Identification and characterization of information-networks in long-tail data collections. (August 2017)

• Record Type: Journal Article
• Authors: Elag, Mostafa M.; Kumar, Praveen; Marini, Luigi; Myers, James D.; Hedstrom, Margaret; Plale, Beth A.
• Is Part Of: Environmental modelling & software. Volume 94(2017)
• Page Start: 100
• Page End: 111
• Publication Date: 2017-08
• Subjects: Long-tail data -- Information-networks -- Linked-data -- Cyberinfrastructure -- Environmental data -- Data-intensive science; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.03.032
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Scientists' ability to synthesize and reuse long-tail scientific data lags far behind their ability to collect and produce these data. Many Earth Science Cyberinfrastructures enable sharing and publishing their data over the web using metadata standards. While profiling data attributes advances the Linked Data approach, it has become clear that building information-networks among distributed data silos is essential to increase their integration and reusability. In this research, we developed a Long-Tail Information-Network (LTIN) model, which uses a metadata-driven approach to build semantic information-networks among datasets published over the web and aggregate them around environmental events. The model identifies and characterizes the spatial and temporal contextual association links and dependencies among datasets. This paper presents the design and application of the LTIN model, and an evaluation of its performance. The model capabilities were demonstrated by inferring the information-network of a stream discharge located at the downstream end of the Illinois River. Highlights: A metadata driven approach is proposed to build Information-Networks for long-tail data. A model is built to identify and characterize the contextual association among datasets. The model provides a semantic information-network. The performance of the model is evaluated in terms of time and scalability. The models is used to infer the information network at the outlet of Illinois River.