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Auflistung nach Schlagwort "stream processing"

1 - 4 von 4
  • Zeitschriftenartikel
    Adaptive Application Performance Management for Big Data Stream Processing
    (Softwaretechnik-Trends Band 35, Heft 3, 2015) Eichelberger, Holger; Qin, Cui; Schmid, Klaus; Niederée, Claudia
    Big data applications with their high-volume and dynamically changing data streams impose new challenges to application performance management. Efficient and effective solutions must balance performance versus result precision and cope with dramatic changes in real-time load and needs without overprovisioning resources. Moreover, a developer should not be burdened too much with addressing performance management issues, so he can focus on the functional perspective of the system For addressing these challenges, we present a novel comprehensive approach, which combines software configuration, model-based development, application performance management and runtime adaptation.
  • Konferenzbeitrag
    The Role of Performance in Streaming Analytics Projects: Expert Interviews on Current Challenges and Future Research Directions
    (Softwaretechnik-Trends Band 43, Heft 1, 2023) Rank, Johannes; Hein, Andreas; Krcmar, Helmut
    Stream processing systems (SPS) are becoming more frequent due to current trends such as Industry 4.0 or the Internet of Things. These systems’ performance is particularly important, as their timely processing is a crucial capability. At the same time, these systems are often combined with novel machine learning approaches (steaming analytics) that have high performance demands. This combination poses potential challenges for performance management. In this paper, we have conducted expert interviews in the industry to identify performance challenges in streaming analytics implementations and to derive future research directions to address them. Our analysis shows that while the experts had different opinions on the role of performance in project management, they agreed on five common challenges.
  • Zeitschriftenartikel
    A self-portrayal of GI Junior Fellow Matthias Weidlich: Event-driven analysis of service processes
    (it - Information Technology: Vol. 60, No. 1, 2018) Weidlich, Matthias
    In domains such as e-commerce, logistics, or healthcare, the conduct of service processes is widely supported by information systems and event data is generated continuously during process execution. Such event data constitutes a valuable source of information to monitor and improve the respective service processes. My research focuses on models and methods to support event-driven analysis of service processes. Specifically, I study how event logs produced by information systems are used to automatically construct models for qualitative and quantitative analysis. Aiming at online assessment and predictive analysis of a process' behaviour, I develop monitoring techniques that utilise streams of event data produced by diverse sources. Architectures that enable efficient handling of event streams are another focal point of my research. In this article, I outline some of the related research questions and highlight my recent results in these areas.
  • Konferenzbeitrag
    Workload Prediction for IoT Data Management Systems
    (BTW 2023, 2023) Burrell, David; Chatziliadis, Xenofon; Zacharatou, Eleni Tzirita; Zeuch, Steffen; Markl, Volker
    The Internet of Things (IoT) is an emerging technology that allows numerous devices, potentially spread over a large geographical area, to collect and collectively process data from high-speed data streams.To that end, specialized IoT data management systems (IoTDMSs) have emerged.One challenge in those systems is the collection of different metrics from devices in a central location for analysis. This analysis allows IoTDMSs to maintain an overview of the workload on different devices and to optimize their processing. However, as an IoT network comprises of many heterogeneous devices with low computation resources and limited bandwidth, collecting and sending workload metrics can cause increased latency in data processing tasks across the network.In this ongoing work, we present an approach to avoid unnecessary transmission of workload metrics by predicting CPU, memory, and network usage using machine learning (ML).Specifically, we demonstrate the performance of two ML models, linear regression and Long Short-Term Memory (LSTM) neural network, and show the features that we explored to train these models.This work is part of an ongoing research to develop a monitoring tool for our new IoTDMS named NebulaStream.