Linked Data - wo bleiben die Anwendungen?

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Linked Data ist als Stichwort inzwischen seit einigen Jahren auch im Bibliotheksumfeld virulent. Trotzdem gibt es noch keine große bekannte Linked Data Anwendung. Woran liegt es und wann kommt endlich die Killer-App

Interconnection network with a shared whiteboard: Impact of (a)synchronicity on computing power

In this work we study the computational power of graph-based models of distributed computing in which each node additionally has access to a global whiteboard. A node can read the contents of the whiteboard and, when activated, can write one message of O(log n) bits on it. When the protocol terminates, each node computes the output based on the final contents of the whiteboard. We consider several scheduling schemes for nodes, providing a strict ordering of their power in terms of the problems which can be solved with exactly one activation per node. The problems used to separate the models are related to Maximal Independent Set, detection of cycles of length 4, and BFS spanning tree constructions

Sollen wir Bibliothekare jetzt alle Informatiker werden? : Forschungsdatenmanagement, Datenerhaltung und -pflege als neue Aufgabenfelder

Immer mehr Bibliotheken übernehmen Aufgaben im Bereich der Datenerhaltung und -pflege. Informatiker/innen tragen im Öffentlichen Dienst oft die Bezeichnung »Angestellte/r in der Datenverwaltung«, obwohl die Verwaltung gespeicherter Daten schon lange keine Aufgabe mehr nur für Informatiker/innen darstellt. Gleichzeitig geht die bibliothekarische Arbeit über die klassische Literaturversorgung hinaus und umfasst zunehmend mehr und mehr technische Bereiche. Ein hochaktuelles Tätigkeitsfeld für wissenschaftliche Bibliotheken stellt das Forschungsdatenmanagement dar. Was verbirgt sich dahinter und welche Anforderungen ergeben sich daraus für das bibliothekarische Berufsbild? Sollen wir jetzt alle Informatiker/ innen werden

Evaluation of the socially evaluated cold-pressor group test (SECPT-G) in the general population

Background In stress research, economic instruments for introducing acute stress responses are needed. In this study, we investigated whether the socially evaluated cold-pressor group test (SECPT-G) induces salivary alpha-amylase (sAA) and/or cortisol responses in the general population and whether this is associated with anthropometric, experimental, and lifestyle factors. Methods A sample of 91 participants from the general population was recruited. Salivary cortisol and sAA levels were assessed prior to (t0), immediately after (t1), and 10 min after the SECPT-G (t2). Results A robust cortisol increase was found immediately after the SECPT-G, which further increased between t1 and t2. This was independent of most of the control variables. However, men showed a trend toward higher cortisol increases than women (p = 0.005). No sAA responses were found at all. However, sAA levels were dependent on measurement time point with highest levels between 9 pm and 9:30 pm. Participants who immersed their hands into the ice water for the maximally allowed time of 3 min showed higher sAA levels at all time points than participants who removed their hands from the water earlier. Conclusions We conclude that the SECPT-G is a good means of an acute stress test when cortisol—but not necessarily sAA—responses are intended

Coalition Formation Algorithm of Prosumers in a Smart Grid Environment

In a smart grid environment, we study coalition formation of prosumers that aim at entering the energy market. It is paramount for the grid operation that the energy producers are able to sustain the grid demand in terms of stability and minimum production requirement. We design an algorithm that seeks to form coalitions that will meet both of these requirements: a minimum energy level for the coalitions and a steady production level which leads to finding uncorrelated sources of energy to form a coalition. We propose an algorithm that uses graph tools such as correlation graphs or clique percolation to form coalitions that meet such complex constraints. We validate the algorithm against a random procedure and show that, it not only performs better in term of social welfare for the power grid, but also that it is more robust against unforeseen production variations due to changing weather conditions for instance.Comment: 6 pages, 4 figures, 1 table. submited to ICC 201

Leveraging Reinforcement Learning for Task Resource Allocation in Scientific Workflows

Scientific workflows are designed as directed acyclic graphs (DAGs) and consist of multiple dependent task definitions. They are executed over a large amount of data, often resulting in thousands of tasks with heterogeneous compute requirements and long runtimes, even on cluster infrastructures. In order to optimize the workflow performance, enough resources, e.g., CPU and memory, need to be provisioned for the respective tasks. Typically, workflow systems rely on user resource estimates which are known to be highly error-prone and can result in over- or underprovisioning. While resource overprovisioning leads to high resource wastage, underprovisioning can result in long runtimes or even failed tasks. In this paper, we propose two different reinforcement learning approaches based on gradient bandits and Q-learning, respectively, in order to minimize resource wastage by selecting suitable CPU and memory allocations. We provide a prototypical implementation in the well-known scientific workflow management system Nextflow, evaluate our approaches with five workflows, and compare them against the default resource configurations and a state-of-the-art feedback loop baseline. The evaluation yields that our reinforcement learning approaches significantly reduce resource wastage compared to the default configuration. Further, our approaches also reduce the allocated CPU hours compared to the state-of-the-art feedback loop by 6.79% and 24.53%.Comment: Paper accepted in 2022 IEEE International Conference on Big Data Workshop BPOD 202