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In recent years, mobility solutions have experienced a significant upswing. Consequently, it has increased the importance of forecasting the number of passengers and determining the associated demand for vehicles. We analyze all bus routes in a rural area in contrast to other work that predicts just a single bus route. Some differences in bus routes in rural areas compared to cities are highlighted and substantiated by a case study data using Roding, a town in the rural district of Cham in northern Bavaria, as an example. Data collected and we selected a random forest model that lets us determine the passenger demand, bus line effectiveness, or general user behavior. The prediction accuracy of the selected model is currently 87%. The collected data helps to build new mobility-as-a-service solutions, such as on-call buses or dynamic route optimizations, as we show with our simulation.
Any re-design of a distributed legacy system requires a migration which involves numerous complex data replication and transformation steps. Migration procedures can become quite difficult and time-consuming, especially when the setup (i.e., the employed databases, encodings, formats etc.) of the legacy and the target system fundamentally differ, which is often the case with finance data, grown over decades. We report on experiences from a real-world project: the recent migration of a customer loyalty system from a COBOL-operated mainframe to a modern service-oriented architecture. In this context, we present our easy-to-adopt solution for running most replication steps in a high-performance manner: the QuickApply HPC-software which helps minimizing the replication time, and, thereby, the overall downtime of the migration. Business processes can be kept up and running most of the time, while pre-extracted data already pass a variety of platforms and representations toward the target system. We combine the advantages of traditional migration approaches: transformations, which require the interruption of business processes are performed with static data only, they can be made undone in case of a failure and terminate quickly, due to the use of parallel processing.
Programming GPUs with low-level libraries like CUDA and OpenCL is a tedious and error-prone task. Fortunately, algorithmic skeletons can shield developers from the complexity of parallel programming by encapsulating common parallel computing patterns. However, this simplification typically constrains programmers to write their applications using the GPU library employed by the skeleton implementation. In this work, we combine skeletal programming with modeldriven software development (MDSD) to increase the freedom of choice regarding the employed GPU library instead of leaving all technical decisions to the skeleton implementation. We present a code-generator that transforms models comprising skeletons, their input data and input functions to parallel C++ code while taking care of data-offset calculations. The generator has been tested using different GPU and multi-GPU communication libraries such as Thrust and CUDA-MPI. We demonstrate our novel approach to GPU programming with two example applications: affinity propagation and n-body simulation.
While high-level software components simplify the programming of grid applications and Web services increase their interoperability, developing such components and configuring the interconnecting services is a demanding task. In this paper, we consider the combination of Higher-Order Components (HOCs) with the Fractal component model and the ProActive library.
HOCs are parallel programming components, made accessible on the grid via Web services that use a special class loader enabling code mobility: executable code can be uploaded to a HOC, allowing one to customize the HOC. Fractal simplifies the composition of components and the ProActive library offers a generator for automatically creating Web services from components composed with Fractal, as long as all the parameters of these services have primitive types.
Taking all the advantages of HOCs, ProActive and Fractal together, the obvious conclusion is that composing HOCs using Fractal and automatically exposing them as Web services on the grid via ProActive minimizes the required efforts for building complex grid systems. In this context, we solved the problem of exchanging code-carrying parameters in automatically generated Web services by integrating the HOC class loading mechanism into the ProActive library.
In the engineering domain, representing real-world objects using a body of data, called a digital twin, which is frequently updated by “live” measurements, has shown various advantages over tradi- tional modelling and simulation techniques. Consequently, urban planners have a strong interest in digital twin technology, since it provides them with a laboratory for experimenting with data before making far-reaching decisions. Realizing these decisions involves the work of professionals in the architecture, engineering and construction (AEC) domain who nowadays collaborate via the methodology of building information modeling (BIM). At the same time, the citizen plays an integral role both in the data acquisition phase, while also being a beneficiary of the improved resource management strategies. In this paper, we present a prototype for a “digital energy twin” platform we designed in cooperation with the city of Regensburg. We show how our extensible platform de- sign can satisfy the various requirements of multiple user groups through a series of data processing solutions and visualizations, in- dicating valuable design and implementation guidelines for future projects. In particular, we focus on two example use cases concern- ing building electricity monitoring and BIM. By implementing a flexible data processing architecture we can involve citizens in the data acquisition process, meeting the demands of modern users regarding maximum transparency in the handling of their data.
Forschung 2019
(2019)
Recent advances in the development of smart homes have led to the availability of a wide variety of devices providing a high level of convenience via gesture and speech control or fully automated operation. Many smart home appliances also address the aspects of safety and electricity savings by automatically powering themselves off after not being used for a while. However, many devices remain in a typical household that are not themselves "smart", or are not primarily electric (such as heating systems). We address the savings aspect by identifying processes involving the use of multiple devices in the electrical flow data, as captured by a smart meter in a modern household, rather than focusing on a single appliance. Therefore, we introduce a novel approach to usage pattern analysis based on the idea that a pattern of device usages as a result of a resident's 'routine' (such as making breakfast) can be interpreted similarly to a natural language 'sentence'; Natural Language Processing (NLP) algorithms can then be used for interpreting the residents' behavior. We introduce the notion of bag-of-devices (BoD), derived from the bag-of-words model used in document classification. In an experiment, we show how we use this model to infer predictions about the inhabitants from device usage, such as the resident leaving for the day or just to fetch the newspaper.
In the realm of parallel computing, optimization plays a pivotal role in achieving efficient and scalable solutions. In this work, we present the parallelization of a hybrid genetic search for solving the Capacitated Vehicle Routing Problem with Pickup and Delivery (CVRPPD).It leverages the synergy between genetic algorithms and parallel computing to address the complex optimization problem. This hybrid algorithm combines a customized version of local search with a genetic algorithm to compute an effective solution. Our implementation makes use of the Message Passing Interface (MPI) for data distribution and parallel execution. In addition, we run multi-threaded processes on NVIDIA graphical processors using the CUDA technology, which further increases the computation speed and consequently minimizes the runtime. Parallelization also allows the best-improvement strategy to be used instead of the rst-improvement strategy while maintaining the same runtime. We store the resulting routes in a bus route database which we created as the basis of an extensive library of optimal routes for our specifc use case of optimizing bus routes in a rural area. The experimental results on real road data show that the parallel implementation of the Hybrid Genetic Search (HGS) achieves significant improvements in runtime over the sequential implementation above a certain problem size. We believe that our implementation of the parallel hybrid genetic search method can have a great in influence on optimization strategies in parallel computing and can also be applied to other subproblems of the VRP.
Forschung 2018
(2018)
Control and Automation of services of the urban infrastructure offered to citizens and tourists are elementary parts of a smart city. But both rely on a stable supply of data from sensors spread across the whole city, e. g., the fill level sensors of waste bins needed for a waste management tool which we developed in a collaboration with the Regensburg city council for the on-demand collection of waste bins. Europe has a lot of historic cities like Regensburg with narrow streets and huge building walls, some made from granite and fieldstones, which often represents an insurmountable obstacle to wireless data transmission. The reduction of the road traffic volume poses an additional challenge for city planners. By means of networked planning and simulation software, the situation, state and efficiency of citywide logistic services can be monitored and optimized. In the course of such optimizations, we propose the combination of digital and logistic services. As an example, we show that monitoring state information, such as the waste bin fill levels, can be accomplished using the same vehicles and the same planning software, that is used for luggage transportation. Moreover, we describe how we adapted a solver for a variant of the TSP, namely the prize-collecting traveling salesman, to optimize the route planning dynamically.