Visualizing Big Data From a Philosophical Perspective

Visualizing Big Data From a Philosophical Perspective

DOI: 10.4018/978-1-5225-3142-5.ch028
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Big Data-wow! Look at the size of it! People are dazzled easily and impressed by the amount of data legions of developers can produce and moreover how they store it. But one should ask why so much data is being collected. Part of the answer is simple in light of scientific projects but why is there so much data on us? Such questions prompt this chapter on the philosophy of Big Data. After some background on definitions, origins and contemporary applications, the main discussion begins with thinking about Big Data from a complexity standpoint. Big Data is turned into knowledge but knowledge is extrapolated from the past and used to manage the future. Yet it is questionable whether humans have the capacity to manage contemporary technological and social complexity evidenced by our world in crisis and possibly on the brink of extinction. We are at the center of observation from which Big Data emerges, the overall human project being not only to create an artificial brain with an attendant mind but a society that might be able to survive what “natural” humans cannot.
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It is likely that each one of us has encountered some aspect of Big Data, it often being a buzz word. Whenever we interact with a government agency, go to school, shop or use a computer, data is collected with or without our consent. The trail from the origins of data as representations of information ages ago to its present uses is long. While we marvel at the technology, it is critical to understand why the technology was created in the first place.

You are reading Big Data Storage and Visualization Techniques but with “Visualizing Big Data from a Philosophical Perspective”, most of the discussion is not simply of techniques but about the thinking underscoring why Big Data should be generated at all. As to the Visualization, there are just as many ways of displaying data as there are projects and methods for creating it as will be seen below. One can type in “Big Data Visualization” and similar phrases on the Internet and literally millions of entries will appear.

This chapter describes Big Data, what is collected and how and the limits of collecting large quantities of data. If something can be measured, data can be collected be it the length of every spider's leg, width of every tree or weight of every grain of sand. There is no dearth of facts but without organization and context there is little or no meaning. An overarching consideration is whether it should be collected at all.

Contemporary events may seem to be enveloping us, the proverbial tail wagging the dog. One need to point only to global warming, exploding populations, mounting political and religious violence, and economic failures. There are luminaries who doubt the capacity of this species even to survive the next 100 years (Firth, 2015; Rees, 2008; Sheldrick, 2014; Carey, 2014). We read of events daily often noting the facts but failing to apprehend the context. Reactions to technology often are like this where people learn of the cleverness being developed, the “Gee Whiz” and glitz of the gadgets. Big Data is no exception as in the wide eyed reaction to being told of huge data storage capacities. Older generations accustomed to paper based card catalog systems in libraries especially are taken aback in learning the Big Data of the whole of the Library of Congress in the very near future (and even now with 20 ten terabyte (TB) hard drives) can be sitting on their desk at home or on their laptop (Hard Drive Capacity, 2015).

Facts are meaningless in themselves and they need to be connected to each other. Even if a person relates the facts to each other still there is no final coherence unless there is a framework for that coherence, i.e., a theme. There are two basic ways of remembering a situation: rote and as a series of connected events or motif. Describing the beautiful setting of the Grand Canyon including the ecology, geological history and so forth is further impressed upon us by introducing something closer and to which we can translate as a relevant experience the way someone like ourselves interacted with that environment as a story. So, one tells of a story of someone in the Grand Canyon invoking the description of all the things the narrator would like for the audience to remember - the environment, history and so forth. This is the way knowledge was passed down throughout prehistoric times by tales, proverbs, myths and other oral communications (Eliade, 1954). Here, a story is told about the development of Big Data, that story being analogous to a parable conveying meaning of the assemblage of descriptions about that Big Data.

Key Terms in this Chapter

Second Order Cybernetics: The introduction of the observer or creator of a situation as a part of what is being observed.

Dyadic Binary Formula: A composition of terms in a two-variable or base-2, system with a set of operators according to rules establishing how to compose an expression. Examples: p ? q, Rxy & Pyx, A + B (where “A” and “B” are the only two variables); 00111 * 10010.

Reductionism (Cartesianism): Subdivision of a whole into parts normally meaning that the whole can be described by the sum of those parts.

Broad Agency Announcement (BAA): A BAA is a solicitation to the general public for one of its members or organizations to provide under contract, in the words of the U.S. Code of Federal Regulations, 48 CFR 35.016(a), “scientific study and experimentation directed toward advancing the state-of-the-art or increasing knowledge or understanding.”

Emergence: Unexpected phenomena appearing (and often having a regularity or pattern) from a collection of apparently unrelated elements and where the elements themselves do not have the characteristics of the phenomena and that phenomena itself is not contained deductively within the elements (See “Deduction”).

Quantum Computing: A Computer Architecture relying on the physics of Quantum Mechanics where the units or qubits of transmitting data may be in a multiple set of states simultaneously and where the transmission of data requires the “collapse” of one or more the qubits to a definite state.

Complexity Theory: Thinking underscoring the identification of simplicity or difficulty required in understanding the construction, analysis or resolution of a problem particularly related to systems.

Visualization Technique: Simply and broadly stated, a method of pictorializing or generating images of a symbolized: such as verbal or numerical description.

Induction: In an argument, the Conclusions follow only probabilistically from the premises. The scope of the Conclusions are larger than the set of premises individually or collectively.

Cloud Storage: A repository of data on a remote storage medium often distributed through multiple servers accessible through the Internet and owned and managed by a provider.

Recursion: In its simplest sense: repeatability. For example, when the outputs or results of a formula are fed back into the formula as inputs, the outputs will reappear. In the computer world, an overall solution requires the solution of its smaller constituents. One statement can generate any number of objects, meaning a specific program can describe limitless computations. A computer program reflects Recursion by having a function call itself inside a program.

Cellular Automaton: A grid of any number of dimensions composed of regularly-sized spaces or cells each of which may be in an active or inactive state, the whole assemblage of which assumes distinct conditions or goal states and through each unit of time each cell's condition being dependent upon the condition of one or more other cells.

Deduction: In an argument, if the premises are found to be true, the conclusion follows with certainty; the Conclusion is found entirely within the set of premises; a closed system.

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