growth for sustainable international businesses. Moon, Sagisaka, Tahara, and Tsukahara (2017) explain measurement and assessment of environmental and socioeconomic impacts of stakeholders including child labor in the production of nanofibers. Francisco and Swanson (2018) suggest that blockchain technologies can be implemented for supply chain transparency and expose scandalous manufacturing activities such as child labor and forced labor.

Awan, U., Kraslawski, A., & Huiskonen, J. (2018). Governing interfirm relationships for social sustainability: The relationship between governance mechanisms, sustainable collaboration, and cultural intelligence. Sustainability, 10(12). doi:10.3390/su10124473

Francisco, K., & Swanson, D. (2018). The supply chain has no clothes: Technology adoption of blockchain for supply chain transparency. Logistics, 2(1). doi:10.3390/logistics2010002

Moon, D., Sagisaka, M., Tahara, K., & Tsukahara, K. (2017). Progress towards sustainable production: Environmental, economic, and social assessments of the cellulose nanofiber production process. Sustainability, 9(12). doi:10.3390/su9122368

Simas, S. M., Golsteijn, L., Huijbregts, A. J. M., Wood, R., & Hertwich, G. E. (2014). The “bad labor” footprint: Quantifying the social impacts of globalization. Sustainability, 6(11). doi:10.3390/su6117514

Sonntag, I. W., & Spiller, A. (2018). Measuring public concerns? Developing a moral concerns scale regarding non-product related process and production methods. Sustainability, 10(5). doi:10.3390/su10051375

Science, technology, engineering, and mathematics programs

Science, technology, engineering, and mathematics programs may have more participation with creative new approaches and consideration for practical applications (Chavan & Bedekar, 2019). Cornetta, Mateos, Touhafi, and Muntean (2019) highlight that the proportion of students in Europe that graduated from science, technology, engineering, and mathematics programs declined since the turn of the century. Chavan and Bedekar (2019) propose methods to increase learning engagement of massive open online courses by developing puzzle and game based designs for learning environments. Kehrer and Penzenstadler (2018) identify five components for sustainability in research software systems which include individual, social, economic, technical, and environmental dimensions.

Ipshita and Jane (2016) measure the history of the pay gap between women mothers and non-mothers and identify a recent rise in the pay of non-mothers over mothers that they explain can be due to a number of reasons. Michelmore and Sassler (2016) identify the STEM fields with the smallest female representation are engineering and computer science. Apps (2017) suggests that gender pay gaps may possibly be addressed by taxation structures.

Song, Hooper, and Loke (2013) explain that even with the advance of open access journals, publication bias has been on the rise and suggest measures to reduce publication bias. Shields, Hall, and Mamun (2011) describe a disproportionate amount of men publishing in journals in nursing literature than the percentage of men in the nursing field. Holman, Stuart-Fox, and Hauser (2018) studied the gender gap in publication in science, technology, engineering, medicine, and mathematics fields and demonstrated an underrepresentation of women as single and senior authors.

A number of factors may contribute to the gender pay gap in employment (Livia, 2014). Wieschke (2018) studies the frequency at which college graduates change employers and finds that women change employers more frequently than men. Jovana and Svetlana (2015) explain that regulation, action programs, and promoting gender equality in the mainstream, are strategies for reducing the gender pay gap in developing government policies. Livia (2014) expresses that stereotypes of family responsibilities, promotion, and career goals can contribute the gender pay gap.

Singh and Peers (2019) explore the percentage of women in the engineering field in a number of categories and highlight that social prestige and tradition can be a reason for the percentage gap in some countries. Brynin and Perales (2015) describe the existence of gender segregation in certain fields and explain that education is often a key component in breaking occupational barriers. Gürerk, Irlenbusch, and Rockenbach (2018) identify in their study that men may be more likely to initially favor team environments than women which may require changes to such environments to promote equal access.

