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Utilization of Satellite Geophysical Data as Precursors for Earthquake Monitoring
Published in Ramesh P. Singh, Darius Bartlett, Natural Hazards, 2018
The study area and the generalized geological map over Gujarat are shown in Figures 4.1 and 4.2, respectively. The details of high-resolution satellite-derived gravity data have been discussed by Hwang et al. (2002). They carried out very detailed data assimilation for calculation of the deflection of the vertical and then generated a 2 × 2 min (4 × 4 km) grid over oceans and geoidal gravity using a high-resolution geoid, for example, Earth Gravitational Model 1996 (EGM96), over land (Majumdar et al. 2001; Hwang et al. 2002). Detailed quality evaluation of marine gravity (Hwang et al. 2002) over the Indian offshore has been discussed elsewhere (Chatterjee et al. 2007a,b). Root mean square errors (RMSEs) for various satellite-derived profiles with ship-borne gravity over the Arabian Sea and the Bay of Bengal have been found to be within ±3–6 mGal, which is quite satisfactory (Chatterjee et al. 2007b). The National Geophysical Research Institute (NGRI), Hyderabad, has taken several in situ gravimeter surveys over Kachchh and other places in Gujarat and generated an in situ gravity map over this region (NGRI Map Series 1978). The NGRI land gravity data grid size is approximately 0.5° × 0.5°. Recently, Gravity Recovery and Climate Experiment (GRACE) gravity data (grid size ~0.1° × 0.1°) have been generated over land which are utilized over the Kachchh region (GRACE website 1). Figure 4.7a–c shows various gravity images over the Kachchh region after superimposition with the tectonic map.
The Art of In-Memory Computing for Big Data Processing
Published in Kuan-Ching Li, Hai Jiang, Albert Y. Zomaya, Big Data Management and Processing, 2017
Mihaela-Andreea Vasile, Florin Pop
In-memory streaming support addresses a broad variety of big data applications that cannot be efficiently executed by traditional systems. It allows to process infinite streams of data that may arrive at different rates (even millions of events per second) in scalable and fault-tolerant fashion. It relies on the data grid for data locality. The streams are partitioned between the nodes and processed in sliding windows. Continuous queries may be registered for the changing data. It integrates with multiple solutions that acquire streams of data and consumes the streams into the Ignite cache: Apache Flume Sink, Apache Kafka, or Apache Camel Streamer.
Intuition in Decision Making – An Investigation in the Delivery Room
Published in Frédéric Adam, Dorota Kuchta, Stanisław Stanek, Frédéric Adam, Joanna Iwko, Gaye Kiely, Dorota Kuchta, Ewa Marchwicka, Stephen McCarthy, Gloria Phillips-Wren, Stanisław Stanek, Tadeusz Trzaskalik, Irem Ucal Sari, Rational Decisions in Organisations, 2022
Frédéric Adam, Eugene Dempsey, Brian Walsh, Mmoloki Kenosi
Analysis was undertaken by the co-authors based on key elements of diagnosis in part derived from literature and in part identified through observation of the actual decision-making process. These elements were implemented in a new framework to reflect the decision-making conditions in the delivery room, as illustrated below. The framework was used to systematically analyse the facts of the case and to plan for the analysis of further cases in a way that guarantees rigorous replication of the analysis across cases. A data grid was developed based on the framework in which the data from each case were coded.
Trends in data replication strategies: a survey
Published in International Journal of Parallel, Emergent and Distributed Systems, 2019
Stavros Souravlas, Angelo Sifaleras
Several applications are moving towards a distributed interconnected environment. A Data Grid is such an environment [1–5], where the data storage and all computational resources are distributed throughout different and widespread locations.