Related papers: Big Data: Overview
This article is about how the "SP theory of intelligence" and its realisation in the "SP machine" may, with advantage, be applied to the management and analysis of big data. The SP system -- introduced in the article and fully described…
Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the…
Data clustering is the process of identifying natural groupings or clusters within multidimensional data based on some similarity measure. Clustering is a fundamental process in many different disciplines. Hence, researchers from different…
In recent years, we have witnessed a dramatic data explosion in genomics, thanks to the improvement in sequencing technologies and the drastically decreasing costs. We are entering the era of millions of available genomes. Notably, each…
The great prosperity of big data systems such as Hadoop in recent years makes the benchmarking of these systems become crucial for both research and industry communities. The complexity, diversity, and rapid evolution of big data systems…
Geospatial big data plays a major role in the era of big data, as most data today are inherently spatial, collected with ubiquitous location-aware sensors. Efficiently collecting, managing, storing, and analyzing geospatial data streams…
Big data is gaining overwhelming attention since the last decade. Almost all the fields of science and technology have experienced a considerable impact from it. The cloud computing paradigm has been targeted for big data processing and…
An emerging internet based super computing model is represented by cloud computing. Cloud computing is the convergence and evolution of several concepts from virtualization, distributed storage, grid, and automation management to enable a…
Industrial big data is an important part of big data family, which has important application value for industrial production scheduling, risk perception, state identification, safety monitoring and quality control, etc. Due to the…
Big Data involves both a large number of events but also many variables. This paper will concentrate on the challenge presented by the large number of variables in a Big Dataset. It will start with a brief review of exploratory data…
Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges in big data analytics. Meeker and Hong (2014, Quality Engineering, pp. 102-116) provided an extensive…
Workflow is a common term used to describe a systematic breakdown of tasks that need to be performed to solve a problem. This concept has found best use in scientific and business applications for streamlining and improving the performance…
Recent advances in big/foundation models reveal a promising path for deep learning, where the roadmap steadily moves from big data to big models to (the newly-introduced) big learning. Specifically, the big learning exhaustively exploits…
The last 30 years have seen the creation of a variety of electronic collaboration tools for science and business. Some of the best-known collaboration tools support text editing (e.g., wikis). Wikipedia's success shows that large-scale…
"Big data" has become a major area of research and associated funding, as well as a focus of utopian thinking. In the still growing research community, one of the favourite optimistic analogies for data processing is that of the oil…
Data science is not a science. It is a research paradigm. Its power, scope, and scale will surpass science, our most powerful research paradigm, to enable knowledge discovery and change our world. We have yet to understand and define it,…
For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined 'Big Data', massive amounts of information has quite often been gathered…
While Large Language Models require more and more data to train and scale, rather than looking for any data to acquire, we should consider what types of tasks are more likely to benefit from data scaling. We should be intentional in our…
Efficient handling of large data-volumes becomes a necessity in today's world. It is driven by the desire to get more insight from the data and to gain a better understanding of user trends which can be transformed into economic incentives…
Analytics corresponds to a relevant and challenging phase of Big Data. The generation of knowledge from extensive data sets (petabyte era) of varying types, occurring at a speed able to serve decision makers, is practiced using multiple…