“Ethnography, an approach for studying everyday life as lived by groups of people, provides powerful resources for the study of the cultures of virtual worlds. As ethnographers, what interests us about virtual worlds is not what is extraordinary about them, but what is ordinary. We are intrigued not only by the individuals in a group, but by the sum of the parts. We aim to study virtual worlds as valid venues for cultural practice, seeking to understand both how they resemble and how they differ from other forms of culture. We do this by immersing our embodied selves within the cultures of interest, even when that embodiment is in the form of an avatar, the representation of self in these spaces. The goal of this handbook is to provide ethnographers with a practical set of tools and approaches for conducting successful fieldwork in virtual worlds”
“The term “Big Data” has been buzzing around in the IT industry for the last few months. Top universities in the nation are rushing to offer courses on it. Although data-rich companies such as Amazon, Google, and Yahoo! started this process, traditional companies are now exploring it as well. It is a prominent field that could transform management as we know it. In this paper, the authors look at the implications Big Data has for the overall management and direction of a company, specifically in terms of business strategy. To be sure, this paper is not about learning Big Data, but rather how it integrates with the overall strategy of a company. We begin by defining what Big Data is, and what it is not. We then look at what motivated us to write about big data and its management implications. We also look at applications of Big Data, specifically Hadoop and Google BigQuery”http://media.wix.com/ugd/36cf28_9daac86c5ceeae18162159a8fb968920.pdf
“There is, however, a new flavor of innovation on the scene: Big Data. “Big Data”is shorthand for the combination of a technology and a process. The technology is a configuration of information processing hardware capable of sifting, sorting and interrogating vast quantities of data in very short times. The process involves mining the data for patterns, distilling the patterns into predictive analytics, and applying the analytics to new data. Together, the technology and the process comprise a technique for converting data flows into a particular, highly data-intensive type of knowledge. The technique of Big Data can be used to analyze data about the physical world—for example, climate or seismological data—or it can be used to analyze physical, transactional, and behavioral data about people. So used, it is vastly more nimble than old category-driven profiling developed in the late twentieth century and now widely criticized. According to its enthusiasts, Big Data will usher in a new era of knowledge production and innovation, producing enormous benefits to science and business alike.According to its critics, Big Data is profiling on steroids, unthinkably intrusive and eerily omniscient”.
“It seems like everyone been talking about “big data” recently, speculating on the future of AI and intelligent systems. Big data has been characterized in many ways, from Doug Laney’s original 2001 “3Vs” model to the various recent extended “4Vs” descriptions. Laney’s three Vs are volume, velocity, A Big-Data Perspective on AI: Newton, Merton, and Analytics Intelligence for Complex Systems and variety; the fourth V could be variability, virtual, or value, depending on whom you ask. To most, those Vs indicate “bigness”—big size, fast movement, many types, and significant impact. To me, the “bigness” of big data is derived from its “smallness,” or more precisely, for its inclusion and use of data stemming from all degrees of volume, velocity, variety, value, variability, and so on, whether virtual or real. In particular, big data implies that the long-tail effects on personal living and business operations will be a normal mode in the future. But what does big data really mean in the era of cyberspace?”
Useful to sofisticate current (trivial) discourses about data visualization and knowledge
“As data-intensive and computational science become increasingly established as the dominant mode of conducting scientific research, visualisations of data and of the outcomes of science become increasingly prominent in mediating knowledge in the scientific arena. This position piece advocates that more attention should be paid to the epistemological role of visualisations beyond their being a cognitive aid to understanding, but as playing a crucial role in the formation of evidence for scientific claims. The new generation of computational and informational visualisations and imaging techniques challenges the philosophy of science to re-think its position on three key distinctions: the qualitative/quantitative distinction, the subjective/objective distinction, and the causal/non-causal distinction”
“The tools through which people make inquiries about society are central to the way they come to understand it and the possibility of harnessing ´big data´ are currently giving rise to a set of semiautomated tools that promise new ways to organize social inquiry. They work by programming algorithms to harness massive amounts of behavioral data on the web and synthesize it into manageable visualizations of the social. On the basis of interviews and document analyses this paper provides an analysis of the ways in which such visualizations are constructed and made sense of by project leaders across the areas of public governance, advertizing, military intelligence, strategic foresight and the social sciences. The theoretical framework of the paper is grounded in Social Construction of Technology, Actor-Network Theory and Software Studies in order to focus analytical attention on technological and discursive elements that are playing an influential role in the ´production-chain´ behind these new tools. Looking for similar elements across different industries allows for identifying ´core elements´ that are inevitably salient when constructing visualizations. Similarities in the way these elements are approached will be defined as signs of ´stabilization´ in the trend of visualizing big data whereas elements and approaches that are unique to subsets of cases and specific professional cultures will be denoted as ´flexible addons ´ to these core elements. By following the process of organizing the meaning and use of these emerging tools the paper intend open up these assemblages and specifically ponder the role of algorithms in organizing visibilit”