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

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Dashboards and reporting of data.

Research in Big Data News

Research in Big Data

Sep 1, 2021 Issue 20



Big data and archeological research



Ethical issues in archeological research may be subject to the institutional review board (White, 2020).  White (2020) describes the history of the development of human subjects research protection and institutional review boards.  Kim (2012) emphasizes the importance of protection of human subjects in clinical research.  Rosen (1980) discussed the ethical problems associated with the excavation of Native American burial sites and the views of Native Americans of preserving sites for their spiritual ancestors.  Okeke, Ibenwa, and Okeke (2017) discuss some of the differences between African traditional religions and Western religions in burial ceremonies which may lead to ethical problems with excavations.

Information technology may provide tools to support archeological research (Anderson, Galvin, & de Torres Rodriguez, 2018).  Anderson et al. (2018) describe a growth in digital technologies for archeological research and practice.  Hildebrand et al. (2018) discuss a social motivation for landmarks and public structures in orchestrating labour and ritual.  Wegner (2017) explains the burial of watercraft in tombs in Egypt.  Fahlander (2020) outlines the history and development of burial archeology.

Data analysis, machine learning, and artificial intelligence may support archeological research (Sifogeorgaki et al., 2020).  Sifogeorgaki et al. (2020) conduct a hierarchical density based spatial clustering algorithm for sediment analysis and Pleistocene sequencing.  Byron et al. (2021) apply CT imaging for studying Ecuadorian tsantsas.  Ekblom, Shoemaker, Gillson, Lane, and Lindholm (2019) describe an integrated landscape analysis for an inclusive network based approach to maintaining biocultural heritage.

 

 

References:

 

Anderson, H., Galvin, E., & de Torres Rodriguez, J. (2018). Museological approaches to the management of digital research and engagement: The African rock art image project. African Archaeological Review, 35(2), 321-337. doi:10.1007/s10437-018-9280-8

Byron, C. D., Kiefer, A. M., Thomas, J., Patel, S., Jenkins, A., Fratino, A. L., & Anderson, T. (2021). The authentication and repatriation of a ceremonial tsantsa to its country of origin (Ecuador). Heritage Science, 9(1), 50. doi:10.1186/s40494-021-00518-z

Ekblom, A., Shoemaker, A., Gillson, L., Lane, P., & Lindholm, K.-J. (2019). Conservation through biocultural heritage—examples from Sub-Saharan Africa. Land, 8(1). doi:10.3390/land8010005

Fahlander, F. (2020). Becoming dead: Burial assemblages as vitalist devices. Cambridge Archaeological Journal, 30(4), 555-569. doi:10.1017/S0959774320000116

Hildebrand, E. A., Grillo, K. M., Sawchuk, E. A., Pfeiffer, S. K., Conyers, L. B., Goldstein, S. T., . . . Wang, H. (2018). A monumental cemetery built by eastern Africa’s first herders near Lake Turkana, Kenya. Proceedings of the National Academy of Sciences, 115(36), 8942. doi:10.1073/pnas.1721975115

Kim, W. O. (2012). Institutional review board (IRB) and ethical issues in clinical research. Korean Journal of Anesthesiology, 62(1), 3-12. doi:10.4097/kjae.2012.62.1.3

Okeke, C. O., Ibenwa, C. N., & Okeke, G. T. (2017). Conflicts between African Traditional Religion and Christianity in Eastern Nigeria: The Igbo example. SAGE Open, 7(2), 2158244017709322. doi:10.1177/2158244017709322

Rosen, L. (1980). The excavation of American Indian burial sites: A problem in law and professional responsibility. American Anthropologist, 82(1), 5-27. doi:https://doi.org/10.1525/aa.1980.82.1.02a00010

Sifogeorgaki, I., Klinkenberg, V., Esteban, I., Murungi, M., Carr, A. S., van den Brink, V. B., & Dusseldorp, G. L. (2020). New excavations at Umhlatuzana Rockshelter, KwaZulu-Natal, South Africa: A stratigraphic and taphonomic evaluation. African Archaeological Review, 37(4), 551-578. doi:10.1007/s10437-020-09410-w

Wegner, J. (2017). A royal boat burial and watercraft tableau of Egypt's 12th Dynasty (c.1850 BCE) at South Abydos. International Journal of Nautical Archaeology, 46(1), 5-30. doi:https://doi.org/10.1111/1095-9270.12203

White, M. G. (2020). Why human subjects research protection is important. Ochsner Journal, 20(1), 16. doi:10.31486/toj.20.5012



Applications of big data in geographic information systems



Data visualization may provide opportunities for greater participation in geographic information systems research (Johnson, Cozart, & Isokpehi, 2019).    Hofmann, Münster, and Noennig (2019) evaluate digital participation of the public in urban planning projects.  Johnson et al. (2019) describe applications of data visualization for agriculture in health promotion research.  Benghadbane (2017) provide a data visualization with spatial distribution of sanitation equipment to identify hospitals with the closest proximity of these services.

