Research in Big Data News

Research in Big Data

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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.

Big Data

Big Data

The management of large amounts and large variety of data.

Reporting

Visualization and Reporting

Dashboards and reporting of data.

Research in Big Data News

Research in Big Data

Mar 1, 2021 Issue 18



Big data analytics and organizational knowledge management



Big data analytics platforms provide knowledge management tools that can support measurements of organizational performance and organizational change (Shabbir & Gardezi, 2020).  Shabbir and Gardezi (2020) explore knowledge management as applications of big data analytics as a means to enhance organizational performance and gain competitive advantage.  Mihai and Anca (2019) quantitatively measure applications of big data on organizational performance with a meta-analysis.

Applications of big data analytics may also support research in organizational theory (Brock & Khan, 2017).  Brock and Khan (2017) explain how perceived usefulness of adoption of big data analytics can improve business and academic applications.  Nembhard et al. (2020) explore changes and research gaps of organizational theory in hospital health care management settings. 

Big data analytics may also provide tools measuring organizational change (Riyanto, Purnama Putri, & Abdul Rahman, 2020).  Riyanto et al. (2020) describe a social cognitive theory where human behavior is reciprocal to explain self-efficacy as a policy for organizational change.  Leitão and Ferreira (2021) consider the ability for material production to be a dominant factor for choosing environmentally friendly technologies.  Khine and Shun (2017) review applications of big data analytics systems in different types of organizations.

 

 

Brock, V., & Khan, H. U. (2017). Big data analytics: does organizational factor matters impact technology acceptance? Journal of Big Data, 4(1), 21. doi:10.1186/s40537-017-0081-8

Khine, P. P., & Shun, W. Z. (2017). Big data for organizations: A review. Journal of Computer and Communications, 5, 40-48.

Leitão, J., & Ferreira, J. (2021). Dynamic effects of material production and environmental sustainability on economic vitality indicators: A panel VAR approach. Journal of Risk and Financial Management, 14(2). doi:10.3390/jrfm14020074

Mihai, B., & Anca, B. (2019). Big data analytics and organizational performance: A meta-analysis study. Management and Economics Review, 4(2), 1-13.

Nembhard, I. M., Flood, A. B., Kimberly, J. R., Kovner, A. R., Shortell, S. M., & Zinn, J. S. (2020). Moving organizational theory in health care forward: A discussion with suggestions for critical advancements. Health Care Management Review, 45(1).

Riyanto, D. W. U., Purnama Putri, V., & Abdul Rahman, R. (2020). Effect of perceived organizational support and self-efficacy to change readiness for change in hospital of Muhammadiyah Malang University, Indonesia. Humanities & Social Sciences Reviews, 8(5), 199-209. doi:10.18510/hssr.2020.8519

Shabbir, M. Q., & Gardezi, S. B. W. (2020). Application of big data analytics and organizational performance: the mediating role of knowledge management practices. Journal of Big Data, 7(1), 47. doi:10.1186/s40537-020-00317-6



Artificial intelligence in developing policies for health care and education



Artificial intelligence may have applications for developing public and private sector responses to the global pandemic (van der Schaar et al., 2020).  van der Schaar et al. (2020) describe how public and private sectors can cooperate to apply artificial intelligence in developing plans and responses to pandemics.  Valdova, Penna, Tobin, and Fishbein (2020) discuss the importance of focused data collection to support identifying treatments in the pandemic.

The global pandemic has also increased the difficulty of providing effective learning environments in education (Li, An, & Ren, 2020).  Policy makers may be able to apply artificial intelligence in increasing resources to support educational environments (van der Schaar et al., 2020).  Li et al. (2020) study the habits of educators and students in online learning environments during the pandemic.  Policy makers may need to change the model of approaching the effects on education from a data protection issue to an ethical issue (Kazim & Koshiyama, 2020).  Kazim and Koshiyama (2020) discuss the challenges in applying a data protection lens to the complex assessment of ethical issues in artificial intelligence.

