Enhancing Job Scheduling of an Atmospheric Intensive Data Application

Nowadays, e-Science applications involve great deal of data to have more accurate analysis. One of its application domains is the Radio Occultation which manages satellite data. Grid Processing Management is a physical infrastructure geographically distributed based on Grid Computing, that is implemented for the overall processing Radio Occultation analysis. After a brief description of algorithms adopted to characterize atmospheric profiles, the paper presents an improvement of job scheduling in order to decrease processing time and optimize resource utilization. Extension of grid computing capacity is implemented by virtual machines in existing physical Grid in order to satisfy temporary job requests. Also scheduling plays an important role in the infrastructure that is handled by a couple of schedulers which are developed to manage data automaticall

Data intensive scientific analysis with grid computing

At the end of September 2009, a new Italian GPS receiver for radio occultation was launched from the Satish Dhawan Space Center (Sriharikota, India) on the Indian Remote Sensing OCEANSAT-2 satellite. The Italian Space Agency has established a set of Italian universities and research centers to implement the overall processing radio occultation chain. After a brief description of the adopted algorithms, which can be used to characterize the temperature, pressure and humidity, the contribution will focus on a method for automatic processing these data, based on the use of a distributed architecture. This paper aims at being a possible application of grid computing for scientific research

Index to Library Trends Volume 38

published or submitted for publicatio

Meeting the Challenges of Met Data with Mysql

The objective of this project is to develop a relational database housing meteorological data used in direct support of emergency response activities for an atmospheric consulting firm, hereon referred to by the pseudonym WindInc. The intent is to demonstrate to WindInc how a relational database system would be superior to their present flat-file approach by providing the flexibility, performance, and reliability needed to meet their ever-increasing business demands, while simultaneously boosting the performance of their atmospheric dispersion modeling system

Freshwater ecosystem services in mining regions : modelling options for policy development support

The ecosystem services (ES) approach offers an integrated perspective of social-ecological systems, suitable for holistic assessments of mining impacts. Yet for ES models to be policy-relevant, methodological consensus in mining contexts is needed. We review articles assessing ES in mining areas focusing on freshwater components and policy support potential. Twenty-six articles were analysed concerning (i) methodological complexity (data types, number of parameters, processes and ecosystem-human integration level) and (ii) potential applicability for policy development (communication of uncertainties, scenario simulation, stakeholder participation and management recommendations). Articles illustrate mining impacts on ES through valuation exercises mostly. However, the lack of ground-and surface-water measurements, as well as insufficient representation of the connectivity among soil, water and humans, leave room for improvements. Inclusion of mining-specific environmental stressors models, increasing resolution of topographies, determination of baseline ES patterns and inclusion of multi-stakeholder perspectives are advantageous for policy support. We argue that achieving more holistic assessments exhorts practitioners to aim for high social-ecological connectivity using mechanistic models where possible and using inductive methods only where necessary. Due to data constraints, cause-effect networks might be the most feasible and best solution. Thus, a policy-oriented framework is proposed, in which data science is directed to environmental modelling for analysis of mining impacts on water ES

A Hypothetical Database-Driven Web-Based Meteorological Weather Station with Dynamic Datalogger System

This paper proposes the development and implementation strategies of a hypothetical database-driven web-based meteorological (meteo) weather station with a dynamic datalogger system to provide up-to-the-minute real-time ground-based weather information online to any interested client. The meteo weather station will provide weather data/information for eight parameters namely: relative humidity; solar radiance; wind speed; wind direction; barometric pressure; temperature; nefobasimeter for monitoring and measuring cloud height, thickness and number of layers detection; and rainfall. The hypothetical design present techniques that can be used to capture and log meteo data in a dynamic relational database management system (DRDBMS) and implements a TCP/IP network server. The meteo weather information will be collected from sensors incorporated into measuring instruments and transmitted via 1–Wire network and stored in the TINI’s non–volatile static random access memory (NV-SRAM). The TINI processes and uploads the information over a TCP/IP network via a switch, router, common gateway interface (CGI), very small aperture terminal (VSAT) via an Internet service provider (ISP) to the Internet for any interested user in the world. More so, although the acquired data will be made available as they are being logged to both the Internet and the database (DRDBMS) but the data will be automatically deleted every 24 hours at 00:00G MT from the TINI’s NV-SRAM to free the memory for the next day data at the same 00:00G MT. As a result of this, the DRDBMS which is an object for data/information storage using the MySQL stores the meteo data/information which can be retrieved by any interested client on request. However, the request will be made possible via the use of web pages, where the each meteo data/information will be displayed and accessed using special user login codes (username and password) upon subscription. Keywords: DBMS, Dynamic datalogger, embedded systems, html, hypothetical weather station, JavaScript, meteorology, MySQL, PHP, TCP/IP protocol, Tiny INternet interface (TINI), WAMP server, web development

Enhancing Job Scheduling of an Atmospheric Intensive Data Application

Nowadays, e-Science applications involve great deal of data to have more accurate analysis. One of its application domains is the Radio Occultation which manages satellite data. Grid Processing Management is a physical infrastructure geographically distributed based on Grid Computing, that is implemented for the overall processing Radio Occultation analysis. After a brief description of algorithms adopted to characterize atmospheric profiles, the paper presents an improvement of job scheduling in order to decrease processing time and optimize resource utilization. Extension of grid computing capacity is implemented by virtual machines in existing physical Grid in order to satisfy temporary job requests. Also scheduling plays an important role in the infrastructure that is handled by a couple of schedulers which are developed to manage data automatically

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone