中文簡介

分布式并行數(shù)據(jù)庫技術(shù)一直是國內(nèi)外研究和開發(fā)的熱點。利用這一技術(shù)的許多實際應(yīng)用和商業(yè)產(chǎn)品也存在。自1990年代中期以來，基于網(wǎng)絡(luò)的信息管理使用分布式和/或并行數(shù)據(jù)管理來取代集中管理的數(shù)據(jù)管理。這一領(lǐng)域的成熟，以及基礎(chǔ)技術(shù)的變化所引起的新問題，要求這一領(lǐng)域的工作有一個中心重點。分布式和并行數(shù)據(jù)庫為介紹和傳播新的研究成果、系統(tǒng)開發(fā)工作以及用戶在分布式和并行數(shù)據(jù)庫系統(tǒng)中的經(jīng)驗提供了這樣一個重點。分布式和并行數(shù)據(jù)庫在數(shù)據(jù)庫研究的所有傳統(tǒng)和大多數(shù)新興領(lǐng)域發(fā)表論文，包括：數(shù)據(jù)集成、數(shù)據(jù)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(yōu)化；大型數(shù)據(jù)集的分析、挖掘和可視化、存儲、數(shù)據(jù)碎片化、放置和分配、復(fù)制協(xié)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(tǒng)和并行系統(tǒng)背景下的一系列問題包括：用于管理數(shù)據(jù)和進程的移動、服務(wù)、P2P、網(wǎng)格和云計算、管理分布式系統(tǒng)的異質(zhì)性和自主性、語義互操作性和集成(匹配、映射)、連接數(shù)據(jù)、開放數(shù)據(jù)、移動數(shù)據(jù)、流數(shù)據(jù)、傳感器數(shù)據(jù)、多媒體和多式聯(lián)運數(shù)據(jù)、元數(shù)據(jù)、知識庫、本體、網(wǎng)絡(luò)規(guī)模數(shù)據(jù)管理、關(guān)系、面向?qū)ο?、XML、圖形、RDF、事件數(shù)據(jù)管理、支持組/協(xié)作工作；支持非傳統(tǒng)應(yīng)用(例如用于數(shù)據(jù)處理的軟計算、利用各種數(shù)據(jù)的翻譯醫(yī)學(xué))、與數(shù)據(jù)管理有關(guān)的替代軟件和硬件架構(gòu)、利用分布式和并行數(shù)據(jù)庫技術(shù)管理生物、地理、空間、時間、科學(xué)和統(tǒng)計數(shù)據(jù)、系統(tǒng)支持和數(shù)據(jù)管理接口問題。

投稿網(wǎng)址：http://www.springer.com/journal/10619/submission

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.