Customer Use Cases

Source: Deep Learning on Medium

There different approaches and attempts to decentralize or distribute various aspects of computing. In most cases, it is tried to distribute some customer use cases, whereas the underlying algorithms are so well known that an individual result validation can be developed. However, if a distributed computing network only focuses on supported use cases or requires customers to also supply a validation method, the usability is heavily limited for customers.

HiveNet on the contrary focuses on mass adoption with flexible use cases. Customers will be able to request the computation of a wide set of tasks, which they can define, without the need of additionally supplying validation methods. This is made possible, because HiveNet integrates artificial intelligence to distribute and validate these tasks even if the use case is not pre-defined. Thereby, HiveNet will facilitate a lot of flexibility for customers to apply their individual use cases without too much worrying.

Whereas customers of respective cloud computing services know their use cases very well, for many people it remains obscure what use cases actually need all the computing power.

In the following we give just a few examples of typical customer use cases to provide you a glimpse of what customers are actually doing with all the computing power. If you want to learn more about the individual examples, please follow the reference links in the respective headlines.

Deep learning is an example of machine learning, which is based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

Numerical weather prediction uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs. Mathematical models based on the same physical principles can be used to generate either short-term weather forecasts or longer-term climate predictions. The latter are widely applied for understanding and projecting climate change. The improvements made to regional models have allowed for significant improvements in tropical cyclone track and air quality forecasts.

Protein structure prediction refers to an algorithmic process by which protein tertiary structure is predicted from its amino acid sequence. So far, this method is not finally solved, although intensive research was employed in this field. However, some of the most successful methods have a reasonable probability of predicting the folds of small, single-domain proteins. If no previous knowledge about a protein’s structure is available, this method tends to require vast computational resources. Therefore, it is only carried out for relatively small proteins. Prediction of larger protein structures will require better algorithms and larger computational resources such as those afforded by either powerful supercomputers or distributed computing projects. Although computational barriers are big, the potential benefits of clarified protein structures to fields such as medicine and drug design, clearly outweigh the effort.

Computational fluid dynamics uses numerical analysis and data structures to analyze and solve problems that involve fluid flows. Computers are used to perform the calculations required to simulate the free-stream flow of the fluid, and the interaction of the fluid (liquids and gases) with surfaces defined by boundary conditions. With high-speed supercomputers, better solutions can be achieved, and are often required to solve the largest and most complex problems. Ongoing research yields software that improves the accuracy and speed of complex simulation scenarios such as transonic or turbulent flows. Computational fluid dynamics is applied to a wide range of research and engineering problems in many fields of study and industries, including aerodynamics and aerospace analysis, weather simulation, natural science and environmental engineering, industrial system design and analysis, biological engineering and fluid flows, and engine and combustion analysis.

Seismic tomography is a technique for imaging the subsurface of the Earth with seismic waves produced by earthquakes or explosions. Plane-, shear-, and surface waves can be used for tomographic models based on seismic wavelength, wave source distance, and the seismograph array coverage. The data received at seismometers are used to solve an inverse problem, wherein the locations of reflection and refraction of the wave paths are determined. This solution can be used to create 3D images of velocity anomalies which may be interpreted as structural, thermal, or compositional variations. Geoscientists use these images to better understand core, mantle, and plate tectonic processes. Computing power limits the amount of seismic data, number of unknowns, mesh size, and iterations in tomographic models.

Computational biology represents a wide range of various use cases. It involves the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, ecological, behavioral, and social systems. The field is broadly defined and includes foundations in biology, applied mathematics, statistics, biochemistry, chemistry, biophysics, molecular biology, genetics, genomics, computer science and evolution.

Density functional theory is a computational quantum mechanical modelling method used in physics, chemistry and materials science to investigate the electronic structure (or nuclear structure) of many-body systems, in particular atoms, molecules, and the condensed phases. Using this theory, the properties of a many-electron system can be determined by using functionals (functions of another function), which in this case is the spatially dependent electron density. This method is among the most popular and versatile ones available in condensed-matter physics, computational physics, and computational chemistry. The required computing work is increasing exponentially with the complexity of the analyzed system and is driving the need for more and more computing power. Even though the calculations can be large and expensive, they are a lot faster and cheaper than the set-up of actual tests and analyses. In general, this method finds increasingly broad application in chemistry and materials science for the interpretation and prediction of complex systems at an atomic scale.

Computer-generated imagery (CGI) is the application of computer graphics to create or contribute to images in art, printed media, video games, films, television programs, shorts, commercials, videos, and simulators. The visual scenes may be dynamic or static and may be two-dimensional (2D), though the term “CGI” is most commonly used to refer to 3D computer graphics used for creating scenes or special effects in films and television. Computer graphics software is used to make computer-generated imagery for films, etc.

Computational finance deals with problems of practical interest in finance. Computational finance emphasizes practical numerical methods rather than mathematical proofs and focuses on techniques that apply directly to economic analyses. Finance theory is heavily based on financial instrument pricing such as stock option pricing. Many of the problems facing the finance community have no known analytical solution. As a result, numerical methods and computer simulations for solving these problems have proliferated. Many computational finance problems have a high degree of computational complexity. Additional complexity results from the need to respond to quickly changing markets. For example, in order to take advantage of inaccurately priced stock options, the computation must complete before the next change in the almost continuously changing stock market. This has led to research that applies alternative computing techniques to finance.

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