аЯрЁБс>ўџ DFўџџџCџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџьЅСu@ №ПЪ.bjbjцц ">ŒŒЦ&џџџџџџˆ6666ЊЊЊО††††ЂО92ЪЪЪЪЪЉЉЉИКККККК$kRНfоЊ–ЅЉ––о66ЪЪлѓ```–p6RЪЊЪИ`–И`&`†:м ,ˆ"ЊdЪО жŒx3Ф† ”$ 09R#".#dОО6666#Њd0Љ"Ы`уїŸЉЉЉооОО„BDPООBBackground Each year across the United States, floods, tornadoes, hail, strong winds, lightning, and winter storms – so-called mesoscale weather events -- cause hundreds of deaths, routinely disrupt transportation and commerce, and result in annual economic losses greater than $13B. Although mitigating the impacts of such events would yield enormous economic and societal benefits, the ability to do so is stifled by rigid IT frameworks that cannot accommodate the real time, on-demand, and dynamically-adaptive needs of mesoscale weather research; its disparate, high volume data sets and streams; and the tremendous computational demands of its numerical models and data assimilation systems. In response to the need for a national cyberinfrastructure in mesoscale meteorology, we creating an integrated, scalable framework -- known as Linked Environments for Atmospheric Discovery (LEAD) -- for identifying, accessing, preparing, assimilating, predicting, managing, analyzing, mining, and visualizing a broad array of meteorological data and model output, independent of format and physical location. A transforming element of LEAD is dynamic workflow orchestration and data management, which allows the use of analysis tools, forecast models, and data repositories not in fixed configurations or as static recipients of data, as is now the case, but rather as dynamically adaptive, on-demand, Grid-enabled systems that can (a) change configuration rapidly and automatically in response to weather; (b) continually be steered by new data; (c) respond to decision-driven inputs from users; (d) initiate other processes automatically; and (e) steer remote observing technologies to optimize data collection for the problem at hand. Software Infrastructure LEAD research is focused on creating a series of interconnected, heterogeneous virtual IT “Grid environments” that are linked at several levels to enable data transport, service chaining, interoperability, and distributed computation. From the user perspective, these environments include a deployable system consisting of the LEAD Toolkit and “MyLEAD,” both within the Local User Environment. MyLEAD represents a virtual information space for controlling information flows, posting results for access by others, and managing interconnected processes.  The User Productivity Environment contains models, tools and algorithms for operating on data and other information available within the LEAD Data Cloud – a virtual space of existing servers that provides access to all types of geophysical data. The Data Services Environment will handle the complexities of data transport, formatting and interoperability using interchange technologies. Users will be able to download and process data locally using Productivity Environment tools, or if the data are too large, operate on them remotely, using the Distributed Technologies Environment to schedule resources and distribute work across the Grid. In this environment, the proposed Grid workflow infrastructure will autonomously compute scheduling constraints, dynamically acquire resources, recover from component errors and adapt to changing plans. A set of Linked Grid and Web Services Testbeds will maintain a rolling archive of several months of recent data (plus selected historical data); provide tools for operating on these data (e.g., mining engines, algorithms); and serve as a framework (i.e., a mini Grid) for developing distributed Web services capabilities. Computing Barriers Workflow Orchestration – the construction and scheduling of tasks data sources drawn from real-time sensor streams, simulations and data mining, by input from sensors, and by the monitored load on resources. Interaction and Control of Dynamically Adaptive Sensors – the protocols, command interfaces, and linkages with meteorological models and other tools needed for two-way adaptivity. Data Streaming – to support robust, high bandwidth transmission of multi-sensor data. LEAD’s dynamic and real-time functionality will require more flexible content-based subscription approaches to data stream management and control. Also, methods for asynchronous pull-based retrieval will be needed. Batching methods also might be useful for relaying large volumes of archival data. Several fundamental research problems exist in these areas. Both data formats and logical notions of time may differ, forcing reconciliation before flow can be established. Further, synchronization between data streams and models must be resilient to application restarts, sensor restarts and other malfunctions Distributed Monitoring and Performance Estimation – to enable soft real-time performance guarantees by estimating resource behavior. To identify resource classes capable of satisfying performance expectations, and to verify that the resources behave as expected during ensemble execution, we must develop an extended set of Grid monitoring and performance estimation tools suitable for use with real time weather data streams, Grid services and dynamic orchestrations. Leveraging