“Why account for software ontologically?” (Mackenzie, Cutting Code. Software and Sociality)

[…] “In the third part, “The Violence of Participation,” Mark Andrejevic reports from the new frontiers of data mining. He makes the case that the commercial appropriation of information meets an abstract definition of exploitation. Andrejevic argues that it is indeed the sign of a certain kind of material luxury to be able to be exploited online—to have the leisure time and resources to engage in the activities that are monitored and tracked. Google tracks its 1 billion unremunerated users and sells their data to advertising clients, who consequently target users with ads. The intertwining of labor, leisure, consumption, production, and play complicates the understanding of exploitation, but Andrejevic remarked that the potential usefulness of an exploitation-based critique of online monitoring is that it invites us to reframe questions of individual choice and personal pleasure in terms of social relations. Andrejevic also discusses peer pressure and the obligation to network online, which is becoming institutionalized, and the fruits of this labor are recognized as a source of value. Commercial surveillance has become a crucial component of our communicative infrastructure, he observes. Exploitation, however, does not mean that workers don’t take pleasure in the success of a collaborative effort. There are moments of pleasure despite the fact that we are losing control of our productive and creative activities. While his critique of exploitation does not disparage the pleasures of workers, it also does not nullify exploitative social relations […] The violence of participation is about data mining on the one hand and the personal and professional price they would pay for their refusal of mainstream social media services on the other. Refusal would be tantamount to social isolation” (from the INTRODUCTION “Why Does Digital Labor Matter Now?” Trebor Scholz, Digital Labour: the Internet As Playground and Factory, Routledge, 2012)

http://mitpress.mit.edu/books/collaborative-media

” […] The development of IA, which is seemingly analogous to architecture as the design of the built environment, is not only a potentially new design field in which architects can work, but it is also a challenge to the profession. Reflecting on the notion of the virtual library for example, the architectural theorist, William J. Mitchell, reflects on the impact on architecture as it is traditionally conceived once digital edifices have replaced physical ones: The task facing the designers of [the] soft library is a transformation (with some invariants but many radical changes) of what faced the Smirke brothers and the librarian Panizzi as they evolved the design for the British Museum and Library. The façade is not to be constructed of stone and located on a street in Bloomsbury, but of pixels on thousands of screens scattered throughout the world. Organizing book stacks and providing access to them turns into a task of structuring databases and providing search and retrieval routines. Reading tables become display windows on screens …. The hugestacks shrink to almost negligible size, the seats and carrels disperse and there is nothing left to put a grand façade on (Mitchell 2000: 56–7). In Mitchell’s description, architecture has clearly been reconfi gured and perhaps replaced by a new IA. Although he places emphasis on the radical changes that are being brought about by digital technology, particularly in terms of the storage and retrieval of information, he notes that there are certain ‘invariant’ characteristics. From the very notion of an information architect, through to the building analogy used by Rosenfeld and Morville (2002), it is clear that traditional notions of information and of its representation in physical space provide a conceptual framework, linking information, its organization, its display and its navigation in new digital contexts. Where, if anywhere, is the architecture in information architecture?“ (The Architecture of Information. Architecture, interaction design and the patterning of digital information, Martyn Dade-Robertson, Routledge, p. 14)

“[…] After months of data mining on makes and models of cars and which orders correlate to each type of vehicle, the system reliably estimated what the short-order cooks should deliver as customers drove up. Bob extends the Internet landing page strategy to the parking lot. Even privacy advocates have trouble finding fault with Bob. The computer system is only recognizing a car and making a guess about what the car’s occupants will order. If the company does not sell that information and does not associate the purchaser’s identity with the car’s details, then the invasion of privacy can seem minor. The company can even expunge details about when the car visits, removing information that otherwise could have legal value in a court case, for instance, establishing the validity of an alibi. Recognizing cars well enough—perception—and making the right decisions about what to cook—cognition—were unthinkable at this level ten years ago. Today this is nearly standard practice” (by Illah Reza Nourbakhsh, Robot Futures, The MIT Press, 2013, p.11)

[…] This design has reduced cognitive load by assuming the form of a physical object. This is not just data visualization but also data formation. It is an interface you do not operate, and as a part of the scene it is ambient. Such developments in ambient interface may as yet be a sideshow in comparison to how disembodied information media blanket urban space with their screens, but it’s a start. As yet, the capacity to tag, to project, or even to inhabit one’s own contributions or one’s group’s curations of augmented urban space is at a very early stage. The challenge is to find the right contexts, scale, texture, timescale, and spatial resolution, and then, as this inquiry attempts, to combine insights on attention with insights on the history of the built environment. For all of this prospect, it seems wise to note that information can take form” (from McCullough, “Ambient Commons. Attention in the Age of Embodied Information, The MIT Press, 2013, p.88).

