Author: Cosimo Accoto

“Again, these pessimistic conclusions concern liquid markets with a large number of very simple and visible transactions. In other information markets this effect is far less relevant. In fact, even financial information markets have seen a boom in the last decades thanks to all the market  inefficiencies and the “noise” produced by random traders with random goals and objectives. In sum, consistent with the reality in which information markets flourish, the rest of our discussion assumes the proper functioning of information markets, that is, where ownership can be protected by copyright laws and private information does not get revealed in people’s actions. While information sellers need to be aware of their market’s potential vulnerability, these market conditions do apply to the vast majority of information products”.

http://mitpress2.mit.edu/books/chapters/026201694Xchap2.pdf

“To understand the rise of marketing and market research, we need to understand what these two overlapping fields are about. We must understand what problems they tried to grapple with and what their goals and methods were. Put into simple language, marketing deals with three broad complexes: information, institutions, and markets. First, it is important to stress that there is no such thing as objective information in marketing. Such information is inherently ambiguous. Its production and interpretation are deeply value-laden and influenced by the gatherer’s intentions. It directly relates to the reduction of complexity in Luhmann’s sense, as the number of categories for consumers and products has to be small, and these categories tend to ignore finer differentiations. Market research does not just rely on developing consumer profiles, although these are certainly necessary for gaining a rough understanding of the marketplace and making informed investment decisions. Market research also actively fashions and transforms consumers and markets”

http://www.palgrave.com/PDFs/9780230341067_sample.pdf

“Fueled by big data collected by a wide range of high-throughput tools and technologies, a new wave of data-driven, interdisciplinary science have rapidly proliferated during the past decade, impacting a wide array of disciplines, from physics and computer science to cell biology and economics. In particular, the ICT’s are inundating us with huge amounts of information about human activities, oering access to observing and measuring human behavior at an unprecedented level of details. These large-scale datasets, oering objective description on human activity pat- terns, have started to reshape, and are expected to fundamentally alter, our discussions on quantifying and understanding human behavior. An impressive shift has been witnessed in statistical physics and complex system theory since the beginning of the new millennium, when the possibility of analyzing large datasets of human activities and social interactions has boosted a renewed interest in the study of human mobility on one side, and of social networks on the other side”

http://www.dashunwang.com/pdf/2012-mobilityBook.pdf

“In database research, the big data issue has mainly been addressed as a scale issue: storing and providing the same level of services as before in terms of speed, reliability, interoperability and distribution.The scale challenges are now being solved by research and industry. A less advertised issue raised by the increase of available data is the unprecedented opportunity for discoveries and exploratory studies. For example, Metagenomics is a research ???eld in Biology that sequences DNA from random samples of material collected in the wild to discover the largest possible diversity of living species. With new high-throughput sequencing technologies, the genome of millions of species are sequenced and stored in databases but are not systematically explored due to the lack of appropriate tools. Bank transactions are being logged and monitored to ???nd patterns of frauds, but new schemes arise continually that need to be discovered in the billions of transactions logged daily. This task of discovery of innovative fraud schemes is not well supported by existing tools either. Similar problems arise in a wide range of domains where data is available but requires speci???c exploration tools, including sentiment analysis on the Web, quality control in Wikipedia, and epidemiology to name a few. However, current database technologies do not meet the requirements needed to interactively explore massive or even reasonably-sized data sets”

http://sites.computer.org/debull/A12sept/visual.pdf

“Big data biology—bioinformatics, computational biology, systems biology (including ‘omics’), and synthetic biology—raises a number of issues for the philosophy of science. This article deals with several such: Is data-intensive biology a new kind of science, presumably post-reductionistic? To what extent is big data biology data-driven? Can data ‘speak for themselves?’ I discuss these issues by way of a reflection on Carl Woese’s worry that “a society that permits biology to become an engineering discipline, that allows that science to slip into the role of changing the living world without trying to understand it, is a danger to itself.” And I argue that scientific perspectivism, a philosophical stance represented prominently by Giere, Van Fraassen, and Wimsatt, according to which science cannot as a matter of principle transcend our human perspective, provides the best resources currently at our disposal to tackle many of the philosophical issues implied in the modeling of complex, multilevel/multiscale phenomena”

“Even by the journal’s own standards, this was a wild claim. In July 2008, Wired magazine announced on its cover nothing less than “The End of Science”. It explained that “The quest for knowledge used to begin with grand theories. Now it begins with massive amounts of data”.¹ Such claims about the emergence of a new “data-driven” science in response to a “data deluge” have now become common, from the pages of The Economist to those of Nature.² Proponents of “data-driven” and “hypothesis-driven” science argue over the best methods to turn massive amounts of data into knowledge. Instead of jumping into the fray, I would like to historicize some of the questions and problems raised by data-driven science, taking as a point of departure the three rich papers by Isabelle Charmantier and Staffan Müller-Wille on Linnaeus’ information processing strategies, Sabina Leonelli and Rachel Ankeny on model organisms databases, and Peter Keating and Alberto Cambrosio on microarray data in clinical research”

“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…