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Analytical workloads are evolving as the number of users surges and applications that submit queries in batches become popular. However, traditional analytical databases that optimize-then-execute each query individually struggle to provide timely response ...
Current online applications, such as search engines, social networks, or file sharing services, execute across a distributed network of machines. They provide non-stop services to their users despite failures in the underlying network. To achieve such a hi ...
Many map-reduce frameworks as well as NoSQL systems rely on collection programming as
their interface of choice due to its rich semantics along with an easily parallelizable set of
primitives. Unfortunately, the potential of collection programming is not ...
Synchronous mini-batch SGD is state-of-the-art for large-scale distributed machine learning. However, in practice, its convergence is bottlenecked by slow communication rounds between worker nodes. A natural solution to reduce communication is to use the " ...
Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...
Many medical image analysis tasks require complex learning strategies to reach a quality of image-based decision support that is sufficient in clinical practice. The analysis of medical texture in tomographic images, for example of lung tissue, is no excep ...
Multicopters stand to revolutionize parcel delivery because of their capability to operate in areas with unsuitable road infrastructure and precisely maneuver in cluttered environments. However, current multicopters for delivery can be dangerous for people ...
To meet today's data management needs, it is a widespread practice to use distributed data storage and processing systems. Since the publication of the MapReduce paradigm, a plethora of such systems arose, but although widespread, the capabilities of these ...
Distributed optimization algorithms are essential for training machine learning models on very large-scale datasets. However, they often suffer from communication bottlenecks. Confronting this issue, a communication-efficient primal-dual coordinate ascent ...
Rooted in radio-astronomy, the Bluebild algorithm is a paradigm-changing imager to estimate the sky intensity function. However, current imaging pipelines in radio-astronomy are highly-tailored to the CLEAN-family of imagers, and hence lack the flexibility ...