Stamarski and Son Hing (2015) model the root causes of gender inequalities in the workplace and speculate that inequalities exist whether it is a male-dominated or female dominated workplace environment. Thébaud and Charles (2018) suggest policies and practices for keeping women in science, technology, engineering, and mathematics careers and organizations. Ma and Liu (2017) apply logistic regression to predict the probability of degree attainment for race and gender groups. Sassler, Michelmore, and Smith (2017) discover significant gender and race inequalities in science, technology, engineering, and mathematics fields for computer science and engineering. Sanabria and Penner (2017) examine whether women are less likely than men to complete a degree in science, technology, engineering, and mathematics after failing a prerequisite course.

Kurniawan, Nurhaeni, Mugijatna, and Habsari (2018) explains that in addition to the challenges for women to enter the engineering field, there is additional bias of the need to appear more professional and demonstrate more technical ability once in the field. Dalingwater (2018) explain that one reason in a difference in pay for a country may be the availability of part time work that is available in that country. Sardelis, Oester, and Liboiron (2017) make ten recommendations to increase the participation of women in scientific conferences.

The differences in pay between men and women in the technology field may become an obstacle as information technology transcends into the next generation. Juhn and McCue (2017) explain that although women have made progress with regards to pay equality in recent decades there is still an underrepresentation in high earning occupations such as STEM fields. Gillespie (2014) highlights a false claim of a technology company that stated that women are more expensive to higher because of a smaller population in the field. Blau and Kahn (2017) discuss the gender differences in college majors to be a determining reason in the gender pay difference between men and women.

The progress made in gender equality in the sciences may be more specific to certain fields of science, technology, engineering, mathematics, and medicine (Holman et al., 2018). Holman et al. (2018) explore the gender gap in science and provide a detailed study of women authors in the fields of science. Chekene and Kashim (2018) explore the role of women in agricultural research and identify that women contribute more than 60% of global food supply, processing, and preparation as well as farm labour. Verniers and Vala (2018) predict that it would take another century to close the global gender economic and education gap according to the trend of the past decade. Cornetta et al. (2019) suggest that disengagement from science, technology, engineering, and mathematics subjects usually occurs during secondary education. Shastri (2014) details the history of discrimination against women and gender inequality. Baqi et al. (2017) compare differences in gender pay, support, and social issues in different countries in the medical profession.

Apps, P. (2017). Gender equity in the tax-transfer system for fiscal sustainability. In M. Stewart (Ed.), Tax, Social Policy and Gender (pp. 69-98): ANU Press.

Baqi, S., Albalbeesi, A., Iftikhar, S., Baig-Ansari, N., Alanazi, M., & Alanazi, A. (2017). Perceptions of gender equality, work environment, support and social issues for women doctors at a university hospital in Riyadh, Kingdom of Saudi Arabia. PLoS One, 12(10), e0186896. doi:10.1371/journal.pone.0186896

Blau, F. D., & Kahn, L. M. (2017). The gender wage gap: Extent, trends, and explanations. Journal of Economic Literature.

Brynin, M., & Perales, F. (2015). Gender wage inequality: The de-gendering of the occupational structure. European Sociological Review, 32(1), 162-174. doi:10.1093/esr/jcv092

Chavan, S., & Bedekar, M. (2019). MOOCS for digital game based learning for learners. Paper presented at the IRAJ International Conference, Pune, India.

Chekene, M. B., & Kashim, I. U. (2018). Gender equality: Women in agriculture or gender in agriculture. Agricultural Research & Technology, 18(5).

Cornetta, G., Mateos, J., Touhafi, A., & Muntean, G.-M. (2019). Design, simulation and testing of a cloud platform for sharing digital fabrication resources for education. Journal of Cloud Computing, 8(1), 12. doi:10.1186/s13677-019-0135-x

Dalingwater, L. (2018). Neo-liberalism and gender inequality in the workplace in Britain. Revue Française de Civilisation Britannique, XXIII(1).

