中文简介
分布式并行数据库技术一直是国内外研究和开发的热点。利用这一技术的许多实际应用和商业产品也存在。自1990年代中期以来，基于网络的信息管理使用分布式和/或并行数据管理来取代集中管理的数据管理。这一领域的成熟，以及基础技术的变化所引起的新问题，要求这一领域的工作有一个中心重点。分布式和并行数据库为介绍和传播新的研究成果、系统开发工作以及用户在分布式和并行数据库系统中的经验提供了这样一个重点。分布式和并行数据库在数据库研究的所有传统和大多数新兴领域发表论文，包括：数据集成、数据共享、安全和隐私、交易管理、流程和工作流程管理、信息提取、查询处理和优化；大型数据集的分析、挖掘和可视化、存储、数据碎片化、放置和分配、复制协议、可靠性、容错、持久性、保存、性能和可伸缩性，以及各种通信和传播平台和中间件的使用。在分布式系统和并行系统背景下的一系列问题包括：用于管理数据和进程的移动、服务、P2P、网格和云计算、管理分布式系统的异质性和自主性、语义互操作性和集成(匹配、映射)、连接数据、开放数据、移动数据、流数据、传感器数据、多媒体和多式联运数据、元数据、知识库、本体、网络规模数据管理、关系、面向对象、XML、图形、RDF、事件数据管理、支持组/协作工作；支持非传统应用(例如用于数据处理的软计算、利用各种数据的翻译医学)、与数据管理有关的替代软件和硬件架构、利用分布式和并行数据库技术管理生物、地理、空间、时间、科学和统计数据、系统支持和数据管理接口问题。
英文简介
Distributed and parallel database technology has been the subject of intense research and development effort. Numerous practical application and commercial products that exploit this technology also exist. Since the mid-1990s, web-based information management has used distributed and/or parallel data management to replace their centralized cousins. The maturation of the field, together with the new issues that are raised by the changes in the underlying technology, requires a central focus for work in the area. Distributed and Parallel Databases provides such a focus for the presentation and dissemination of new research results, systems development efforts, and user experiences in distributed and parallel database systems.Distributed and Parallel Databases publishes papers in all the traditional as well as most emerging areas of database research, including: Data Integration, Data Sharing, Security and Privacy, Transaction Management, Process and Workflow Management, Information Extraction, Query Processing and Optimization, the Analysis, Mining and Visualization of large data sets, Storage, Data Fragmentation, Placement and Allocation, Replication Protocols, Reliability, Fault Tolerance, Persistence, Preservations, Performance and Scalability, and Use of various communication and dissemination platforms and middleware.Example sets of issues in the context of distributed and parallel systems include: Mobile, Service, P2P, grid and cloud computing for managing data and processes, Managing Heterogeneity and Autonomy in Distributed Systems, Semantic interoperability and integration (matching, mapping), Linked Data, Open Data, Mobile Data, Streaming Data, Sensor Data, Multimedia and Multimodal Data, Metadata, Knowledge Bases, Ontologies, Web scale data management, Relational, Object-Oriented, XML, Graph, RDF, Event data management, Supporting Group/Collaborative Work, Support for Non-Traditional Applications (e.g., Soft Computing applied to Data Processing, Translational medicine exploiting a variety of data), Alternative Software and Hardware Architectures Related to Data Management, The Use of Distributed and Parallel Database Technology in Managing Biological, Geographic, Spatial, Temporal, Scientific and Statistical Data, System Support and Interface Issues for Data Management.
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