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31#
發(fā)表于 2025-3-27 00:18:58 | 只看該作者
Open Standards for Service-Based Database Access and Integrationtegration standards for distributed environments. These standards provide a set of uniform web service-based interfaces for data access. A core specification, WS-DAI, exposes and, in part, manages data resources exposed by DAIS-based services. The WS-DAI document defines a core set of access pattern
32#
發(fā)表于 2025-3-27 04:00:54 | 只看該作者
33#
發(fā)表于 2025-3-27 08:53:23 | 只看該作者
Distributed Data Management with OGSA–DAIdistributed data (e.g. relational, XML, files and RDF triples). It does this by executing workflows that can encapsulate complex distributed data management scenarios in which data from one or more sources can be accessed, updated, combined and transformed. Moreover, the data processing capabilities
34#
發(fā)表于 2025-3-27 10:24:19 | 只看該作者
The DASCOSA-DB Grid Database System requirements for efficient query processing. To meet these requirements, we have developed the DASCOSA-DB distributed database system. In this chapter, a detailed overview of the architecture and implementation of DASCOSA-DB is given, as well as a description of novel features developed to better s
35#
發(fā)表于 2025-3-27 16:57:39 | 只看該作者
Access Control and Trustiness for Resource Management in Cloud Databasesabase. There are numerous service providers such as feeders, owners, and creators, who are less likely the same agent. Consequently, resources in a cloud database cannot be securely managed by traditional access control models, and therefore cloud database services may be trustless. This chapter pro
36#
發(fā)表于 2025-3-27 19:08:55 | 只看該作者
37#
發(fā)表于 2025-3-28 01:28:09 | 只看該作者
38#
發(fā)表于 2025-3-28 02:15:38 | 只看該作者
Scientific Computation and Data Management Using Microsoft Windows Azure to clouds. With the use of a case study, Microsoft Windows Azure has been applied to Space Situational Awareness (SSA) creating a system that is robust and scalable, demonstrating how to harness the capabilities of cloud computing. The generic aspects of cloud computing are discussed throughout.
39#
發(fā)表于 2025-3-28 10:07:13 | 只看該作者
40#
發(fā)表于 2025-3-28 13:38:03 | 只看該作者
Provenance Support for Data-Intensive Scientific Workflowssmall to large numbers. The traditional way of logging experimental process is no longer valid. This has resulted in a need for techniques to automatically collect information on workflows known as provenance. Several solutions for e-Science provenance have been proposed but these are predominantly
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