Abstract Web search is a crucial technology for the digital economy. Dominated by a few gatekeepers focused on commercial success, however, web publishers have to optimize their content for these gatekeepers, resulting in a closed ecosystem of search engines as well as the risk of publishers sacrificing quality. To encourage an open search ecosystem and offer users genuine choice among alternative search engines, we propose the development of an Open Web Index (OWI). We outline six core principles for developing and maintaining an open index, based on open data principles, legal compliance, and collaborative technology development. The combination of an open index with what we call declarative search engines will facilitate the development of vertical search engines and innovative web data products (including, e.g., large language models), enabling a fair and open information space. This framework underpins the EU‐funded project OpenWebSearch.EU, marking the first step towards realizing an Open Web Index.

Summary Load balancing is often a challenge in task-parallel applications. The balancing problems are divided into static and dynamic. “Static” means that we have some prior knowledge about load information and perform balancing before execution, while “dynamic” must rely on partial information of the execution status to balance the load at runtime. Conventionally, work stealing is a practical approach used in almost all shared memory systems. In distributed memory systems, the communication overhead can make stealing tasks too late. To improve, people have proposed a reactive approach to relax communication in balancing load. The approach leaves one dedicated thread per process to monitor the queue status and offload tasks reactively from a slow to a fast process. However, reactive decisions might be mistaken in high imbalance cases. First, this article proposes a performance model to analyze reactive balancing behaviors and understand the bound leading to incorrect decisions. Second, we introduce a proactive approach to improve further balancing tasks at runtime. The approach exploits task‐based programming models with a dedicated thread as well, namely . Nevertheless, the main idea is to force not only to monitor load; it will characterize tasks and train load prediction models by online learning. “Proactive” indicates offloading tasks before each execution phase proactively with an appropriate number of tasks at once to a potential victim (denoted by an underloaded/fast process). The experimental results confirm speedup improvements from to in important use cases compared to the previous solutions. Furthermore, this approach can support co‐scheduling tasks across multiple applications.

In this poster contribution, we present a scheduling system for automated remote operation of instruments at high-altitude research facilities and similar remote sites. Via web-based interfaces, the system allows instrument owners as well as authorized third-party scientists to schedule and execute measurements and observations.The system has been developed as a thesis project in the context of the AlpEnDAC-II ("Alpine Environmental Data Analysis Centre", www.alpendac.eu) collaboration (funded by the Bavarian State Ministry of the Environment and Consumer Protection). Consequently, the scheduler and interfaces have been integrated with the AlpEnDAC Operating-on-Demand functionalities. A first use case for the framework has been the operation of an airglow imager (FAIM) in Oberpfaffenhofen (DE).We describe the design and implementation of our system for scheduling and execution of multi-user observations on instruments, including scheduling-data transfers and data retrieval. Our core implementation uses an optimization-based scheduler (Google’s OR-Tools) to ensure maximum instrument use and to minimize idle times. Results show that the scheduler is reliable, fast, and is consistently able to provide optimal observation plans. The extensibility of the system is guaranteed by the usage of modern software in the core of the system, including well-defined and specified communication through REST APIs. Thus, it can easily be adapted to other settings and instruments, which is also facilitated by a modern deployment strategy using Docker and Kubernetes.

Two-dimensional deformation estimates derived from Persistent Scatterer Interferometric (PSI) analysis of Synthetic Aperture Radar (SAR) data can improve the characterisation of spatially and temporally varying deformation processes of Earth’s surface. In this study, we examine the applicability of Persistent Scatterer (PS) Line-Of-Sight (LOS) estimates in providing two-dimensional deformation information, focusing on the retrieval of the local surface-movement processes. Two Sentinel-1 image stacks, ascending and descending, acquired from 2015 to 2018, were analysed based on a single master interferometric approach. First, Interferometric SAR (InSAR) deformation signals were corrected for divergent plate spreading and the Glacial Isostatic Adjustment (GIA) signals. To constrain errors due to rasterisation and interpolation of the pointwise deformation estimates, we applied a vector-based decomposition approach to solve the system of linear equations, resulting in 2D vertical and horizontal surface-deformation velocities at the PSs. We propose, herein, a two-step decomposition procedure that incorporates the Projected Local Incidence Angle (PLIA) to solve for the potential slope-deformation velocity. Our derived 2D velocities reveal spatially detailed movement patterns of the active Svínafellsjökull slope, which agree well with the independent GPS time-series measurements available for this area.

In citizen science, citizens are encouraged to participate in research, with web technologies promoting location-independent participation and broad knowledge sharing. In this study, web technologies were extracted from 112 citizen science projects listed on the “Bürger schaffen Wissen”. Four indicators on web technologies—Online platforms, Educational tools, Social media, and Data sharing between projects—were chosen to quantify the extent to which web technologies are used within citizen science projects. The results show that the use of web technologies is already very well established in both the natural and social science projects and only the possibilities for data sharing between projects are limited.