Abstract

In this contribution, we give an insight in our experiences in the technical and organizational realization of industrial analytics. We address challenges in implementing industrial analytics in real-world applications and discuss aspects to consider when designing a machine learning solution for production. We focus on technical and organizational aspects to make industrial analytics work for real-world applications in factory automation. As an example, we consider a machine learning use case in the area of industry compressors. We discuss the importance of scalability and reusability of data analytics pipelines and present a container-based system architecture. Document type: Part of book or chapter of book

Abstract

A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction.\\ud \\ud We propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios. Document type: Part of book or chapter of book

Abstract

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems. Document type: Article