A common application of geographic information systems has been in land coverage analysis (Kamusoko, Kamusoko, Chikati, & Gamba, 2021).  Ganasri and Dwarakish (2015) evaluate remote sensing and geographic information systems for land cover analysis.  Kamusoko et al. (2021) apply satellite imagery analysis and land cover classification and identified limitations of land cover analysis in urban areas.

Geographic information systems may support health research, analysis of the aging population, and facilitation of public services (Thornton, Pearce, & Kavanagh, 2011).  Thornton et al. (2011) provide guidance for integrating geographic information systems with health research such as social science, epidemiology, and studies of obesity.  Duarte and Teodoro (2021) provide a systemic literature review of the open source tools available for geographic information systems development.  Aksoy and Korkmaz-Yaylagul (2019) explore strategies for planning aging in urban analysis.  Mahony et al. (2019) similarly evaluate contributors for emergency visits of the elderly with geographic distributions.  Liu, Hung, Tse, and Saggau (2020) determine the effectiveness of geographic information systems in assisting communities with identification of municipal facilities.

Urban planning may implement geographic information systems may implement climate analysis for influences in prediction and forecasting  (Heinonen & Czepkiewicz, 2021).  Heinonen and Czepkiewicz (2021) explore how travel related to tourism influences climate change for urban planning.  Georgiadou and Reckien (2018) evaluate strategies for geographic information systems in renewable energies, climate change, bioenergy generation, and water supply in urban planning.

 

 

References:

Aksoy, E., & Korkmaz-Yaylagul, N. (2019). Assessing liveable cities for older people in an urban district in Turkey using the analytical hierarchy process. Urban Planning, 4(2), 83–95.

Benghadbane, F. (2017). The geographic information systems (GIS) application in the evaluation of sanitary services in the big Algerian cities empirical study on the city of Annaba. Journal of Remote Sensing & GIS, 6(4).

Duarte, L., & Teodoro, A. C. (2021). GIS open-source plugins development: A 10-year bibliometric analysis on scientific literature. Geomatics, 1(2). doi:10.3390/geomatics1020013

Ganasri, B. P., & Dwarakish, G. S. (2015). Study of land use/land cover dynamics through classification algorithms for harangi catchment area, Karnataka State, India. Aquatic Procedia, 4, 1413-1420. doi:https://doi.org/10.1016/j.aqpro.2015.02.183

Georgiadou, Y., & Reckien, D. (2018). Geo-information tools, governance, and wicked policy problems. ISPRS International Journal of Geo-Information, 7(1), 21.

Heinonen, J., & Czepkiewicz, M. (2021). Cities, long-distance travel, and climate impacts. Urban Planning, 6(2), 228–231.

Hofmann, M., Münster, S., & Noennig, J. R. (2019). A theoretical framework for the evaluation of massive digital participation systems in urban planning. Journal of Geovisualization and Spatial Analysis, 4(1), 3. doi:10.1007/s41651-019-0040-3

Johnson, M. O., Cozart, T., & Isokpehi, R. D. (2019). Harnessing knowledge for improving access to fruits and vegetables at farmers markets: Interactive data visualization to inform food security programs and policy. Health Promotion Practice, 21(3), 390-400. doi:10.1177/1524839919877172

Kamusoko, C., Kamusoko, O. W., Chikati, E., & Gamba, J. (2021). Mapping urban and peri-urban land cover in Zimbabwe: Challenges and opportunities. Geomatics, 1(1). doi:10.3390/geomatics1010009

Liu, H. K., Hung, M. J., Tse, L. H., & Saggau, D. (2020). Strengthening urban community governance through geographical information systems and participation: An evaluation of my Google Map and service coordination. Australian Journal of Social Issues, 55(2), 182-200. doi:https://doi.org/10.1002/ajs4.98

Mahony, E., Ní Shé, É., Bailey, J., Mannan, H., McAuliffe, E., Ryan, J., . . . Cooney, M. T. (2019). Using geographic information systems to map older people’s emergency department attendance for future health planning. Emergency Medicine Journal, 36(12), 748. doi:10.1136/emermed-2018-207952

Thornton, L. E., Pearce, J. R., & Kavanagh, A. M. (2011). Using geographic information systems (GIS) to assess the role of the built environment in influencing obesity: a glossary. International Journal of Behavioral Nutrition and Physical Activity, 8(71).



