Inicio  /  Future Internet  /  Vol: 6 Par: 3 (2014)  /  Artículo
ARTÍCULO
TITULO

7R Data Value Framework for Open Data in Practice: Fusepool

Michael Kaschesky and Luigi Selmi    

Resumen

Based on existing literature, this article makes a case for open (government) data as supporting political efficiency, socio-economic innovation and administrative efficiency, but also finds a lack of measurable impact. It attributes the lack of impact to shortcomings regarding data access (must be efficient) and data usefulness (must be effective). To address these shortcomings, seven key activities that add value to data are identified and are combined into the 7R Data Value Framework, which is an applied methodology for linked data to systematically address both technical and social shortcomings. The 7R Data Value Framework is then applied to the international Fusepool project that develops a set of integrated software components to ease the publishing of open data based on linked data and associated best practices. Real-life applications for the Dutch Parliament and the Libraries of Free University of Berlin are presented, followed by a concluding discussion.

 Artículos similares

       
 
Bing Su and Jiwu Liang    
With the innovation of wireless communication technology and the surge of data in mobile networks, traditional routing strategies need to be improved. Given the shortcomings of existing opportunistic routing strategies in transmission performance and sec... ver más
Revista: Future Internet

 
Haneul Lee and Seokheon Yun    
Accurately predicting construction costs during the initial planning stages is crucial for the successful completion of construction projects. Recent advancements have introduced various machine learning-based methods to enhance cost estimation precision... ver más
Revista: Buildings

 
Jiantao Qu, Chunyu Qi and He Meng    
Within the Shuo Huang Railway Company (Suning, China ) the long-term evolution for railways (LTE-R) network carries core wireless communication services for trains. The communication performance of LTE-R cells directly affects the operational safety of t... ver más
Revista: Future Internet

 
Xiaokai Sun, Baoyun Guo, Cailin Li, Na Sun, Yue Wang and Yukai Yao    
In urban point cloud scenarios, due to the diversity of different feature types, it becomes a primary challenge to effectively obtain point clouds of building categories from urban point clouds. Therefore, this paper proposes the Enhanced Local Feature A... ver más

 
Futo Ueda, Hiroto Tanouchi, Nobuyuki Egusa and Takuya Yoshihiro    
River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrological... ver más
Revista: Water