中文簡介

分布式并行數據庫技術一直是國內外研究和開發的熱點。利用這一技術的許多實際應用和商業產品也存在。自1990年代中期以來，基于網絡的信息管理使用分布式和/或并行數據管理來取代集中管理的數據管理。這一領域的成熟，以及基礎技術的變化所引起的新問題，要求這一領域的工作有一個中心重點。分布式和并行數據庫為介紹和傳播新的研究成果、系統開發工作以及用戶在分布式和并行數據庫系統中的經驗提供了這樣一個重點。分布式和并行數據庫在數據庫研究的所有傳統和大多數新興領域發表論文，包括：數據集成、數據共享、安全和隱私、交易管理、流程和工作流程管理、信息提取、查詢處理和優化；大型數據集的分析、挖掘和可視化、存儲、數據碎片化、放置和分配、復制協議、可靠性、容錯、持久性、保存、性能和可伸縮性，以及各種通信和傳播平臺和中間件的使用。在分布式系統和并行系統背景下的一系列問題包括：用于管理數據和進程的移動、服務、P2P、網格和云計算、管理分布式系統的異質性和自主性、語義互操作性和集成(匹配、映射)、連接數據、開放數據、移動數據、流數據、傳感器數據、多媒體和多式聯運數據、元數據、知識庫、本體、網絡規模數據管理、關系、面向對象、XML、圖形、RDF、事件數據管理、支持組/協作工作；支持非傳統應用(例如用于數據處理的軟計算、利用各種數據的翻譯醫學)、與數據管理有關的替代軟件和硬件架構、利用分布式和并行數據庫技術管理生物、地理、空間、時間、科學和統計數據、系統支持和數據管理接口問題。

　　英文簡介

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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