Related papers: Data Provenance and Management in Radio Astronomy:…
A major challenge in modern radio astronomy is dealing with the massive data volumes generated by wide-bandwidth receivers. Such massive data rates are often too great for a single device to cope, and so processing must be split across…
Modern advances in space technology have enabled the capture and recording of unprecedented volumes of data. In the field of solar physics this is most readily apparent with the advent of the Solar Dynamics Observatory (SDO), which returns…
Data intensive applications often involve the analysis of large datasets that require large amounts of compute and storage resources. While dedicated compute and/or storage farms offer good task/data throughput, they suffer low resource…
During the last decade or so, we have had a deluge of data from not only science fields but also industry and commerce fields. Although the amount of data available to us is constantly increasing, our ability to process it becomes more and…
This paper presents a novel high speed clustering scheme for high dimensional data streams. Data stream clustering has gained importance in different applications, for example, in network monitoring, intrusion detection, and real-time…
In this decade astronomy is undergoing a paradigm shift to handle data from next generation observatories such as the Square Kilometre Array (SKA) or the Vera C. Rubin Observatory (LSST). Producing real time data streams of up to 10 TB/s…
As the landscape of deep neural networks evolves, heterogeneous dataflow accelerators, in the form of multi-core architectures or chiplet-based designs, promise more flexibility and higher inference performance through scalability. So far,…
The domain of radio astronomy is currently facing significant computational challenges, foremost amongst which are those posed by the development of the world's largest radio telescope, the Square Kilometre Array (SKA). Preliminary…
The latest generation of radio astronomy interferometers will conduct all sky surveys with data products consisting of petabytes of spectral line data. Traditional approaches to identifying and parameterising the astrophysical sources…
Data stream processing is an increasingly important topic due to the prevalence of smart devices and the demand for real-time analytics. Geo-distributed streaming systems, where cloud-based queries utilize data streams from multiple…
Astronomical photometry is the science of measuring the flux of a celestial object. Since its introduction, the CCD has been the principle method of measuring flux to calculate the apparent magnitude of an object. Each CCD image taken must…
Stream computing is the use of multiple autonomic and parallel modules together with integrative processors at a higher level of abstraction to embody "intelligent" processing. The biological basis of this computing is sketched and the…
Stellar streams are sensitive probes of the Milky Way's gravitational potential. The mean track of a stream constrains global properties of the potential, while its fine-grained surface density constrains galactic substructure. A precise…
Efficient execution of deep learning workloads on dataflow architectures is crucial for overcoming memory bottlenecks and maximizing performance. While streaming intermediate results between computation kernels can significantly improve…
Distributed Stream Processing (DSP) focuses on the near real-time processing of large streams of unbounded data. To increase processing capacities, DSP systems are able to dynamically scale across a cluster of commodity nodes, ensuring a…
Data stream processing systems (DSPSs) enable users to express and run stream applications to continuously process data streams. To achieve real-time data analytics, recent researches keep focusing on optimizing the system latency and…
The immense growth of data demands switching from traditional data processing solutions to systems, which can process a continuous stream of real time data. Various applications employ stream processing systems to provide solutions to…
Stream processing has become a critical component in the architecture of modern applications. With the exponential growth of data generation from sources such as the Internet of Things, business intelligence, and telecommunications,…
Due to recent advances in data collection techniques, massive amounts of data are being collected at an extremely fast pace. Also, these data are potentially unbounded. Boundless streams of data collected from sensors, equipments, and other…
Denoising Diffusion Probabilistic Models (DDPMs) have garnered popularity for data generation across various domains. However, a significant bottleneck is the necessity for whole-network computation during every step of the generative…