Monitoring of Stream Processing Engines Beyond the Cloud: An Overview

The Internet of Things (IoT) is rapidly growing into a network of billions of interconnected physical devices that constantly stream data. To enable data-driven IoT applications, data management systems like NebulaStream have emerged that manage and process data streams, potentially in combination with data at rest, in a heterogeneous distributed environment of cloud and edge devices. To perform internal optimizations, an IoT data management system requires a monitoring component that collects system metrics of the underlying infrastructure and application metrics of the running processing tasks. In this paper, we explore the applicability of existing cloud-based monitoring solutions for stream processing engines in an IoT environment. To this end, we provide an overview of commonly used approaches, discuss their design, and outline their suitability for the IoT. Furthermore, we experimentally evaluate different monitoring scenarios in an IoT environment and highlight bottlenecks and inefficiencies of existing approaches. Based on our study, we show the need for novel monitoring solutions for the IoT and define a set of requirements

A gaze independent brain-computer interface based on visual stimulation through closed eyelids

A classical brain-computer interface (BCI) based on visual event-related potentials (ERPs) is of limited application value for paralyzed patients with severe oculomotor impairments. In this study, we introduce a novel gaze independent BCI paradigm that can be potentially used for such end-users because visual stimuli are administered on closed eyelids. The paradigm involved verbally presented questions with 3 possible answers. Online BCI experiments were conducted with twelve healthy subjects, where they selected one option by attending to one of three different visual stimuli. It was confirmed that typical cognitive ERPs can be evidently modulated by the attention of a target stimulus in eyes-closed and gaze independent condition and further classified with high accuracy during online operation (74.58% ± 17.85 s.d.; chance level 33.33%), demonstrating the effectiveness of the proposed novel visual ERP paradigm. Also, stimulus-specific eye movements observed during stimulation were verified as reflex responses to light stimuli and they did not contribute to classification. To the best of our knowledge, this study is the first to show the possibility of using a gaze independent visual ERP paradigm in an eyes-closed condition, thereby providing another communication option for severely locked-in patients suffering from complex ocular dysfunctions.BMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine InteraktionBMBF, 16SV5839, Maschinelles Lernen zur Optimierung der Kommunikation schwerstgelähmter Patienten per BC

NebulaStream: Complex Analytics Beyond the Cloud

The arising Internet of Things (IoT) will require significant changes to current stream processing engines (SPEs) to enable large-scale IoT applications. In this paper, we present challenges and opportunities for an IoT data management system to enable complex analytics beyond the cloud. As one of the most important upcoming IoT applications, we focus on the vision of a smart city. The goal of this paper is to bridge the gap between the requirements of upcoming IoT applications and the supported features of an IoT data management system. To this end, we outline how state-of-the-art SPEs have to change to exploit the new capabilities of the IoT and showcase how we tackle IoT challenges in our own system, NebulaStream. This paper lays the foundation for a new type of systems that leverages the IoT to enable large-scale applications over millions of IoT devices in highly dynamic and geo-distributed environments