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Journal articles on the topic 'Big Data'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 April 2025

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1

A, Eicher,. "Big business with Big Data Big Business mit Big Data." GIS Business 12, no. 3 (June 12, 2019): 20–25. http://dx.doi.org/10.26643/gis.v12i3.5173.

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2

Sizov, Ivan Aleksandrovich. "BIG DATA – BIG DATA IN BUSINESS." Economy. Business. Computer science, no. 3 (January 1, 2016): 8–23. http://dx.doi.org/10.19075/2500-2074-2016-3-8-23.

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3

Habegger, Benjamin. "Big Data vs. Privacy Big Data." Services Transactions on Big Data 1, no. 1 (January 2014): 25–35. http://dx.doi.org/10.29268/stbd.2014.1.1.3.

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4

Weber, A. S. "The Big Student Big Data Grab." International Journal of Information and Education Technology 6, no. 1 (2016): 65–70. http://dx.doi.org/10.7763/ijiet.2016.v6.660.

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5

Mager, Astrid. "The politics of big data. Big data, big brother?" Information, Communication & Society 22, no. 10 (January 22, 2019): 1523–25. http://dx.doi.org/10.1080/1369118x.2019.1567804.

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6

Viceconti, Marco, Peter Hunter, and Rod Hose. "Big Data, Big Knowledge: Big Data for Personalized Healthcare." IEEE Journal of Biomedical and Health Informatics 19, no. 4 (July 2015): 1209–15. http://dx.doi.org/10.1109/jbhi.2015.2406883.

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7

Riahi, Youssra, and Sara Riahi. "Big Data and Big Data Analytics: concepts, types and technologies." International Journal of Research and Engineering 5, no. 9 (November 2018): 524–28. http://dx.doi.org/10.21276/ijre.2018.5.9.5.

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8

Lesk, Michael. "Big Data, Big Brother, Big Money." IEEE Security & Privacy 11, no. 4 (July 2013): 85–89. http://dx.doi.org/10.1109/msp.2013.81.

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9

West, Stephen W., Jo Clubb, Tracy A. Blake, James Fern, Harry Bowles, and Torstein Dalen-Lorentsen. "Big data. Big potential. Big problems?" BMJ Open Sport & Exercise Medicine 10, no. 2 (April 2024): e001994. http://dx.doi.org/10.1136/bmjsem-2024-001994.

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10

Martin, Joseph. "Big data, big future." BioTechniques 68, no. 4 (April 2020): 166–68. http://dx.doi.org/10.2144/btn-2020-0027.

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11

Waldherr, Annie, Daniel Maier, Peter Miltner, and Enrico Günther. "Big Data, Big Noise." Social Science Computer Review 35, no. 4 (May 9, 2016): 427–43. http://dx.doi.org/10.1177/0894439316643050.

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In this article, we focus on noise in the sense of irrelevant information in a data set as a specific methodological challenge of web research in the era of big data. We empirically evaluate several methods for filtering hyperlink networks in order to reconstruct networks that contain only webpages that deal with a particular issue. The test corpus of webpages was collected from hyperlink networks on the issue of food safety in the United States and Germany. We applied three filtering strategies and evaluated their performance to exclude irrelevant content from the networks: keyword filtering, automated document classification with a machine-learning algorithm, and extraction of core networks with network-analytical measures. Keyword filtering and automated classification of webpages were the most effective methods for reducing noise, whereas extracting a core network did not yield satisfying results for this case.

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12

Pflugfelder, Ehren Helmut. "Big data, big questions." Communication Design Quarterly 1, no. 4 (August 2013): 18–21. http://dx.doi.org/10.1145/2524248.2524253.

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Jarvis, Suzanne. "Big data, big picture." Veterinary Record 183, no. 12 (September 28, 2018): 363. http://dx.doi.org/10.1136/vr.k4081.

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McKenna, Josephine. "Big data: big promise." European Heart Journal 38, no. 7 (February 14, 2017): 470–71. http://dx.doi.org/10.1093/eurheartj/ehx021.

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15

Kitner, Kathi R., and Thea de Wet. "Big city, big data." Interactions 22, no. 4 (June 25, 2015): 70–73. http://dx.doi.org/10.1145/2788453.

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16

Commissariat, Tushna. "Big data, big responsibilities." Physics World 30, no. 9 (September 2017): 39. http://dx.doi.org/10.1088/2058-7058/30/9/38.

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17

Boland, Michael V. "Big Data, Big Challenges." Ophthalmology 123, no. 1 (January 2016): 7–8. http://dx.doi.org/10.1016/j.ophtha.2015.08.041.

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18

Anoushiravani, Afshin A., Jason Patton, Zain Sayeed, Mouhanad M. El-Othmani, and Khaled J. Saleh. "Big Data, Big Research." Orthopedic Clinics of North America 47, no. 4 (October 2016): 717–24. http://dx.doi.org/10.1016/j.ocl.2016.05.008.

