Big Data: From Forecasting to Mesoscopic Understanding. Meta-Profiling as Complex Systems

Abstract

We consider Big Data as a phenomenon with acquired properties, similar to collective behaviours, that establishes virtual collective beings. We consider the occurrence of ongoing non-equivalent multiple properties in the conceptual framework of structural dynamics given by sequences of structures and not only by different values assumed by the same structure. We consider the difference between modelling and profiling in a constructivist way, as De Finetti intended probability to exist, depending on the configuration taken into consideration. The past has little or no influence, while events and their configurations are not memorised. Any configuration of events is new, and the probabilistic values to be considered are reset. As for collective behaviours, we introduce methodological and conceptual proposals using mesoscopic variables and their property profiles and meta-profile Big Data and non-computable profiles which were inspired by the use of natural computing to deal with cyber-ecosystems. The focus is on ongoing profiles, in which the arising properties trace trajectories, rather than assuming that we can foresee them based on the past.We assume that dealing with Big Data as a phenomenon contains not only traces of past events, but also the assumption of having properties that are independent from the original event, when studied as a virtual collective being [6]. Thus, we consider the occurrence of ongoing, non-equivalent, multiple properties with any initial temporal beginning, duration, and variable combination in conceptual correspondence with the nature of quasi-systems, which occur when a system is not always the same system and or even a system at all [7,8].We then mention the criticality of the level of description, as well as the scalarity assumed to detect the properties as well as the conceptual ineffectiveness of increasing the quantity of data available. Before proposing new approaches allowed by the conceptual 'definitions' mentioned above, we consider the differences between models and profiles. Models (especially ideal) are expected to support understanding, while profiling is intended to represent non-ideal, data-driven models, and their emergent, ongoing properties, for example, correlations and coherences [9]. Another differentiation considered is between forecasting and understanding. Forecasting is based on the importance of the past, is based on analogies and repetitiveness, and takes some contextual conditions into account. Understanding is considered to be the ability to conjecture, hypothesise, speculate, and, finally, realise the nature of the phenomenon under study, for instance, chaotic, hosting bifurcations, fluctuations, and the presence of multiple dynamic coherences.We specify that the differences introduced above should be considered in a constructivist conceptual framework in which, instead of trying to find how a phenomenon 'really' is, one looks for the most effective way to think of it (multiple modelling) [10]. For instance, we consider the conceptual framework of the t...

On Theoretical Incomprehensibility

“…The central point is that it is possible to increase the number of representations and disciplines used, but the resulting multiple and dynamic representation is not to be theoretically considered as exhaustive or complete. This is comparable to the misunderstanding that the increase in the data and information available involves an increment of knowledge and bearing to the paradox of Big Data [72]. The data are not convergent to the explanation and the knowledge (the poor idea was to make knowledge in production by quantitative increases of data).…”

“…It may mean not yet complete, be chronically incomplete and, in theory, not completable with a single approach or model, as in the case of complexity for which multiple non-equivalent and variable models are required for the processes of emergence, as considered by the DYnamic uSAge of Models (DYSAM) (see [8], pp. [64][65][66][67][68][69][70][71][72][73][74][75].…”

A Note on the Reality of Incomputable Real Numbers and Its Systemic Significance

“…302-305; [24], pp. 107, 165) and dynamics of the multiplicity of emergence [25], processes of decision-making from information and timings, Big Data [26], and quantum phenomena. Multiple systems are established by considering the multiple roles of their interacting components and their interchangeability, such as in in ergodic behavior.…”

“…A typical example of a field of application for such approaches is that of Big Data, where, for example, the properties of infraclusters can be considered [26,68].…”

Nonclassical Systemics of Quasicoherence: From Formal Properties to Representations of Generative Mechanisms. A Conceptual Introduction to a Paradigm-Shift

“…Finally, we mention the expected generalizability of the concepts introduced above in dealing with domains of configurations of interrelated constraints when moving from the physical space to virtual spaces such as in architecture, finance, medicine, and styles, e.g., in design and music [74]. It can also be a matter of studying how domains may represent the "essential" features of Big Data [75].…”