Related papers: Geospatial Big Data Handling with High Performance…
New challenges in Astronomy and Astrophysics (AA) are urging the need for a large number of exceptionally computationally intensive simulations. "Exascale" (and beyond) computational facilities are mandatory to address the size of…
Several scientific and industry applications require High Performance Computing (HPC) resources to process and/or simulate complex models. Not long ago, companies, research institutes, and universities used to acquire and maintain…
As particle physics experiments push their limits on both the energy and the intensity frontiers, the amount and complexity of the produced data are also expected to increase accordingly. With such large data volumes, next-generation…
Many datasets nowadays contain links between geographic locations and natural language texts. These links can be geotags, such as geotagged tweets or geotagged Wikipedia pages, in which location coordinates are explicitly attached to texts.…
In this work, system monitoring and analysis are discussed in terms of their significance and benefits for operations and research in the field of high-performance computing (HPC). HPC systems deliver unique insights to computational…
Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources,…
Spatiotemporal data are being produced in continuously growing volumes by a variety of data sources and a variety of application fields rely on rapid analysis of such data. Existing systems such as PostGIS or MobilityDB usually build on…
Spatial data science has emerged in recent years as an interdisciplinary field. This position paper discusses the importance of building and sharing high-quality datasets for spatial data science.
We describe the design and implementation of a high performance cloud that we have used to archive, analyze and mine large distributed data sets. By a cloud, we mean an infrastructure that provides resources and/or services over the…
Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of…
Gaussian processes (GPs) are widely used in nonparametric regression, classification and spatio-temporal modeling, motivated in part by a rich literature on theoretical properties. However, a well known drawback of GPs that limits their use…
The experience of the COVID-19 pandemic, which has accelerated many chaotic processes in modern society, has highlighted in a very serious and urgent way the need to understand complex processes in order to achieve the common well-being.…
This report focuses on spatial data intelligent large models, delving into the principles, methods, and cutting-edge applications of these models. It provides an in-depth discussion on the definition, development history, current status,…
The ability to understand how a scientific application is executed on a large HPC system is of great importance in allocating resources within the HPC data center. In this paper, we describe how we used system performance data to identify:…
With the upcoming generation of telescopes, cluster scale strong gravitational lenses will act as an increasingly relevant probe of cosmology and dark matter. The better resolved data produced by current and future facilities requires…
In the last decade, following the emergence of the mobile applications domain, the significance of location information has changed radically. Nowadays, location data not only is a key component of geospatial databases, but also a critical…
Power consumption will be a key constraint on the future growth of Distributed High Throughput Computing (DHTC) as used by High Energy Physics (HEP). This makes performance-per-watt a crucial metric for selecting cost-efficient computing…
Geocoding systems are widely used in both scientific research for spatial analysis and everyday life through location-based services. The quality of geocoded data significantly impacts subsequent processes and applications, underscoring the…
The evolution of the Internet and computer applications have generated colossal amount of data. They are referred to as Big Data and they consist of huge volume, high velocity, and variable datasets that need to be managed at the right…
The rapid growth of spatiotemporal data volumes needs to be handled by database systems capable of efficiently managing and querying such data. Existing systems such as PostGIS, SpaceTime, and MobilityDB offer partial solutions but differ…