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19

Shaha, Steven H., Zain Sayeed, Afshin A. Anoushiravani, Mouhanad M. El-Othmani, and Khaled J. Saleh. "Big Data, Big Problems." Orthopedic Clinics of North America 47, no. 4 (October 2016): 725–32. http://dx.doi.org/10.1016/j.ocl.2016.05.009.

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20

Clarke, Roger. "Big data, big risks." Information Systems Journal 26, no. 1 (December 15, 2015): 77–90. http://dx.doi.org/10.1111/isj.12088.

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21

Mackie, P., F. Sim, and C. Johnman. "Big data! Big deal?" Public Health 129, no. 3 (March 2015): 189–90. http://dx.doi.org/10.1016/j.puhe.2015.02.013.

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22

van Lonkhuyzen, Peter. "Big data, Big risk." Lucide 4, no. 2 (March 2015): 34–41. http://dx.doi.org/10.1007/s40408-015-0037-z.

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23

Wrobel, Stefan, Hans Voss, Joachim Köhler, Uwe Beyer, and Sören Auer. "Big Data, Big Opportunities." Informatik-Spektrum 38, no. 5 (June 24, 2014): 370–78. http://dx.doi.org/10.1007/s00287-014-0806-4.

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24

R., Sahil, and Aarati Mahajan. "Big Challenges? Big Data …" International Journal of Computer Applications 131, no. 11 (December 17, 2015): 14–18. http://dx.doi.org/10.5120/ijca2015907452.

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25

König, René. "Big Data, Big Problems?" TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis 25, no. 2 (August 1, 2016): 101–3. http://dx.doi.org/10.14512/tatup.25.2.101.

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26

Clancy, Thomas R., and Laura Reed. "Big Data, Big Challenges." JONA: The Journal of Nursing Administration 46, no. 3 (March 2016): 113–15. http://dx.doi.org/10.1097/nna.0000000000000307.

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27

Pressman, Jessica. "Big Novels/Big Data." American Book Review 37, no. 2 (2016): 14. http://dx.doi.org/10.1353/abr.2016.0012.

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28

Wogan, David. "Big Data, Big Energy." Scientific American 310, no. 1 (December 17, 2013): 22. http://dx.doi.org/10.1038/scientificamerican0114-22b.

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29

Laurance, William F., Frédéric Achard, Stephen Peedell, and Susanne Schmitt. "Big data, big opportunities." Frontiers in Ecology and the Environment 14, no. 7 (September 2016): 347. http://dx.doi.org/10.1002/fee.1316.

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30

Jiang, Min, and King-Wa Fu. "Chinese Social Media and Big Data: Big Data, Big Brother, Big Profit?" Policy & Internet 10, no. 4 (December 2018): 372–92. http://dx.doi.org/10.1002/poi3.187.

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31

Samaranayake, L. "Big data is or big data are." British Dental Journal 224, no. 12 (June 2018): 916. http://dx.doi.org/10.1038/sj.bdj.2018.486.

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32

Cobb, Adrienne N., Andrew J. Benjamin, Erich S. Huang, and Paul C. Kuo. "Big data: More than big data sets." Surgery 164, no. 4 (October 2018): 640–42. http://dx.doi.org/10.1016/j.surg.2018.06.022.

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33

Broome, Marion E. "Big data, data science, and big contributions." Nursing Outlook 64, no. 2 (March 2016): 113–14. http://dx.doi.org/10.1016/j.outlook.2016.02.001.

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34

Kumar, S. Senthil, and Ms V. Kirthika. "Big Data Analytics Architecture and Challenges, Issues of Big Data Analytics." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (October 31, 2017): 669–73. http://dx.doi.org/10.31142/ijtsrd4673.

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35

CURRAN, JOHN. "Big Data or ‘Big Ethnographic Data’? Positioning Big Data within the ethnographic space." Ethnographic Praxis in Industry Conference Proceedings 2013, no. 1 (September 2013): 62–73. http://dx.doi.org/10.1111/j.1559-8918.2013.00006.x.

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36

Frelinger, Jeffrey A. "Big Data, Big Opportunities, and Big Challenges." Journal of Investigative Dermatology Symposium Proceedings 17, no. 2 (November 2015): 33–35. http://dx.doi.org/10.1038/jidsymp.2015.38.

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37

Migdał-Najman, Kamila, and Krzysztof Najman. "BIG DATA = CLEAR + DIRTY + DARK DATA." Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, no. 469 (2017): 131–39. http://dx.doi.org/10.15611/pn.2017.469.13.

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38

Arputhamary, B., and L. Arockiam. "Data Integration in Big Data Environment." Bonfring International Journal of Data Mining 5, no. 1 (February 10, 2015): 01–05. http://dx.doi.org/10.9756/bijdm.8001.

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39

Harford, Tim. "Big data: A big mistake?" Significance 11, no. 5 (December 2014): 14–19. http://dx.doi.org/10.1111/j.1740-9713.2014.00778.x.

