Related papers: The JASMIN super-data-cluster
Scientific collaborations require a strong computing infrastructure to successfully process and analyze data. While large-scale collaborations have access to resources such as Analysis Facilities, small-scale collaborations often lack the…
This paper takes an approach to clustering domestic electricity load profiles that has been successfully used with data from Portugal and applies it to UK data. Clustering techniques are applied and it is found that the preferred technique…
ATLAS, a general-purpose experiment at the Large Hadron Collider (LHC), makes use of a large internationally-distributed computing infrastructure, including over $10^6$ TB of managed data on disk and tape and almost one million…
JScanam is the default map-maker for Herschel/PACS photometer observations. Making use of the redundant information from multiple passages on the sky with different scanning directions, JScanam is able to remove the $1/f$ noise that…
Foundation Models (FMs) are large-scale, pre-trained artificial intelligence (AI) systems that have revolutionized natural language processing and computer vision, and are now advancing geospatial analysis and Earth Observation (EO). They…
Subspace clustering methods which embrace a self-expressive model that represents each data point as a linear combination of other data points in the dataset provide powerful unsupervised learning techniques. However, when dealing with…
A key part of planning CO2 storage sites is to devise a monitoring strategy. The aim of this strategy is to fulfill the requirements of legislations and lower cost of the operation by avoiding operational problems. If CCS is going to be a…
The start of data taking at the Large Hadron Collider will herald a new era in data volumes and distributed processing in particle physics. Data volumes of hundreds of Terabytes will be shipped to Tier-2 centres for analysis by the LHC…
The amount of remote sensing data available to applications is constantly growing due to the rise of very-high-resolution sensors and short repeat cycle satellites. Consequently, tackling computational complexity in Earth Observation…
Leadership computing facilities around the world support cutting-edge scientific research across a broad spectrum of disciplines including understanding climate change, combating opioid addiction, or simulating the decay of a neutron. While…
Today's astronomical projects need computational systems capable to store and analyze large amounts of scientific data, to effectively share data with other research Institutes and to easily implement information services to present data…
In January 1999, CERN began to develop CASTOR ("CERN Advanced STORage manager"). This Hierarchical Storage Manager targetted at HEP applications has been in full production at CERN since May 2001. It now contains more than two Petabyte of…
We present a distributed system for storage, processing, three-dimensional visualisation and basic analysis of data from Earth-observing satellites. The database and the server have been designed for high performance and scalability,…
Spatio-Temporal (ST) data science, which includes sensing, managing, and mining large-scale data across space and time, is fundamental to understanding complex systems in domains such as urban computing, climate science, and intelligent…
Clustering is one of the major tasks in data mining. In the last few years, Clustering of spatial data has received a lot of research attention. Spatial databases are components of many advanced information systems like geographic…
The report describes the digital architecture developed for the West Cambridge Digital Twin, particularly focussed on real-time sensor data collection and analysis with a privacy framework allowing occupants of the buildings to be…
The proliferation of latency-critical and compute-intensive edge applications is driving increases in computing demand and carbon emissions at the edge. To better understand carbon emissions at the edge, we analyze granular carbon intensity…
A novel concept of Joint Source and Channel Sensing (JSCS) is introduced in the context of Cognitive Radio Sensor Networks (CRSN). Every sensor node has two basic tasks: application-oriented source sensing and ambient-oriented channel…
Channel knowledge maps (CKMs) provide a site-specific, location-indexed knowledge base that supports environment-aware communications and sensing in 6G networks. In practical deployments, CKM observations are often noisy and irregular due…
Input data for applications that run in cloud computing centres can be stored at distant repositories, often with multiple copies of the popular data stored at many sites. Locating and retrieving the remote data can be challenging, and we…