[…] we argue that database technology should and can be adapted to provide the needed capabilities to support user interaction, user communities, and the social dynamics that arise from them : l)  Database technology should be used to support user interaction because databases tend to have communities of users (i.e., not a single or small group), so they are a perfect environment to enable social interaction . Furthermore, there are several ways in which database systems can benefit from user-created content: it can help interpret the data in the database, enrich it, and fill in any gaps in (very needed, but hardly present) metadata. Also, by allowing users to store their own data in the database, we make them more likely to explore the data and, in general, use the database for their tasks; 2)  Database technology can be used to support user interaction because the relational data model can be seen as a general platform on top of which flexible schemas can be developed so that almost arbitrary content can be captured and stored (from Antonio Badia, Social Interaction in Databases, in “Community-Built Databases”, p. 160, Springer)

“Post-demographics? Leading research into social networking sites considers such issues as presenting oneself and managing one’s status online, the different ‘social classes’ of users of MySpace and Facebook and the relationship between real-life friends and ‘friended’ friends (Boyd & Ellison, 2007). Another set of work, often from software-making arenas, concerns how to make use of the copious amounts of data contained in online profiles, especially interests and tastes. I would like to dub this latter work ‘postdemographics’. Post-demographics could be thought of as the study of the data in social networking platforms, and, in particular, how profiling is, or may be, performed. Of particular interest here are the potential outcomes of building tools on top of profiling platforms, including two described below. What kinds of findings may be made from mashing up the data, or what may be termed meta-profiling? [from Rogers, “Post-demographics Machines”]

A chapter on “Social Media and Post-demographics Machines” in [Rogers, Digital Methods, The MIT Press, 2013)

 

Click to access WalledGarden_ch04_RR.pdf

“At first glance, it may seem that we can ignore the history of a tenth of a second. After all, most events occurring within this short period of time cannot be perceived. Most persons take more than a tenth of a second to react. But in looking more carefully at this moment, it appears strangely constitutive of modernity. The tenth of a second was repeatedly referenced in debates about the nature of time, causality, free will, and the difference between humans and nonhumans. Understanding this short, “invisible” period of time is as important as understanding other equally small and invisible things. When in the seventeenth century Robert Hooke used a newly invented microscope to reveal the shocking wealth of the micro-world, he claimed that the “shadow of things” no longer needed to be taken for their “substance.” Microscopy led him away from “uncertainty,” “mistakes,” “dogmatizing,” and forms of knowledge based largely on “discourse and disputation.” This new technology appeared to him as important as a series of other revolutionary inventions. Hooke listed it among gun powder, the seaman’s compass, printing, etching, and engraving, which together saved man from misguided attempts to advance on knowledge through wasteful “talking,” “arguing,” and “opining.” (from “A tenth of a second. An history”, Jimena Canales, University of Chicago Press)

“[…]Data, reduced to their purest form of mathematical values, exist first and fo remost as number, and, as number, data’s primary mode o f existence is not a visual one. Thus to say “no necessary” means that any visualization of data requires a contingent leap from the mode of the mathematical to the mode of the visual. This does not mean that aestheticization cannot be achieved. And it does not mean that such acts of aestheticization are unmotivated, nugatory, arbitrary, or otherwise unimportant. It simply means that any visualization of data must invent an artificial set of translation rules that convert abstract number to semiotic sign. Hence it is not too juvenile to point out that any data visualization is first and foremost a visualization of the conversion rules themselves, and only secondarily a visualization of the raw data. Visualization wears its own artifice on its sleeve. And because of this, any data visualization will be first and foremost a theater for the logic of necessity that has been superimposed on the vast sea of contingent relations. So with the word “form ” already present in the predicate of the first thesis, and if the reader will allow a sloppy syllogism, it is possible to re jigger the first thesis so that both data and information may be united in something of an algebraic relationship . Hence now it goes, data have no necessary information” (A.R. Galloway, The Interface Effect. Polity, 2013, 83)

“[…]Digital technologies that render the world legible in new ways — by creating new representational forms that account for action in the world (such as activity-recognition systems) or making the invisible visible (such as many location-based systems, social-networking systems, or data-mining applications) — help to shape this encounter and are themselves shaped by it. The social origin of legibility is a critical issue for collaboration in mobile and ubiquitous environments. The examples that we have presented argue for a different view of information and information use than pervades conventional engineering discourse. They argue that the elements of the everyday world around which ubicomp applications seek to organize themselves — individuals, roles, groups, places, activities, times, contexts, and so forth — are not elements of the physical world to be uncovered and recognized but are instead elements of the social world. Their informativeness derives from the nature of social participation, and their nature and meaning are negotiated in, expressed through, and solely available to social practice. When we think of sensing technologies as devices that order the world, rather than devices that describe it, then alternative relationships between the social and technical are strikingly brought to light” (Dourish and Bell, “Divining a Digital Future. Mess and Mythology in Ubiquitous Computing”, The MIT Press, p.195)