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40

Wright, Alex. "Big data meets big science." Communications of the ACM 57, no. 7 (July 2014): 13–15. http://dx.doi.org/10.1145/2617660.

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41

Dhar, Vasant. "Why Big Data = Big Deal." Big Data 2, no. 2 (June 2014): 55–56. http://dx.doi.org/10.1089/big.2014.1522.

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42

Landhuis, Esther. "Neuroscience: Big brain, big data." Nature 541, no. 7638 (January 2017): 559–61. http://dx.doi.org/10.1038/541559a.

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43

Festa, Giuseppe, Imen Safraou, Maria Teresa Cuomo, and Ludovico Solima. "Big data for big pharma." Business Process Management Journal 24, no. 5 (September 3, 2018): 1110–23. http://dx.doi.org/10.1108/bpmj-10-2017-0272.

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Purpose Big pharma, which comprise the most important companies in the pharmaceutical sector, are ambidextrous organizations by nature. Big data can heavily influence this characteristic by simultaneously requiring adequate business process management. In fact, the impact of big data on business process management can assist big pharma in increasing process efficiency (which is related to the research and development pipeline) and process efficacy (related to product portfolio management). The purpose of this paper is to investigate this possibility and opportunity. Design/methodology/approach In the absence of specific scientific studies, as indicated by a review of the existing literature, the authors have adopted a grounded theory approach. This research has observed multiple cases (the 15 most important big pharma companies worldwide) through an electronic survey conducted on secondary data. The study has allowed the generation of a theoretical framework based on the (direct) relationship between knowledge process standardization (as the dependent variable) and big data (as the independent variable) in organizations oriented toward ambidexterity, such as big pharma in the specific scope of this research. Findings As big data utilization becomes widespread along the pipeline (or even along the value chain/supply chain), business process management increasingly uses (or tends to use) standardization, adopting process standardization as the main coordination mechanism to manage big knowledge. This theory is even more true when considering the moderating role of ambidexterity. An enterprise oriented toward structural ambidexterity (such as big pharma) that uses big data will require increased process standardization to manage big knowledge. Alternatively, an enterprise oriented toward contextual ambidexterity that uses big data will require increased output standardization. Originality/value Based on an analytical literature review, no research to date has focused strict attention on the influence that big data can have on business process management to improve the natural ambidexterity of big pharma. The main unique feature of this research relies on this point. The main value of the research originates from the theoretical framework reconstructed by grounded theory, which constitutes a powerful strategic tool to support executives and managers of big pharma in organizing business process management for their ambidextrous organizations using big data.

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44

Marusina, Kate. "Big Data Requires Big Solutions." Genetic Engineering & Biotechnology News 32, no. 15 (September 2012): 1, 34–40. http://dx.doi.org/10.1089/gen.32.15.12.

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45

Wax, Amy, Raquel Asencio, and Dorothy R. Carter. "Thinking Big About Big Data." Industrial and Organizational Psychology 8, no. 4 (December 2015): 545–50. http://dx.doi.org/10.1017/iop.2015.81.

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Guzzo, Fink, King, Tonidandel, and Landis (2015) review important issues—privacy, informed consent, and data/data analysis integrity—that are critical logistical considerations in any program of research with human subjects, including studies utilizing big data. Overall, we agree with the cautionary sentiment conveyed throughout the focal article; industrial and organizational (I-O) psychology researchers and practitioners should not assume that big data is a panacea, and many of our established disciplinary approaches for ensuring ethical and accurate research are applicable—or modifiable—in big data contexts. However, we believe that the conversation about big data in I-O psychology is broader than that reviewed by Guzzo et al., and we would like to further elaborate on the focal article. We present this commentary from our perspective as junior scholars entering the field at a critical time—a time when I-O psychology is becoming increasingly intertwined with big data science.

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46

Labant, MaryAnn. "Big Sequencing Beclouds Big Data." Genetic Engineering & Biotechnology News 35, no. 11 (June 1, 2015): 1, 32–34. http://dx.doi.org/10.1089/gen.35.11.01.

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47

White, S. M., I. K. Moppett, and R. Griffiths. "Big Data and Big Numbers." Anaesthesia 69, no. 4 (March 18, 2014): 389–90. http://dx.doi.org/10.1111/anae.12652.

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48

Wears, Robert L., and Deborah J. Williams. "Big Questions for “Big Data”." Annals of Emergency Medicine 67, no. 2 (February 2016): 237–39. http://dx.doi.org/10.1016/j.annemergmed.2015.09.019.

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49

Richert, Joachim. "Big Data ist Big Analytics." Nachrichten aus der Chemie 66, no. 7-8 (July 2018): 707. http://dx.doi.org/10.1002/nadc.20184077289.

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50

Lejay, Anne, and Nabil Chakfe. "Big Data, a Big Mistake?" European Journal of Vascular and Endovascular Surgery 57, no. 2 (February 2019): 258. http://dx.doi.org/10.1016/j.ejvs.2018.09.029.

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