Big data and climate research



Sustainability models may provide support for climate adaptation and urban planning (Mallette, Smith, Elrick-Barr, Blythe, & Plummer, 2021; Rauter, Globocnik, Perl-Vorbach, & Baumgartner, 2019).  Mallette et al. (2021) systematically review research on climate change and categorize preferences for coastal adaptation.  Chang, Sabatini-Marques, da Costa, Selig, and Yigitcanlar (2018) provide a systematic review of knowledge based development for sustainable smart city model assessments.  Pearce (2012) explores the capabilities of open source development in appropriate technology.  Gherghina, Onofrei, Vintilă, and Armeanu (2018) measure fixed-effect regressions for sustainable economic growth in the transportation industry.  Rauter et al. (2019) compare economic and sustainable innovation in business collaborations with external partners.  Bel, Bracons, and Anderberg (2021)  process spatial datasets with radiometric calibration, atmospheric correction, geometric correction, and mosaic to classify land areas in urban expansion prediction.

Research on views on climate change may promote productive global discussion (Droz, 2021; Jakubik, 2021; Wilson, 2021).  Jakubik (2021) explores premises for changes in how institutions of education develop organizational changes to develop wisdom capital.  Droz (2021) evaluate four perspectives on climate change, phenomenological, institutional and sociocultural, spatial aspect and global interconnection, and temporal aspect and intergenerational transmission.  Wilson (2021) explores perspectives on climate change inaction and suggests approaches to improve global discussions.

 

 

References:

 

Bel, N., Bracons, G., & Anderberg, S. (2021). Finding evidence of fraudster companies in the CEO’s letter to shareholders with sentiment analysis. Information, 12(8). doi:10.3390/info12080307

Chang, D. L., Sabatini-Marques, J., da Costa, E. M., Selig, P. M., & Yigitcanlar, T. (2018). Knowledge-based, smart and sustainable cities: a provocation for a conceptual framework. Journal of Open Innovation: Technology, Market, and Complexity, 4(1), 5. doi:10.1186/s40852-018-0087-2

Droz, L. (2021). Distribution of responsibility for climate change within the milieu. Philosophies, 6(3). doi:10.3390/philosophies6030062

Gherghina, Ş. C., Onofrei, M., Vintilă, G., & Armeanu, D. Ş. (2018). Empirical evidence from EU-28 countries on resilient transport infrastructure systems and sustainable economic growth. Sustainability, 10(8). doi:10.3390/su10082900

Jakubik, M. (2021). Searching for practical wisdom in higher education with logos, pathos and ethos. Case: Finnish Universities of Sciences. Philosophies, 6(3). doi:10.3390/philosophies6030063

Mallette, A., Smith, T. F., Elrick-Barr, C., Blythe, J., & Plummer, R. (2021). Understanding preferences for coastal climate change adaptation: A systematic literature review. Sustainability, 13(15). doi:10.3390/su13158594

Pearce, J. M. (2012). The case for open source appropriate technology. Environment, Development and Sustainability, 14(3), 425-431. doi:10.1007/s10668-012-9337-9

Rauter, R., Globocnik, D., Perl-Vorbach, E., & Baumgartner, R. J. (2019). Open innovation and its effects on economic and sustainability innovation performance. Journal of Innovation & Knowledge, 4(4), 226-233. doi:https://doi.org/10.1016/j.jik.2018.03.004

Wilson, P. J. (2021). Climate change inaction and optimism. Philosophies, 6(3). doi:10.3390/philosophies6030061



Data protection, security, and ethics in machine learning systems



Data protection, security, and ethics may continue to be topics for concern with advances in machine learning systems (Scantamburlo, 2021).   Scantamburlo (2021) discuss challenges in implementing fairness in machine learning applications.  Mühlhoff (2021) review challenges to privacy and data protection in prediction analytics systems.  Krügel, Uhl, and Balcombe (2021) identify complexities in programming ethical decision making systems for autonomous vehicle safety.

 

 

References:

 

Krügel, S., Uhl, M., & Balcombe, B. (2021). Automated vehicles and the morality of post-collision behavior. Ethics and Information Technology. doi:10.1007/s10676-021-09607-w

Mühlhoff, R. (2021). Predictive privacy: towards an applied ethics of data analytics. Ethics and Information Technology. doi:10.1007/s10676-021-09606-x

Scantamburlo, T. (2021). Non-empirical problems in fair machine learning. Ethics and Information Technology. doi:10.1007/s10676-021-09608-9