the Illinois Autopilot Grid toolkit [26], these tools will accept. Because the Grid is built from a wide variety of geographically distributed, network-connected components, it has the potential to be unreliable. When computations are launched and dispersed across the Grid, some may fail due to failed network links or because shared resources were reclaimed for other uses. To increase the probability of success, we are developing a set of data stream bandwidth and resource reliability forecasting tools that monitor resource availability and predict the likelihood of successful execution or data stream connection and sustained bandwidth on a given resource set. Finally, as a complement, we will develop a set of performability models that can be used to estimate the number of redundant copies of application components that must be launched to ensure timely completion of a given experiment. These models exploit knowledge of the task structure of each application, as well as reliability and execution time estimates, to redundantly schedule selected tasks and ensure completion of the aggregate task graph. Data Management – in support of the storage and cataloging of observational data, model output and results from data mining. The three cornerstones of data movement and management are data dissemination, data storage, and user view maintenance. LEAD must consider data storage, cataloguing, archival of processes, metadata, and ephemeral data. These products include experiment descriptions, data transformation processes, derived products from recent experimental runs, and intermediate files produced as part of a multi-phase process. Personalization (user view maintenance) of LEAD’s computing and data-intensive research environment is a key need of LEAD data management. The MyLEAD data space is envisioned as an environment, accessible from the LEAD portal that provides fast access to a user’s private data and to data of interest to any user. Where the scientific portal provides customized access to the computing capability of LEAD, MyLEAD provides personalized access for developing index support for fast and transparent access to personal data from heterogeneous sources including inverted indexes for hierarchically organized content. It also provides a multi-key index for augmented spatial data, that is, geospatial data augmented with time-based data such as storm developments. Data publication and privacy issues involving the metacatalog system is also a key research issue. Policies and mechanisms that preserve the privacy of data that might include encryption of selected data and access mechanisms that subsequently operate over the encrypted data. Data Mining – provides the tools that enable users to glean insights from data and model output. The LEAD data mining capabilities will be implemented as sets of federated mining components capable of operating on multiple platforms as independent services. This approach serves to provide the integration of data mining functionality into the multiple LEAD analysis environments as Grid and Web resources, extensions to GIS, and visualization tools. Methods will be developed to dynamically link federated services for analysis tasks. In addition, the mining components will interface with Grid middleware technologies so that mining tasks can be efficiently scheduled and distributed among the available processing resources. LEAD will employ genetic algorithms and simulated annealing-based optimization methods for feature selection and parameter tuning and will facilitate creation of ensemble classifiers. We are developing data integrity components to check for missing or invalid data values, incorrect formats, and other potential problems. We will include in the LEAD mining environment neural networks that use cross validation and fault detection to ensure integrity of the mining process. Semantic and Data Interchange Technologies – to enable use of heterogeneous data by diverse tools and applications. To allow orchestration of workflows involving both heterogeneous information from different data sources, and chains of disparate services, in a seamless, dynamic, automated fashion, LEAD will incorporate ontologies, interchange technologies, and other concepts being explored in the Semantic Web and Semantic Grid. By utilizing ESML as an interchange technology, many different data formats can coexist, and applications within LEAD can achieve the required interoperability. LEAD will augment the existing capabilities of ESML by providing machine-understandable semantics for the automated linking of information and services. 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Reed  ўџр…ŸђљOhЋ‘+'Гй0t˜ЄАШдр№  0 < HT\dlф1ss Bob WilhelmsonBobBobNormallDaniel A. Reed3niMicrosoft Word 10.0@д­є@№$фv3Ф@ ?x3ФЩ§ ўџеЭеœ.“—+,љЎ0ь hp€ˆ˜  ЈАИ Р Юф NCSAtlFГ&{ 1 Title ўџџџ!"#$%&'ўџџџ)*+,-./012ўџџџ456789:ўџџџ<=>?@ABўџџџ§џџџEўџџџўџџџўџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџRoot Entryџџџџџџџџ РF`œx3ФG€Data џџџџџџџџџџџџ 1Tableџџџџ(7WordDocumentџџџџ">SummaryInformation(џџџџџџџџџџџџ3DocumentSummaryInformation8џџџџџџџџ;CompObjџџџџџџџџџџџџjџџџџџџџџџџџџўџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџўџ џџџџ РFMicrosoft Word Document MSWordDocWord.Document.8є9Вq