“Today almost every aspect of life for which data exists can be rendered as a network. Financial data, social networks, biological ecologies: all are visualized in links and nodes, lines connecting dots. A network visualization of a corporate infrastructure could look remarkably similar to that of a terrorist organization. In An Aesthesia of Networks, Anna Munster argues that this uniformity has flattened our experience of networks as active and relational processes and assemblages. She counters the “network anaesthesia” that results from this pervasive mimesis by reinserting the question of experience, or aesthesia, into networked culture and aesthetics. Rather than asking how humans experience computers and networks, Munster asks how networks experience—what operations they perform and undergo to change and produce new forms of experience” (from the abstract: Anna Munster, “An Aesthesia of Networks. Conjunctive Experience in Art and Technology”, The MIT Press, 2013)

http://mitpress.mit.edu/books/aesthesia-networks

“What exactly is Big Data? At first glance, the term seems rather vague referring to something that is large and full of information. That description does indeed fit the bill, yet it provides no information on what Big Data really is. Big Data is often described as extremely large data sets that have grown beyond the ability to manage and analyze them with traditional data processing tools. Searching the Web for clues reveals an almost universal definition, shared by the majority of those promoting the ideology of Big Data, that can be condensed into something like this: Big Data defines a situation in which data sets have grown to such enormous sizes that conventional information technologies can no longer effectively handle either the size of the data set or the scale and growth of the data set. In other words, the data set has grown so large that it is difficult to manage and even harder to garner value out of it. The primary difficulties are the acquisition, storage, searching, sharing, analytics, and visualization of data.There is much more to be said about what Big Data actually is. The concept has evolved to include not only the size of the data set but also the processes involved in leveraging the data. Big Data has even become synonymous with other business concepts, such as business intelligence, analytics, and data mining. Paradoxically, Big Data is not that new. Although massive data sets have been created in just the last two years, Big Data has its roots in the scientific and medical communities, where the complex analysis of massive amounts of data has been done for drug development, physics modeling, and other forms of research, all of which involve large data sets. Yet it is these very roots of the concept that have changed what Big Data has come to be” (from: Frank J. Ohlhorst, Big data analytics : turning big data into big money, 2013)

“Language and the future: this is the subject of this book. Not the rhetorical future of history, politics, and so on, but the pragmatic future of the next second, of the next minute, of the next action and interaction. Future and interaction: this is the task (or the destiny) of code. So code is “ speaking ” us. Geoff Cox is trying to show the other side of the moon: if we can say that code is speaking us (pervading and formatting our action), the other way round is also true. We are speaking code in many ways. In the beginning someone is writing the code, and others are supposed to submit themselves to the effects of the code written by someone. Power is more and more inscribed in code. Writing to Thomas Sebeok, Bill Gates once remarked that “ power is making things easy ” (quoted by Arthur Kroker and Michael A. Weinstein in Data Trash , 1994). Code and interfaces: interfaces are supposed to make the complexity of the code easy, but code in itself is more often about simplifying technical procedures of social life, particularly of economic production and exchange. So code is speaking us, but we are not always working through the effects of written code. More and more we are escaping (or trying to escape) the automatisms implied in the written code (from the preface, Geoff Cox,”Speaking Code: Coding as Aesthetic and Political Expression”, The MIT Press, 2013)

“When a movement is motion-captured, Manning argues, digital technology breaks its virtual wholeness, dissecting its potential relational web and mapping it into bits of numerical data. In order for the mapping to work and for the capturing system to be able to read the motion, a specific (and very limited or simplified) kind of gesture is required, a linear, clearly traceable displacement replicating a sort of gestural conformity to the software, diminishing what a body can really do. For Manning, the unmappable virtuality of a movement’s preacceleration, or the virtual tendency of the body toward movement, toward relation, is what allows a displacement to take place and form. At the same time, relation is also what allows memory to be generated, for example, in rehearsal, during which it appears not as “a recurrence of the same,” but as “difference embodied through movement”: remembering the relation and its potential changes as recollecting the future. On the other hand, a visible, already actualized movement is all that is necessary for software detection, usually a displacement of a limb or of the whole body across space; the virtual double or twin of the gesture is left out of the picture” (Stamatia Portanova, Moving without a body : digital philosophy and choreographic thoughts: The MIT Press, 2013)