Center for Business Data Analytics(cbsBDA) at the Department of Digitalization of the Copenhagen Business School conducts transdisciplinary basic research at the socio-technical intersections of computer science and social science with specific applications to managers in companies, teachers in schools and residents in cities.
cbsBDA's basic research program is aimed at modelling and explaining socio-technical interactions using set theory. Our applied research program seeks to design, develop and evaluate big data analytics applications for managers (descriptive, predictive, and prescriptive analytics), teachers (teaching analytics and learning analytics), and citizens (public health analytics).
Our current research, education, and consulting focuses on Big Social Data Analytics, GPS Analysis, Building Usage Analytics, and Bitcoin Blockchain Analytics.
cbsBDA aspires to conduct engaged scholarship in the Pasteur's Quadrant.
Our objective is not only to make seminal contributions to scientific knowledge as evidenced by peer-reviewed publications but also to create practical applications that yield meaningful facts, actionable insights, valuable outcomes, and sustainable impacts for organizations and society.
Please see CBS Research for the complete list of publications from the Center for Business Data Analytics(cbsBDA).
Vatrapu, R., Mukkamala, R., Hussain, A., Flesch, B., & Lasrado, L. (in press/2019). Social Set Analysis.Springer Series on Computational Social Sciences, New York.
Reimann, P., Bull, S., Kickmeier-Rust, M., Vatrapu,R., & Wasson, B (Editors). (2015). Measuring and Visualizing Learning in the Information-Rich Classroom. Routledge, New York.
Sun Yin, H., Harlev, M., Langenheldt, K., Mukkamala, R.M., & Vatrapu, R. (in press/2019). Regulating Cryptocurrencies: A Supervised Machine Learning Approach to De-Anonymising the Bitcoin Blockchain. Journal of Management Information Systems.
Mukkamala, R.R., Vatrapu, R., Ray, P. K., Sengupta, G., & Halder, S. (2018). Blockchain for Social Business: Principles and Applications. IEEE Engineering Management Review. doi: 10.1109/EMR.2018.2881149 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8533389&isnumber=8466592.
Kunst, K., & Vatrapu, R. (2018). Understanding electronic word of behavior: conceptualization of the observable digital traces of consumers’ behaviors. Electronic Markets. doi:10.1007/s12525-018-0301-x
Siikanen, M., Baltakys, K., Kanniainen, J., Vatrapu, R., Mukkamala, R., & Hussain, A. (2018). Facebook Drives Behavior of Passive Households in Stock Markets. Finance Research Letters, https://doi.org/10.1016/j.frl.2018.1003.1020.
Menon, K., Kärkkäinen, H., Jussila, J., Huhtamäki, J., Mukkamala, R. R., Lasrado, L., Vatrapu, R., & Hussain, A. (2018). Analysing the Role of Crowdfunding in Entrepreneurial Ecosystems: A Social Media Event Study of Two Competing Product Launches. International Journal of Entrepreneurship and Small Business.
Jensen, T., Vatrapu, R., & Bjorn-Andersen, N. (2018). Avocados Crossing Borders: The Problem of Runaway Objects and the Solution of a Shipping Information Pipeline for Improving International Trade. Information Systems Journal.
C. Mejia; B. Florian-Gaviria; R. Vatrapu; S. Bull; S. Gomez; R. Fabregat. (2017). A novel web-based approach for visualization and inspection of reading difficulties on university students. IEEE Transactions on Learning Technologies, 10(1), pp. 53-67, doi: 10.1109/TLT.2016.2626292.
Vatrapu, R., Mukkamala, R. R., Hussain, A., & Flesch, B. (2016). Social Set Analysis: A Set Theoretical Approach to Big Data Analytics. IEEE Access, 4, 2542-2571. . DOI: 10.1109/ACCESS.2016.2559584.
Zimmerman, C., Hansen, K., & Vatrapu, R. (2014). A Theoretical Model for Digital Reverberations of City Spaces and Public Places. International Journal of Electronic Government Research, 10(1), 46-62. DOI: 10.4018/ijegr.2014010104.
Flesch, B., Vatrapu, R., & Mukkamala, R. (2017). A Big Social Media Data Study of the 2017 German Federal Election based on Social Set Analysis of Political Party Facebook Pages with SoSeVi. Proceedings of the 2018 IEEE International Conference on Big Data (IEEE Big Data 2018).
Reichert1, J., Kristensen1, K. L., Mukkamala, R., & Vatrapu, R. (2017). A Supervised Machine Learning Study of Online Discussion Forums about Type-2 Diabetes. Proceedings of IEEE HealthCom 2017.
Straton, N., Mukkamala, R. R., & Vatrapu, R. (2017). Big Social Data Analytics for Public Health: Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning. Paper presented at 6th IEEE International Congress on Big Data, Honolulu, United States.
Lasrado, L., Vatrapu, R., & Andersen, K. N. (2016). A Set Theoretical Approach to Maturity Models: Guidelines and Demonstration. In ICIS 2016 Proceedings. (pp. 20). Atlanta, GA: Association for Information Systems. AIS Electronic Library (AISeL). (Proceedings / International Conference on Information Systems (ICIS), Vol. 37).
Lassen, N., Madsen, R., & Vatrapu, R. (2014). Predicting iPhone Sales from iPhone Tweets. Proceedings of IEEE 18th International Enterprise Distributed Object Computing Conference (EDOC 2014), Ulm, Germany, 81-90, ISBN: 1541-7719/1514, DOI: 1510.1109/EDOC.2014.1520.
Lassen, N.B., la Cour, L., & Vatrapu, R. (2017). Predictive Analytics with Social Media Data. In Sloan, L., & Quan-Haase, A. (Eds.), The SAGE Handbook of Social Media Research Methods. Chapter 20. London: Sage.
Privatist PhD Student
Physical infrastructure of cbsBDA is in the form of one large room that houses on-premises servers and the eye-tracking equipment. Meeting rooms and open spaces for research purposes are available from the Department of Digitalization and the central building facilities of CBS.
cbsBDA's IT infrastructure comprises of an on-premises High-Performance Computing (HPC) cluster consisting of 256 CPU cores, 1792 GB RAM, and 332 TB storage on the 1Gbps Danish Research Network with an additional Microsoft Azure HPC cloud solution. Further, there are several standalone virtual servers provisioned by CBS IT, and custom AWS cloud solutions for ongoing research and student projects.
Scientific instrumentation at cbsBDA consists of a desktop eye-tracker (SMI RED 60Hz), eye-tracking glasses (SMI ETG-2), EEG headsets (Emotiv 16-channels), EDR wrist bands and several large-screen and high-resolution displays (3x84-inches display at 4K, 27-inches display at 5K, 8x 34-inch curved ultrawide monitors).