 

 

Kazim, E., & Koshiyama, A. (2020). The interrelation between data and AI ethics in the context of impact assessments. AI and Ethics. doi:10.1007/s43681-020-00029-w

Li, M., An, Z., & Ren, M. (2020). Student-centred webcast + home-based learning model and investigation during the COVID-19 epidemic. Inteligencia Artificial, 23(66), 51-65.

Valdova, V., Penna, S., Tobin, M., & Fishbein, J. (2020). The value of real-world evidence for clinicians and clinical researchers in the coronavirus crisis. Gazette of Pharmacology and Clinical Research, 1(1).

van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., . . . Ercole, A. (2020). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning. doi:10.1007/s10994-020-05928-x



Artificial intelligence in developing policies for health care and education



Artificial intelligence may have applications for developing public and private sector responses to the global pandemic (van der Schaar et al., 2020).  van der Schaar et al. (2020) describe how public and private sectors can cooperate to apply artificial intelligence in developing plans and responses to pandemics.  Valdova, Penna, Tobin, and Fishbein (2020) discuss the importance of focused data collection to support identifying treatments in the pandemic.

The global pandemic has also increased the difficulty of providing effective learning environments in education (Li, An, & Ren, 2020).  Policy makers may be able to apply artificial intelligence in increasing resources to support educational environments (van der Schaar et al., 2020).  Li et al. (2020) study the habits of educators and students in online learning environments during the pandemic.  Policy makers may need to change the model of approaching the effects on education from a data protection issue to an ethical issue (Kazim & Koshiyama, 2020).  Kazim and Koshiyama (2020) discuss the challenges in applying a data protection lens to the complex assessment of ethical issues in artificial intelligence.

 

 

Kazim, E., & Koshiyama, A. (2020). The interrelation between data and AI ethics in the context of impact assessments. AI and Ethics. doi:10.1007/s43681-020-00029-w

Li, M., An, Z., & Ren, M. (2020). Student-centred webcast + home-based learning model and investigation during the COVID-19 epidemic. Inteligencia Artificial, 23(66), 51-65.

Valdova, V., Penna, S., Tobin, M., & Fishbein, J. (2020). The value of real-world evidence for clinicians and clinical researchers in the coronavirus crisis. Gazette of Pharmacology and Clinical Research, 1(1).

van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., . . . Ercole, A. (2020). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning. doi:10.1007/s10994-020-05928-x



Human application of computational tools for social benefit



Humans can apply computational tools for solving social issues and providing benefits to humanity (Abebe et al., 2020).  Abebe et al. (2020) discuss the roles that people can offer to computers to assist in improving social change.  Rolfsen and Lassen (2020) describe the increasing role of digital tools and opportunities for mobile tools.  Abebe, Adamic, and Kleinberg (2018) caution that the overexposure of digital tools to reduce effectiveness in marketing strategies.

Artificial intelligence may offer tools to provide better medical and health services Carlile et al. (2020).  Carlile et al. (2020) provide examples of artificial intelligence in emergency department health care such as predicting patient volume, cardiac complications, or other medical diagnoses.    Hossny and Iskander (2020) employ a biomechanical simulation environment to demonstrate the ability of artificial intelligence to improve stabilization of human posture.  Improvements to artificial intelligence may also more efficiently simulate and recognize human behavior (Liang & Nie, 2020).  Ludtke and Kirste (2020) introduce Bayesian filtering to improve human activity recognition in dynamic environments.  Liang and Nie (2020) apply cluster analysis and support vector machines to apply machine learning to learning recommendations and interventions.

