Big Data and Theory Interplay — Big Data has outpaced concepts. A challenge to better leverage Big Data and AI!
Big Data is growing unprecedentedly so rapidly that is now bypassing concepts and we have to catch up to have better insights. As large data sets are growing unprecedentedly massively, thus the name “Big Data”, development and elaboration of mathematical conceptual/theoretical frameworks will be required to catch up to turn these large data sets into actionable insights.
“The trouble is, we don’t have a unified, conceptual framework for addressing questions of data complexity…Big data without a “big theory” to go with it loses much of its usefulness, potentially generating new unintended consequences.” Geoffrey West (2013)
“The data once perceived as “noise” can now be re-considered with the rest of data, leading to new ways to develop theories and ontologies” David Bollier (2010)
To fill in the gap between big data and concepts, more and more essays and contributions are expected to emerge to address the theoretical/conceptual aspects of Big Data to catch up with its rapid growth. An essay to contribute to the “big theory” by Tom Boellstorff , titled “Making big data in theory” appeared in the “First Monday” electronic journal, a peer–reviewed journal that is solely devoted to the Internet. The author argues in the essay that:
Like any myth, the current hullabaloo regarding big data is overblown but contains grains of truth.
Presence of patterns (“grains of truth”) in big data is an indication of possibility to carry out predictions, which in turn can help in taking appropriate actions. Attempts to set up conceptual frameworks to analyze large data sets and capture patterns in data ranging from climate data to omics data (e.g. genomics data), involving complex networks (Genetic Regulatory Networks) of genes, just appeared in a new book titled “Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits”.
Genetic resources are genetic material of plants, animals or micro — organisms of value as a resource for future generations of humanity. OECD (2001)
This new book is the result of the efforts of great many people including practitioners and farmers’ networks from many countries (across all the 5 continents) working on different disciplines. Some chapters of the book refer to the conceptual frameworks to address large and diverse data types (including people’s knowledge/insights) for their integration and pertinent analysis using among others machine learning techniques such as neural networks, also known as artificial neural networks (ANN/AI).
This new book, which has been highly ranked and positively reviewed has been published recently by Taylor and Francis Group.
Hope you will find it as useful to dig into big data challenges!
In any particular theory there is only as much real science as there is mathematics. Immanuel Kant
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