Gillespie, K. M. (2014). Unequal pay: The role of gender. University of New Hampshire Scholars' Repository. University of New Hampshire

Gürerk, Ö., Irlenbusch, B., & Rockenbach, B. (2018). Endogenously emerging gender pay gap in an experimental teamwork setting. Games, 9(4). doi:10.3390/g9040098

Holman, L., Stuart-Fox, D., & Hauser, C. E. (2018). The gender gap in science: How long until women are equally represented? PLOS Biology, 16(4), e2004956. doi:10.1371/journal.pbio.2004956

Ipshita, P., & Jane, W. (2016). The family gap in pay: New evidence for 1967 to 2013. RSF: The Russell Sage Foundation Journal of the Social Sciences, 2(4), 104-127. doi:10.7758/rsf.2016.2.4.04

Jovana, G., & Svetlana, I. (2015). International and national frameworks of equal economic independence for women and men. Ekonomija: teorija i praksa, 8(2), 1-13.

Juhn, C., & McCue, K. (2017). Specialization then and now: Marriage, children, and the gender earnings gap across cohorts. Journal of Economic Perspectives, 31(1), 183-204. doi:10.1257/jep.31.1.183

Kehrer, T., & Penzenstadler, B. (2018). An exploration of sustainability thinking in research software engineering. Paper presented at the 7th International Workshop on Requirements Engineering for Sustainable Systems (RE4SuSy 2018), Banff, Alberta, Canada.

Kurniawan, Y., Nurhaeni, I. D. A., Mugijatna, & Habsari, S. K. (2018). Gender bias in the workplace: Should women be marginalized in engineering job? IOP Conference Series: Materials Science and Engineering, 306, 012132. doi:10.1088/1757-899x/306/1/012132

Livia, C. (2014). Gender gap dimensions on the labour market in the European Union. Annals of the University of Oradea: Economic Science, 23(1), 106-115.

Ma, Y., & Liu, Y. (2017). Entry and degree attainment in STEM: The intersection of gender and race/ethnicity. Social Sciences, 6(3), 89.

Michelmore, K., & Sassler, S. (2016). Explaining the Gender Wage Gap in STEM: Does Field Sex Composition Matter? RSF: The Russell Sage Foundation Journal of the Social Sciences, 2(4), 194-215. doi:10.7758/rsf.2016.2.4.07

Sanabria, T., & Penner, A. (2017). Weeded out? Gendered responses to failing calculus. Social Sciences, 6(2), 47.

Sardelis, S., Oester, S., & Liboiron, M. (2017). Ten strategies to reduce gender inequality at scientific conferences. Frontiers in Marine Science, 4(231). doi:10.3389/fmars.2017.00231

Sassler, S., Michelmore, K., & Smith, K. (2017). A tale of two majors: Explaining the gender gap in STEM employment among computer science and engineering degree holders. Social Sciences, 6(3), 69.

Shastri, A. (2014). Gender inequality and women discrimination. IOSR Journal Of Humanities And Social Science (IOSR-JHSS), 19(11), 27-30.

Shields, L., Hall, J., & Mamun, A. A. (2011). The 'gender gap' in authorship in nursing literature. Journal of the Royal Society of Medicine, 104(11), 457-464. doi:10.1258/jrsm.2011.110015

Singh, S., & Peers, S. M. C. (2019). Where are the women in the engineering labour market? A cross-sectional study. International Journal of Gender, Science and Technology, 11(1).

Song, F., Hooper, L., & Loke, Y. K. (2013). Publication bias: What is it? How do we measure it? How do we avoid it? Open Access Journal of Clinical Trials, 5, 71–81.

Stamarski, C. S., & Son Hing, L. S. (2015). Gender inequalities in the workplace: The effects of organizational structures, processes, practices, and decision makers' sexism. Frontiers in Psychology, 6, 1400-1400. doi:10.3389/fpsyg.2015.01400

Thébaud, S., & Charles, M. (2018). Segregation, stereotypes, and STEM. Social Sciences, 7(7), 111.

