Big Data is Crowdledge, not 3V
Crowdledge is defined as the knowledge that [weakly] emerges – and is, therefore, unexpected – from Big Data analysis of individuals’ digital footprints spontaneously left in the digital universe. It represents big data in better terms than 3Vs.
By Renato P. dos Santos, ULBRA.
I argue that Big Data is more accurately expressed by crowdledge (dos Santos, 2015), than by the famous 3 V’s: volume, velocity, and variety.
I define crowdledge as the knowledge that [weakly] emerges – and is, therefore, unexpected – from Big Data analysis of individuals’ digital footprints spontaneously left in the digital universe we live in by means of web searches, postings, ‘shares’ and ‘likes’ in social networks, phone calls, SMS messages, images and videos uploaded to sharing websites, etc. (dos Santos, 2015). I also believe that it has interesting applications to the teaching of science, as discussed below.
Here I use the concept of (weakly) emergence in the sense of a new, unexpected, non-obvious property of a complex system that is distinct from the properties of the different parts of the system, while being deductible from and caused by them (cf. Chalmers, 2006). See also (dos Santos, 2015). A well-known example is the Conway’s Game of Life.
As an example of crowdledge, Bengtsson et al. were able to estimate population movements during disasters and outbreaks rapidly and with potentially high validity in areas with high mobile phone use (2011).
Crowdledge is not the wisdom of crowds!
The wisdom of the crowd is the aggregated answers of a large group of individuals rather than the one from a single expert. The aggregated answer has often been found to be better than the one given by any of the individuals within the group. This process, in the business world at least, was discussed in detail by James Surowiecki in his book The Wisdom of Crowds (Surowiecki, 2005).
While not new to the information age, this process has been pushed into the mainstream spotlight by social information sites such as Wikipedia and Yahoo!Answers, and other web resources that rely on human opinion (Baase, 1996).
The group tends to make its best decisions if it is made up of varied opinions and ideologies. On the contrary, social influence can cause the average of the crowd answers to be wildly inaccurate (Lorenz, Rauhut, Schweitzer, & Helbing, 2011).
Crowdledge is not collective wisdom!
Collective wisdom is the shared knowledge arrived at by individuals and groups. It is contained in ancient books such as The Bible, The Torah, The Koran, the works of Confucius, Plato, and Buddha, and the many legends and myths from all cultures, aiming to make life easier/more enjoyable through understanding human behavior.
Crowdledge is not collective intelligence!
Collective intelligence is more of a shared consensus-driven decision process than the collective wisdom. Unlike collective wisdom, collective intelligence is not exclusively human and has been also associated with animal and plant life. The objective of collective intelligence is to make life easier or more enjoyable through the application of acquired knowledge. Émile Durkheim (1912) argued that Society constitutes a higher intelligence because it transcends the individual, thereby achieving collective wisdom. Pierre Lévy (1994) has recently discussed this concept in depth.
Crowdledge also has interesting applications to the teaching of science
We investigated the feasibility of using computers and free public Big Data tools, such as Google Correlate and Google Trends, as mediators in the teaching and learning of Exact Sciences. It is not merely training in computational infrastructure or predictive analytics, however. It aims at preparing our students for the scientific challenges proposed for Big Data to the real world. It also intends to give them a better understanding of the notions of phenomenon, observation, measurement, physical laws, and causal theory, among others (dos Santos & Lemes, 2014).
Baase, S. (1996). A Gift of Fire: Social, Legal, and Ethical Issues for Computing and the Internet (1st ed.). Upper Saddle River, NJ: Prentice Hall.
Bengtsson, L., Lu, X., Thorson, A., Garfield, R., & von Schreeb, J. (2011). Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS medicine, 8(8), e1001083.
Chalmers, D. J. (2006). Strong and weak emergence. In P. Clayton & P. Davies (Eds.), The reemergence of emergence (pp. 244–256). New York: OUP – Oxford University Press.
Dos Santos, R. P. (2015). Big Data: Philosophy, Emergence, Crowdledge, and Science Education. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2696627.
Dos Santos, R. P., & Lemes, I. L. (2014). Aprender-com-Big-Data no Ensino de Ciências. Acta Scientiae, 16(4): 178-198.
Durkheim, É. (1912). Les Formes élémentaires de la vie religieuse: le système totémique en Australie. Paris: PUF.
Lévy, P. (1994). L’intelligence collective – Pour une anthropologie du cyberspace. Paris: La Découverte.
Lorenz, J., Rauhut, H., Schweitzer, F., & Helbing, D. (2011). How social influence can undermine the wisdom of crowd effect. Proceedings of the National Academy of Sciences of the United States of America, 108(22), 9020–9025.
Surowiecki, J. (2005). The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. New York: Doubleday.
Bio: Renato P. dos Santos is a Researcher & Associate Professor at ULBRA, Lutheran University of Brazil. In the last 7 years, he worked with Web 2.0 technologies for STEM teaching & learning, including Big Data and computer simulations in 3D immersive virtual learning environments such as Second Life, where he developed the Second Life Physics Lab. His specialties are Physics, Internet, Web 2.0, Big Data, Second Life.
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