“The era of big data has created exciting new opportunities for research to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. With the emergence of very large-scale data collection techniques and the related new technological support, there seem to be fundamental changes that are occurring with the research questions we can ask, and the research methods we can apply. The contexts include social networks and blogs, political discourse on the Internet, corporate announcements and digital journalism, mobile telephony and digital home entertainment, online gaming and social shopping, and social advertising and social commerce – and much more. The increasingly advantageous costs of data collection, and the new capabilities that researchers have to conduct research that leverages the spectrum of micro-, meso, and macro-level data suggest the possibility of a scientific paradigm shift toward computational social science with big data. The new thinking related to empirical regularities analysis, experimental design, and longitudinal empirical research further suggests that these approaches can be especially tailored for rapid-acquisition big data contexts that involve new ways for researchers to achieve frequent, precise and meaningful observations of real-world phenomena. We discuss how our philosophy of science should be changing in step with the times, but argue against the assertion that theory no longer matters”

http://www2.sis.smu.edu.sg/icec2012/downloads/BCSI_Theme_Paper.pdf 

 

“The extensive use of Big Data has now become common in plethora of technologies and industries. From massive data bases to business intelligence and datamining applications; from search engines to recommendation systems; advancing the state of the art of voice recognition, translation and more. The design, analysis and engineering of Big Data algorithms has multiple flavors, including massive parallelism, streaming algorithms, sketches and synopses, cloud technologies, and more. We will discuss some of these aspects, and reflect on their evolution and on the interplay between the theory and practice of Big Data algorithmics”

http://link.springer.com/chapter/10.1007%2F978-3-642-33090-2_1?LI=true 

 

[…] “Big Data” becomes “Big Social Data” when it arises as a result of human-tohuman interaction, and herein lies the key to unlocking important insights about social processes operating at a worldwide scale as they unfold over time, the potential for which is greater than ever before because of the ubiquitousness of social media. Interactions can be of many kinds (conversation, exchange, response, relationship), and observed at the individual (survey response, votes, purchases), group, organization, and nation (trade, conflict, population movements) levels.  When people interact through web, mobile device and distributed sensors, digital traces of these interactions are left behind. These historic interactions  become more easily quantifiable through digitization and sharing of document and image archives. As a consequence, we face a transformative and disruptive data deluge, from which new scientific, economic, and social value can be extracted”

 

http://www.itu.dk/people/rkva/IWSB-2012/positionPapers/Goggins-CollaborativeB…

“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”

http://www.academia.edu/1999809/Making_the_Visual_Visible_in_Philosophy_of_Sc…

“The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others. In this study, we consider the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns. We present a simple mobility model for predicting peoples’ visited locations using the popularity of places in the city as an interaction parameter between different individuals. This ingredient is suf???cient to reproduce several characteristics of the observed travel behavior such as: the number of trips between different locations in the city, the exploration of new places and the frequency of individual visits of a particular location”

http://link.springer.com/content/pdf/10.1007%2Fs10955-012-0645-0

“Recent empirical studies show that successful companies are distinguished by their ability to use “big data” for strategic decision-making at senior-level (Brown et al., 2011; LaValle et al., 2011; Manyika et al., 2011; Shah et al. 2012). What is missing is a study that thoroughly explores and defines the phenomenon of metrics in the context of “big data” and that provides a holistic investigation of the interdependent role of marketing metrics and financial metrics for senior-level management within the current information and technological landscape. A research agenda is suggested to study whether senior-level managers are guided by a set of marketing metrics or whether the traditional financial metrics still dominate in organisations. In particular, this agenda explores five research challenges that deserve the attention of current and future marketing research”

134204385742Lamest_and_Brady_IAM_2012_Big_Data_and_the_Challenge_of_Marketing_Metrics_ML

The distinction between data/information/knowledge remains tipically vague. The Boisot scheme help us to frame the distinction. “Data can be treated as originating in discernible differences in physical states of the world—that is, states describable in terms of space, time, and energy…Information constitutes those significant regularities residing in the data that agents attempt to extract from it…Finally, knowledge is a set of expectations held by agents and modified by the arrival of information. These expectations embody the prior situated interactions between agents and the world—in short, the agent’s prior learning” (Boisot)

“We all say we like data, but we don’t. We like getting insight out of data. That’s not quite the same as liking the data itself. In fact, I dare say that I don’t quite care for data. It sounds like I’m not alone. It’s tough to nail down a precise definition of “Bad Data.” Some people consider it a purely hands-on, technical phenomenon: missing values, malformed records, and cranky file formats. Sure, that’s part of the picture, but Bad Data is so much more. It includes data that eats up your time, causes you to stay late at the office, drives you to tear out your hair in frustration. It’s data that you can’t access, data that you had and then lost, data that’s not the same today as it was yesterday… In short, Bad Data is data that gets in the way. There are so many ways to get there, from cranky storage, to poor representation, to misguided policy. If you stick with this data science bit long enough, you’ll certainly encounter your fair share”

http://www.amazon.com/Bad-Data-Handbook-Cleaning-Back/dp/1449321887/ref=sr_1_…

“We live in the era of Big Data, with storage and transmission capacity measured notjust in terabytes but in petabytes (where peta– denotes a quadrillion, or athousand trillion). Data collection is constant and even insidious, with every click and every”like” stored somewhere for something. This book reminds us that data is anything but”raw,” that we shouldn’t think of data as a natural resource but as a cultural one thatneeds to be generated, protected, and interpreted. The book’s essays describe eight episodes in thehistory of data from the predigital to the digital. Together they address such issues as the waysthat different kinds of data and different domains of inquiry are mutually defining; how data arevariously “cooked” in the processes of their collection and use; and conflicts over whatcan — or can’t — be “reduced” to data. Contributors discuss the intellectual history ofdata as a concept; describe early financial modeling and some unusual sources for astronomical data;discover the prehistory of the database in newspaper clippings and index cards; and consider contemporary “dataveillance” of our online habits as well as the complexity of scientificdata curation”

http://www.amazon.com/Raw-Data-Is-Oxymoron-Infrastructures/dp/0262518287