Verniers, C., & Vala, J. (2018). Justifying gender discrimination in the workplace: The mediating role of motherhood myths. PLoS One, 13(1), e0190657. doi:10.1371/journal.pone.0190657

Wieschke, J. (2018). Frequency of employer changes and their financial return: gender differences amongst German university graduates. Journal for Labour Market Research, 52(1), 1. doi:10.1186/s12651-017-0235-3

Applications of big data and machine learning

The ability to analyze large amounts and frequencies of data may provide new opportunities in information systems (Anshari, Almunawar, Lim, & Al-Mudimigh, 2019). Sanger and Warin (2016) investigate how sentiment analysis can extrapolate information from social media feeds and high frequency financial market data. Akhter and Imran (2017) describe a new field that is developing which combines chemistry, information sciences, and mathematics. Meenakshi and Kathiresan (2019) explain a method for computing operations of graphs. Anshari et al. (2019) discuss the potential of big data with customer relationship management systems.

Machine learning may have applications with the Internet of Things to support geographic information systems (Kshetri, 2016). Kshetri (2016) describes the advantages that sensors can have in measuring temperature, humidity, and position for geographic information systems to support manufacturing logistics. Vandal et al. (2017) propose a convolutional neural network for measuring and simulating climate systems based on observational and topographical data. Mariescu-Istodor and Fränti (2018) propose a technique for applying machine learning for inferring roads on maps with clustering.

Akhter, S., & Imran, M. (2017). Computing the forgotten topological index of four operations on graphs. AKCE International Journal of Graphs and Combinatorics, 14(1), 70-79. doi:

Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101. doi:

Kshetri, N. (2016). The economics of the Internet of Things in the Global South. Third World Quarterly, 38(2), 311-339. doi:10.1080/01436597.2016.1191942

Mariescu-Istodor, R., & Fränti, P. (2018). CellNet: Inferring road networks from GPS trajectories. ACM Transactions on Spatial Algorithms and Systems, 4(3), 1-22. doi:10.1145/3234692

Meenakshi, C., & Kathiresan, K. M. (2019). A generalization of magic and antimagic labelings of graphs. AKCE International Journal of Graphs and Combinatorics, 16(2), 125-144. doi:

Sanger, W., & Warin, T. (2016). High frequency and unstructured data in finance: An exploratory study of Twitter. Journal of Global Research in Computer Science, 7(4), 6-16.

Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., & Ganguly, A. R. (2017). DeepSD: Generating high resolution climate change projections through single image super-resolution. Paper presented at the Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17.

Research in information systems data security

New research in information systems security may provide guidance on how to measure and prevent the latest data vulnerabilities in (Cartwright, Hernandez Castro, & Cartwright, 2019). Zander, Steinbrück, and Birnstill (2019) implement a game theory model to test the effects of data protection laws to measure economic effects. Mulder (2019) evaluates the privacy effects for health related applications under the data privacy laws. Spoerri (2019) discusses technological, economic, and legal challenges in implementing technology filters on uploaded content. Ursic (2018) discusses data portability movements which allow consumers to export the data that is collected by technology companies.

Cartwright et al. (2019) quantify a strategy on whether ransomware victims should pay the ransom of a ransomware attack. Mermoud, Keupp, Huguenin, Palmié, and Percia David (2019) suggest research of components that influence security information sharing such as organizational processes, cultures, and risk management approaches. Maximilian, Markl, and Mohamed (2018) describe certain vulnerabilities for the Industrial Internet of Things including access attacks, denial of service attacks, man in the middle attacks, malicious software, and data manipulation.

Cartwright, E., Hernandez Castro, J., & Cartwright, A. (2019). To pay or not: Game theoretic models of ransomware. Journal of Cybersecurity, 5(1). doi:10.1093/cybsec/tyz009

Maximilian, L., Markl, E., & Mohamed, A. (2018). Cybersecurity management for (Industrial) Internet of Things: Challenges and opportunities. Journal of Information Technology & Software Engineering, 8(5).

Mermoud, A., Keupp, M. M., Huguenin, K., Palmié, M., & Percia David, D. (2019). To share or not to share: A behavioral perspective on human participation in security information sharing. Journal of Cybersecurity, 5(1). doi:10.1093/cybsec/tyz006

Mulder, T. (2019). Health apps, their privacy policies and the GDPR. European Journal of Law and Technology, 10(1).

