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Tackling complex reasoning tasks typically relies on massive monolithic LLMs, which suffer from severe computational redundancy. While task decomposition through structured pipelines or multi-agent collaborations offers an alternative,…
The complexity of multi-layered, data-intensive systems demands frameworks that ensure flexibility, scalability, and efficiency. DATCloud is a model-driven framework designed to facilitate the modeling, validation, and refinement of…
The data engineering and data science community has embraced the idea of using Python & R dataframes for regular applications. Driven by the big data revolution and artificial intelligence, these applications are now essential in order to…
Recently, distributed processing of large dynamic graphs has become very popular, especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel…
We study the modeling and simulation of gas pipeline networks, with a focus on fast numerical methods for the simulation of transient dynamics. The obtained mathematical model of the underlying network is represented by a nonlinear…
Dataflow devices represent an avenue towards saving the control and data movement overhead of Load-Store Architectures. Various dataflow accelerators have been proposed, but how to efficiently schedule applications on such devices remains…
We present a new way to visualize a large graph in the style of online geographic maps. The method builds a tile pyramid for semantic zoom: at every zoom level the labels of the highest-ranked nodes remain readable, just as the names of…
Graph Neural Networks (GNNs) are vital for learning from graph-structured data, enabling applications in network analysis, recommendation systems, and speech analytics. Deploying them on edge devices like client PCs and laptops enhances…
Multivariate Gaussian processes (GPs) offer a powerful probabilistic framework to represent complex interdependent phenomena. They pose, however, significant computational challenges in high-dimensional settings, which frequently arise in…
With the advent of the Internet of Things (IoT), novel critical applications have emerged that leverage the edge/hub/cloud paradigm, which diverges from the conventional edge computing perspective. A growing number of such applications…
Hardware synthesis from high-level descriptions remains fundamentally limited by the sequential optimization of interdependent design decisions. Current methodologies, including state-of-the-art high-level synthesis (HLS) tools,…
High performance large scale graph analytics are essential to timely analyze relationships in big data sets. Conventional processor architectures suffer from inefficient resource usage and bad scaling on those workloads. To enable efficient…
We demonstrate a novel table discovery pipeline called DIALITE that allows users to discover, integrate and analyze open data tables. DIALITE has three main stages. First, it allows users to discover tables from open data platforms using…
Scaling data volume and diversity is critical for generalizing embodied intelligence. While synthetic data generation offers a scalable alternative to expensive physical data acquisition, existing pipelines remain fragmented and…
We implemented a real-time data processor (rta-dp) framework that can be used to develop real-time analysis pipelines and data handling systems to manage high-throughput data streams with distributed applications in the context of ground…
The Dynamical Graph Grammar (DGG) formalism can describe complex system dynamics with graphs that are mapped into a master equation. An exact stochastic simulation algorithm may be used, but it is slow for large systems. To overcome this…
Due to the Internet of Everything (IoE), data generated in our life become larger. As a result, we need more effort to analyze the data and extract valuable information. In the cloud computing environment, all data analysis is done in the…
Many numerical methods for evaluating matrix functions can be naturally viewed as computational graphs. Rephrasing these methods as directed acyclic graphs (DAGs) is a particularly effective approach to study existing techniques, improve…
Future satellite networks are expected to have thousands of low Earth orbit (LEO) satellites orbiting Earth at very high speeds. User equipment (UE) communicating directly with LEO satellites will experience frequent handovers. Managing the…
Apache Spark is a Big Data framework for working on large distributed datasets. Although widely used in the industry, it remains rather limited in the academic community or often restricted to software engineers. The goal of this paper is…