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Heterogeneous accelerator enhanced computing architectures are a common solution in embedded computing, mainly due to the constraints in energy and power efficiency. Such accelerator enhanced systems dispatch data- and computing-intensive tasks to specialized, optimized and thus efficient hardware units, leaving most control flow tasks for the more generic but less efficient central processing units (CPUs). Nowadays, also high-performance computing (HPC) systems are becoming more heterogeneous by incorporating accelerators into the computing nodes.In this chapter, we introduce the concept of heterogeneous computing and present the design of a hardware accelerator for solving the Link Assessment (LA) problem, in introduced Chapter 3. The hardware accelerator integrates its main dedicated processing units with a customized cache design and light-weight data path. We provide detailed area, energy, and timing results for a 28 nm application specific integrated circuit (ASIC) process and DDR3 memory devices. Compared to an CPU-based cluster, our proposed solution uses 38x less memory and is 1030x more energy efficient for processing a users-movies dataset with half a million edges.

Complex graphs are at the heart of today’s big data challenges like recommendation systems, customer behavior modeling, or incident detection systems. One reoccurring task in these fields is the extraction of network motifs, which are subgraphs that are reoccurring and statistically significant. To assess the statistical significance of their occurrence, the observed values in the real network need to be compared to their expected value in a random graph model.In this chapter, we focus on the so-called Link Assessment (LA) problem, in particular for bipartite networks. Lacking closed-form solutions, we require stochastic Monte Carlo approaches that raise the challenge of finding appropriate metrics for quantifying the quality of results (QoR) together with suitable heuristics that stop the computation process if no further increase in quality is expected. We provide investigation results for three quality metrics and show that observing the right metrics reveals so-called phase transitions that can be used as a reliable basis for such heuristics. Finally, we propose a heuristic that has been evaluated with real-word datasets, providing a speedup of $$15.4\times $$ 15.4 × over previous approaches.

News diversity in the media has for a long time been a foundational and uncontested basis for ensuring that the communicative needs of individuals and society at large are met. Today, people increasingly rely on online content and recommender systems to consume information challenging the traditional concept of news diversity. In addition, the very concept of diversity, which differs between disciplines, will need to be re-evaluated requiring an interdisciplinary investigation, which requires a new level of mutual cooperation between computer scientists, social scientists, and legal scholars. Based on the outcome of a interdisciplinary workshop, we have the following recommendations, directed at researchers, funders, legislators, regulators, and the media industry: - Conduct interdisciplinary research on news recommenders and diversity. - Create a safe harbor for academic research with industry data. - Strengthen the role of public values in news recommenders. - Create a meaningful governance framework for news recommenders. - Fund a joint lab to spearhead the needed interdisciplinary research, boost practical innovation, develop reference solutions, and transfer insights into practice., DagMan, Volume 9, Issue 1, pages 43-61

Socioinformatics is a new scientific approach to study the interactions between humans and IT. These proceedings are a collection of the contributions during a workshop of the Gesellschaft fr Informatik (GI). Researchers in this emerging field discuss the main aspects of interactions between IT and humans with respect to; social connections, social changes, acceptance of IT and the social conditions affecting this acceptance, effects of IT on humans and in response changes of IT, structures of the society and the influence of IT on these structures, changes of metaphysics influenced by IT and the social context of a knowledge society.

We pose a new visualization challenge, asking Graph Drawing algorithms to cope with the requirements of Streaming applications. In this model a source produces a graph one edge at a time. When an edge is produced, it is immediately drawn and its placement cannot be altered. The drawing has an image persistence, that controls the lifetime of edges. If the persistence is k, an edge remains in the drawing for the time spent by the source to generate k edges, and then it fades away. In this model we study the area requirement of planar straight-line grid drawings of trees and we assess the output quality of the presented algorithms by computing the competitive ratio with respect to the best known offline algorithms.