Spoerri, T. (2019). On upload-filters and other competitive advantages for big tech companies under Article 17 of the Directive on Copyright in the digital single market. JIPITEC, 10(2), 173-186.

Ursic, H. (2018). Unfolding the new-born right to data portability: Four gateways to data subject control. scripted: A Journal of Law, Technology & Society, 15(1), 42-69.

Zander, T., Steinbrück, A., & Birnstill, P. (2019). Game-theoretical model on the GDPR - Market for lemons? JIPITEC, 10(2), 200-208.

Mass media communications and culture

The growing mass media communications may have unforeseen effects in various regions (Parashar & Sreenivasan, 2015). Parashar and Sreenivasan (2015) explain possible negative cultural outcomes for the influence of mass media in developing countries. Banday and Mattoo (2013) explain how social media can be implemented in e-governance for benefits such as civic empowerment, municipal efficiency, enforcing regulations, and informing voters. He and He (2015) describe the development of mass media and its influence in education.

Regulation and lack of regulation can vary for different regions in the communication industry (Wang & Chen, 2012). Kalombe and Phiri (2019) suggest that the print media industry in developing countries may find ways to target digital audiences to compete with online media. Oyenuga (2014) indicates the dangers of vertical mergers raising costs of competing businesses and possibly leading to detrimental effects on consumer markets. Wang and Chen (2012) model the effects of trusts among competing firms in different countries according to their regulations. Shelanski (2006) discusses challenges and makes recommendations for how federal agencies may govern media companies.

Orben, Dienlin, and Przybylski (2019) suggest that researchers and policymakers cooperate on understanding effects of social media after studying the influence of social media on analytics. van den Eijnden, Lemmens, and Valkenburg (2016) also measure social media addiction in analytics and attempt to develop a means to identify social media disorder. Amedie (2015) argues that despite the positive benefits of social media for sharing information, there are also negative aspects associated with psychological disorders and criminal activities. Thompson, LaRocca, Gallagher, and Cintron (2009) consider how internet and communication networks affect voting, election information, and news media.

Amedie, J. (2015). The impact of social media on society. Santa Clara University Scholar Commons: Advanced Writing: Pop Culture Intersections, 2.

Banday, M. T., & Mattoo, M. M. (2013). Social media in e-governance: A study with special reference to India. Social Networking, 2, 47-56.

He, L., & He, J. (2015). The revolution of communication media and its impact on education. Open Journal of Social Sciences, 3, 123-127.

Kalombe, C., & Phiri, J. (2019). Impact of online media on print media in developing countries. Open Journal of Business and Management, 7, 1983-1998.

Orben, A., Dienlin, T., & Przybylski, A. K. (2019). Social media’s enduring effect on adolescent life satisfaction. Proceedings of the National Academy of Sciences, 116(21), 10226-10228. doi:10.1073/pnas.1902058116

Oyenuga, A. (2014). Vertical mergers, raising rivals’ costs and foreclosure in a network industry. Modern Economy, 5, 443-460.

Parashar, A., & Sreenivasan, G. (2015). Mindset shaping by media: An overview of media technologies leading towards media imperialism in media mix. Social Networking, 4, 17-21.

Shelanski, H. A. (2006). Antitrust law and mass media regulation: Can merger standards protect the public interest? California Law Review, 94, 371.

Thompson, K., LaRocca, D., Gallagher, P., & Cintron, J. (2009). Impact of internet and communication networks and technologies on concepts of and forms of democratic government and rule. (Senior Research Project (CC499-05)- Capstone Course), Queensborough Community College, CUNY,

van den Eijnden, R. J. J. M., Lemmens, J. S., & Valkenburg, P. M. (2016). The social media disorder scale. Computers in Human Behavior, 61, 478-487. doi:

Wang, J.-S., & Chen, Y.-S. (2012). The impact of different antitrust laws on the actions of cartels. Theoretical Economics Letters, 2(5), 455-458.