Dynamic Planning for Link Discovery

Georgala, Kleanthi; Obraczka, Daniel; Ngomo, Axel-Cyrille Ngonga

doi:10.1007/978-3-319-93417-4_16

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…The architecture within FAIR shall be capable to transform complex climate and weather data into a flexible but userspecific and user-friendly output. The approach was inspired by two research projects BASMATI (Altmann et al, 2017) and GEISER (Georgala et al, 2018). It is based on three assumptions:…”

Section: Technical Implementation and Orchestration Of Fair Servicesmentioning

confidence: 99%

“…Under the umbrella of FAIR, a research project funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI), DWD and eight partners from industry and universities collaborate in order to improve the access and to facilitate the exploitation of open climate and weather data for a broad spectrum of users. To find a generic solution for the wide range of individual requests to the heterogeneous raw data (Gregow et al, 2016), the central idea is to approach the problem with the implementation of a variety of micro services. Each of the services is planned to solve only one independent task, but the combination of the services will provide a complete end-to-end chain from raw data to the specific answer.…”

Section: Introductionmentioning

confidence: 99%

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FAIR: a project to realize a user-friendly exchange of open weather data

Frank¹,

Kaspar

Keller

et al. 2020

Adv. Sci. Res.

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Abstract. Access to high quality weather and climate data is crucial for a wide range of societal and economic issues. It allows optimising industrial processes, supports the identification of potential risks related to climate change or allows the development of corresponding adaptation and mitigation strategies. Although such data is freely available through Germany’s national meteorological service DWD (Deutscher Wetterdienst) since 2017, the application potential in industry and society has certainly not yet been fully unlocked. Major obstacles are the complexity of the raw data, as well as missing tools for their simple integration into existing industrial applications. The goal of the research project FAIR is to simplify the information exchange between the DWD and economical players. In order to reach this goal a requirement analysis with end-users of weather data from three different sectors was conducted. A central requirement regarding the site assessment of wind plants is quick and easy access to historical wind-series at specific sites. Preferably downloadable in formats like CSV or via an API. Event planning partners are interested in a quick access to health relevant weather information at their event location, and the E-mobility sector in temperature data along planned routes. In this paper, we summarize the results of the requirement analysis and present the deduced technical architecture and FAIR services aiming at a user-friendly exchange of weather data.

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Section: Technical Implementation and Orchestration Of Fair Servicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FAIR: a project to realize a user-friendly exchange of open weather data

Frank¹,

Kaspar

Keller

et al. 2020

Adv. Sci. Res.

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“…This information includes (1) the location of the input knowledge bases K 1 and K 2 (e.g., SPARQL endpoints or files), (2) the specification of the sets S and T, (3) the measures and thresholds or the machine learning approach to use. The current version of Limes supports RDF configuration files based on the Limes Configuration Ontology (LCO) 5 (see Fig. 3) and XML configuration files based on the Limes Specification Language (LSL) [15].…”

Section: Architecture Of the Frameworkmentioning

confidence: 99%

“…To this end, the planner relies on runtime approximations derived from linear regressions on large static dictionaries. Condor [5] builds upon HeLios by extending the static planner with dynamic planning to achieve even better runtime.…”

Section: Scalability Algorithmsmentioning

confidence: 99%

LIMES: A Framework for Link Discovery on the Semantic Web

et al. 2021

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The Linked Data paradigm builds upon the backbone of distributed knowledge bases connected by typed links. The mere volume of current knowledge bases as well as their sheer number pose two major challenges when aiming to support the computation of links across and within them. The first is that tools for link discovery have to be time-efficient when they compute links. Secondly, these tools have to produce links of high quality to serve the applications built upon Linked Data well. Solutions to the second problem build upon efficient computational approaches developed to solve the first and combine these with dedicated machine learning techniques. The current version of the Limes framework is the product of seven years of research on these two challenges. A series of machine learning techniques and efficient computation approaches were developed and integrated into this framework to address the link discovery problem. The framework combines these diverse algorithms within a generic and extensible architecture. In this article, we give an overview of version 1.7.4 of the open-source release of the framework. In particular, we focus on an overview of the architecture of the framework, an intuition of its inner workings and a brief overview of the approaches it contains. Some descriptions of the applications within which the framework was used complete the paper. Our framework is open-source and available under a GNU license at https://github.com/dice-group/LIMES together with a user manual and a developer manual.

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