Magrani (2019) describe the challenging complexity with law and ethics in the increasing complexity of the connected world of artificial intelligence.  The complexity of connected devices, sensors, and humans are developing new dilemmas in legal and ethical practices (Magrani, 2019).  One of the areas that legal challenges arise for artificial intelligence is in finance (Hassani, 2020).  Hassani (2020) studies bias in credit scoring for loan applications by examining publicly available datasets for gender and ethnicity.  Another challenge for artificial intelligence is in workplace automation (Danaher & Nyholm, 2020).  Danaher and Nyholm (2020) explain how automation in the workplace can reduce the value and commitment of tasks once completed by humans.  Danaher and Nyholm (2020) suggest that stressing the importance of teamwork and increasing outlets for achievement can mitigate the human context of achievement gaps in the workplace.

 

 

Abebe, R., Adamic, L. A., & Kleinberg, J. (2018). Mitigating overexposure in viral marketing.

Abebe, R., Barocas, S., Kleinberg, J., Levy, K., Raghavan, M., & Robinson, D. G. (2020). Roles for computing in social change. Paper presented at the Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.

Carlile, M., Hurt, B., Hsiao, A., Hogarth, M., Longhurst, C. A., & Dameff, C. (2020). Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department. Journal of the American College of Emergency Physicians Open, n/a(n/a). doi:https://doi.org/10.1002/emp2.12297

Danaher, J., & Nyholm, S. (2020). Automation, work and the achievement gap. AI and Ethics. doi:10.1007/s43681-020-00028-x

Hassani, B. K. (2020). Societal bias reinforcement through machine learning: a credit scoring perspective. AI and Ethics. doi:10.1007/s43681-020-00026-z

Hossny, M., & Iskander, J. (2020). Just Don’t Fall: An AI Agent’s Learning Journey Towards Posture Stabilisation. AI, 1(2), 286-298.

Liang, J., & Nie, Y. (2020). A Hybrid Teaching Mode Based on Machine Learning Algorithm. The Open Artificial Intelligence Journal, 6, 22-28.

Ludtke, S., & Kirste, T. (2020). Lifted Bayesian Filtering in Multiset Rewriting Systems. Journal of Artificial Intelligence Research, 69, 1203-1254.

Magrani, E. (2019). New perspectives on ethics and the laws of artificial intelligence. Internet Policy Review: Journal on internet regulation, 8(3).

Rolfsen, C. N., & Lassen, A. K. (2020). On-site inspections: The shift from forms to digital capture. Organization, Technology and Management in Construction, 11, 2064–2071.



Limitations of facial recognition technologies in real world applications



As the capabilities of artificial intelligence for facial recognition increase, potential failures of the datasets to represent all populations may present challenges for applications of the new technology Denton, Hutchinson, Mitchell, and Gebru (2019).  Denton et al. (2019) explain the dangers of facial recognition technologies in making high stake decisions such as authentication and surveillance tracking.  Alrubaish and Zagrouba (2020) evaluate different techniques that are applied in facial recognition technology.  Buolamwini and Gebru (2018) identified discrepancies in the ability of facial recognition systems to accurately classify individuals when comparing skin complexions.

The ability of facial recognition technology to represent all populations may be representative of larger inconsistencies in applications of machine learning technology.  Raji et al. (2020) discuss limitations in the datasets that facial recognition technologies rely on to be representative of all populations.  Jo and Gebru (2020) discuss how machine learning algorithms for digital archives face challenges with issues such as ethics and privacy.

 

 

Alrubaish, H. A., & Zagrouba, R. (2020). The Effects of Facial Expressions on Face Biometric System’s Reliability. Information, 11(10), 485.

Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Paper presented at the Proceedings of the 1st Conference on Fairness, Accountability and Transparency, Proceedings of Machine Learning Research. http://proceedings.mlr.press

Denton, E., Hutchinson, B., Mitchell, M., & Gebru, T. (2019). Detecting bias with generative counterfactual face attribute augmentation. arXiv preprint arXiv:1906.06439.

Jo, E. S., & Gebru, T. (2020). Lessons from archives: strategies for collecting sociocultural data in machine learning. Paper presented at the Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.

Raji, I. D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., & Denton, E. (2020). Saving face: Investigating the ethical concerns of facial recognition auditing. Paper presented at the Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society.