[from the introduction to “Principles of Big Data” (by Jules J. Berman, Elsevier, 2013, p.xxiii)

“Generally, Big Data come into existence through any of several different mechanisms.

1. An entity has collected a lot of data, in the course of its normal activities, and seeks to organize the data so that materials can be retrieved, as needed. The Big Data effort is intended to streamline the regular activities of the entity. In this case, the data is just waiting to be used. The entity is not looking to discover anything or to do anything new. It simply wants to use the data to do what it has always been doing—only better. The typical medical center is a good example of an “accidental” Big Data resource. The day-to-day activities of caring for patients and recording data into hospital information systems results in terabytes of collected data in forms such as laboratory reports, pharmacy orders, clinical encounters, and billing data. Most of this information is generated for a one-time specific use (e.g., supporting a clinical decision, collecting payment for a procedure). It occurs to the administrative staff that the collected data can be used, in its totality, to achieve mandated goals: improving quality of service, increasing staff efficiency, and reducing operational costs.

2. An entity has collected a lot of data in the course of its normal activities and decides that there are many new activities that could be supported by their data. Consider modern corporations—these entities do not restrict themselves to one manufacturing process or one target audience. They are constantly looking for new opportunities. Their collected data may enable them to develop new products based on the preferences of their loyal customers, to reach new markets, or to market and distribute items via the Web. These entities will become hybrid Big Data/manufacturing enterprises.

3. An entity plans a business model based on a Big Data resource. Unlike the previous entities, this entity starts with Big Data and adds a physical component secondarily. Amazon and FedEx may fall into this category, as they began with a plan for providing a data-intense service (e.g., the Amazon Web catalog and the FedEx package-tracking system). The traditional tasks of warehousing, inventory, pickup, and delivery had been available all along, but lacked the novelty and efficiency afforded by Big Data.

4. An entity is part of a group of entities that have large data resources, all of whom understand that it would be to their mutual advantage to federate their data resources. An example of a federated Big Data resource would be hospital databases that share electronic medical health records.

5. An entity with skills and vision develops a project wherein large amounts of data are collected and organized to the benefit of themselves and their user-clients. Google, and its many services, is an example (see Glossary items, Page rank, Object rank).

6. An entity has no data and has no particular expertise in Big Data technologies, but it has money and vision. The entity seeks to fund and coordinate a group of data creators and data holders who will build a Big Data resource that
can be used by others. Government agencies have been the major benefactors. These Big Data projects are justified if they lead to important discoveries that could not be attained at a lesser cost, with smaller data resources”

(source: http://www.sciencedirect.com/science/book/9780124045767

[abstract] Ubiquitous computing and our cultural life promise to become completely interwoven: technical currents feed into our screen culture of digital television, video, home computers, movies, and high-resolution advertising displays. Technology has become at once larger and smaller, mobile and ambient. InThroughout, leading writers on new media–including Jay David Bolter, Mark Hansen, N. Katherine Hayles, and Lev Manovich–take on the crucial challenges that ubiquitous and pervasive computing pose for cultural theory and criticism. The thirty-foure contributing researchers consider the visual sense and sensations of living with a ubicomp culture; electronic sounds from the uncanny to the unremarkable; the effects of ubicomp on communication, including mobility, transmateriality, and infinite availability; general trends and concrete specificities of interaction designs; the affectivity in ubicomp experiences, including performances; context awareness; and claims on the “real” in the use of such terms as “augmented reality” and “mixed reality (Ulrik Ekman, ed., Throughout: Art and Culture Emerging with Ubiquitous Computing, Cambridge, Mass.; MIT Press, 2012).

http://mitpress.mit.edu/books/throughout-0

“[…] In the early days of BI, running queries was only possible for IT experts. The tremendous increase in available computational power and main memory has allowed us to think about a totally different approach: the design of systems that empower business users to define and run queries on their own. This is sometimes called self-service BI. An important behavioral aspect is the shift from push to pull: people should get information whenever they want and on any device [142]. For example, a sales person can retrieve real-time data about a customer, including BI data, instantly on his smart phone. Another example could be the access of dunning functionality from a mobile device. This enables a salesperson to run a dunning report while on the road and to visit a customer with outstanding payments if she or he is in the area. These examples emphasize the importance of sub-second response time applications driven by in-memory database technology. The amount of data transferred to mobile devices and the computational requirements of the applications for the mobile devices have to be optimized, given limited processing power an connection bandwidths. As explained previously, . An exception was the need to consolidate complex, heterogeneous system landscapes. As a result of the technological developments in recent years, many technical problems have been solved. We propose that BI using operational data could be once again performed on the operational system. In-memory databases using column-oriented and row-oriented storage, allow both operational and analytical workloads to be processed at the same time in the same system” (“In-Memory Data Management, Plattner and Zeier, Springer, p.183).