Related papers: Stream Processing for Solar Physics: Applications …
Optoionics, a promising new field that aims at controlling ion dynamics using light, links photovoltaic power generation with electrochemical charge storage. This has the potential to drive and accelerate the energy revolution by utilizing…
Nowadays, every device connected to the Internet generates an ever-growing stream of data (formally, unbounded). Machine Learning on unbounded data streams is a grand challenge due to its resource constraints. In fact, standard machine…
Cardinality constrained submodular function maximization, which aims to select a subset of size at most $k$ to maximize a monotone submodular utility function, is the key in many data mining and machine learning applications such as data…
Computed color indices and spectral shapes for individual stars are routinely compared with observations for essentially all spectral types, but absolute fluxes are rarely tested. We can confront observed irradiances with the predictions…
Science is becoming very data intensive1. Today's astronomy datasets with tens of millions of galaxies already present substantial challenges for data mining. In less than 10 years the catalogs are expected to grow to billions of objects,…
As new technologies move to the fore, our understanding of the world may seem to have shrunk in comparison, for despite new developments in research, much of it is reduced or rather, abstracted for marketability. Thus, the purpose of this…
Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large…
We review some aspects of the current state of data-intensive astronomy, its methods, and some outstanding data analysis challenges. Astronomy is at the forefront of "big data" science, with exponentially growing data volumes and data…
Process Mining is a branch of Data Science that aims to extract process-related information from event data contained in information systems, that is steadily increasing in amount. Many algorithms, and a general-purpose open source…
The sunspot solar cycle has been usually explained as the result of a dynamo process operating in the sun. This is a classical problem in Astrophysics that until the present is not fully solved. Here we discuss current problems and…
Observational astronomy has changed drastically in the last decade: manually driven target-by-target instruments have been replaced by fully automated robotic telescopes. Data acquisition methods have advanced to the point that terabytes of…
Graph clustering becomes an important problem due to emerging applications involving the web, social networks and bio-informatics. Recently, many such applications generate data in the form of streams. Clustering massive, dynamic graph…
The anomalously slow rotation of the solar core is just one from a remarkable lists of fundamental indications showing that the solar core is somehow coupled to the surface activity cycle. On the other hand, the atmospheric, LSND and solar…
Pulsar data analysis pipelines have historically been comprised of bespoke software systems, supporting the off-line analysis of data. However modern data acquisition systems are making off-line analyses impractical. They often output…
Streamers are the first stage of sparks and lightning; they grow due to a strongly enhanced electric field at their tips; this field is created by a thin curved space charge layer. These multiple scales are already challenging when the…
The operation of the solar dynamo, with all of its remarkable spatio-temporal ordering, remains an outstanding problem of modern solar physics. A number of mechanisms that might plausibly contribute to its operation have been proposed, but…
Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, with a bounded computation time. The application of…
Large scale monitoring systems, enabled by the emergence of networked embedded sensing devices, offer the opportunity of fine grained online spatio-temporal collection, communication and analysis of physical parameters. Various applications…
Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…
As a free, intensive, weakly interacting, and well directional messenger, solar neutrinos have been driving both solar physics and neutrino physics developments for more than half a century. Since more extensive and advanced neutrino…