Towards Self-Learning Energy Management for Optimal PHEV Operation Around Zero Emission Zones

Self-learning energy management is a promising concept, which optimizes real-world system performance by automated, on-line adaptation of control settings. In this work, the potential of self-learning capabilities related to optimization is studied for energy management in Plug-in Hybrid Electric Vehicles (PHEV). These vehicles are of great...

Adaptive On-the-Fly Changes in Distributed Processing Pipelines

Distributed data processing systems have become the standard means for big data analytics. These systems are based on processing pipelines where operations on data are performed in a chain of consecutive steps. Normally, the operations performed by these pipelines are set at design time, and any changes to their functionality require the...

Lifecycle Governance for Effective Digital Twins: A Joint Systems Engineering and IT Perspective

Digital twins run concurrently with the assets and systems they mirror. They offer real-time execution of advanced control schemes, e.g., for smart buildings, but also various analysis tasks, e.g., for predictive maintenance that ensures resilience. In this, they need to cope with change: The physical twins they mirror operate in a highly...

A Business Process Framework and Operations Map for Maritime Autonomous and Unmanned Shipping: MAUSOM

Maritime autonomous and unmanned shipping have considerable potential for cost-effective and safe cargo or passenger transport and other maritime operations. Such a manner of shipping will introduce operations chains with new roles and responsibilities. Widescale adoption requires an agreed upon overarching business process framework and...

Practical Propagation of Trust in Risk Management Systems

Using risk management systems for large-scale asset management is not without risk itself. Systems that collect measurement from a geographically diverse area, across many organisations, contain many interacting components that can fail in many different ways. In this chapter these systems are discussed from a risk assessment point of view,...

Runtime Modifications of Spark Data Processing Pipelines

Distributed data processing systems are the standard means for large-scale data analysis in the Big Data field. These systems are based on processing pipelines where the processing is done via a composition of multiple elements or steps. In current distributed data processing systems, the code and parameters that create the pipeline are set at...

Dynamically Scaling Apache Storm for the Analysis of Streaming Data

Stream processing platforms allow applications to analyse incoming data continuously. Several use cases exist that make use of these capabilities, ranging from monitoring of physical infrastructures to pre selecting video surveillance feeds for human inspection. It is difficult to predict how much computing resources are needed for these stream...

Analysis cloud: running sensor data analysis programs on a cloud computing infrastructure

Sensors have been used for many years to gather information about their environment. The number of sensors connected to the internet is increasing, which has led to a growing demand of data transport and storage capacity. In addition, evermore emphasis is put on processing the data to detect anomalous situations and to identify trends. This...

Deployment strategies for distributed applications on cloud computing infrastructures

Cloud computing enables on-demand access to a shared pool of IT resources. In the case of Infrastructure as a Service (IaaS), the cloud user typically acquires Virtual Machines (VMs) from the provider. It is up to the user to decide at what time and for how long they want to use these VMs. Because of the pay-per-use nature of most clouds, there...

Analysis Cloud: Running Sensor Data Analysis Programs on a Cloud Computing Infrastructure

Sensors have been used for many years to gather information about their environment. The number of sensors connected to the internet is increasing, which has led to a growing demand of data transport and storage capacity. In addition, evermore emphasis is put on processing the data to detect anomalous situations and to identify trends. This...