The Clarens Web Service Framework for Distributed Scientific Analysis in Grid Projects

Large scientific collaborations are moving towards service oriented architecutres for implementation and deployment of globally distributed systems. Clarens is a high performance, easy to deploy Web Service framework that supports the construction of such globally distributed systems. This paper discusses some of the core functionality of Clarens that the authors believe is important for building distributed systems based on Web Services that support scientific analysis

A performance measure of page mode dram as a second level cache in microprocessors

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.Includes bibliographical references (leaf 151).by David R. Shoemaker.M.S

Interactive Feature Selection and Visualization for Large Observational Data

Data can create enormous values in both scientific and industrial fields, especially for access to new knowledge and inspiration of innovation. As the massive increases in computing power, data storage capacity, as well as capability of data generation and collection, the scientific research communities are confronting with a transformation of exploiting the advanced uses of the large-scale, complex, and high-resolution data sets in situation awareness and decision-making projects. To comprehensively analyze the big data problems requires the analyses aiming at various aspects which involves of effective selections of static and time-varying feature patterns that fulfills the interests of domain users. To fully utilize the benefits of the ever-growing size of data and computing power in real applications, we proposed a general feature analysis pipeline and an integrated system that is general, scalable, and reliable for interactive feature selection and visualization of large observational data for situation awareness. The great challenge tackled in this dissertation was about how to effectively identify and select meaningful features in a complex feature space. Our research efforts mainly included three aspects: 1. Enable domain users to better define their interests of analysis; 2. Accelerate the process of feature selection; 3. Comprehensively present the intermediate and final analysis results in a visualized way. For static feature selection, we developed a series of quantitative metrics that related the user interest with the spatio-temporal characteristics of features. For timevarying feature selection, we proposed the concept of generalized feature set and used a generalized time-varying feature to describe the selection interest. Additionally, we provided a scalable system framework that manages both data processing and interactive visualization, and effectively exploits the computation and analysis resources. The methods and the system design together actualized interactive feature selections from two representative large observational data sets with large spatial and temporal resolutions respectively. The final results supported the endeavors in applications of big data analysis regarding combining the statistical methods with high performance computing techniques to visualize real events interactively

DataStations: ubiquitous transient storage for mobile users

technical reportIn this paper, we describe DataStations, an architecture that provides ubiquitous transient storage to arbitrary mobile applications. Mobile users can utilize a nearby DataStation as a proxy cache for their remote home file servers, as a file server to meet transient storage needs, and as a platform to share data and collaborate with other users over the wide area. A user can roam among DataStations, creating, updating and sharing files via a native file interface using a uniform file name space throughout. Our architecture provides transparent migration of file ownership and responsibility among DataStations and a user?s home file server. This design not only ensures file permanence, but also allows DataStations to reclaim their resources autonomously, allowing the system to incrementally scale to a large number of DataStations and users. The unique aspects of our DataStation design are its decentralized but uniform name space, its locality-aware peer replication mechanism, and its highly flexible consistency framework that lets users select the appropriate consistency mechanism on a per-file replica basis. Our evaluation demonstrates that DataStations can support low-latency access to remote files as well as ad-hoc data sharing and collaboration by mobile users, without compromising consistency or data safety