Research on Materialized View Selection

定义了数据仓库领域的视图选择问题,并讨论了与该问题相关的代价模型、收益函数、代价计算、约束条件和视图索引等内容;介绍了3大类视图选择方法,即静态方法、动态方法和混合方法,以及各类方法的代表性研究成果;最后展望未来的研究方向.Definition of view selection issue in the field of data warehouses is presented, followed by the discussion of related problems, such as cost model, benefit function, cost computation, restriction condition, view index, etc. Then three categories of view selection methods, namely, static, dynamic and hybrid methods are discussed. For each method, some representative work is introduced. Finally some future trends in this area are discussed.Supported by the National Natural Science Foundation of China under Grant No.60473051 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant Nos.2007AA01Z191, 2006AA01Z230 (国家高技术研究发展计划(863)

Change Data Capture in Real-Time Active Data Warehouses: A Survey

本文是在北京大学数据库实验室攻读博士学位期间发表的。实时主动数据仓库是数据仓库的最新发展阶段和未来发展趋势,它为企业提供了对战略决策和战术决策的双重支持.实时主动数据仓库中包含两类数据,即实时数据和非实时数据,相应地,需要两种不同类型的变化数据捕捉方法,即支持实时变化数据捕捉的方法和普通的(不支持实时的)变化数据捕捉方法.结合在该领域的研究经验,对实时主动数据仓库中可以使用的多种变化数据捕捉方法进行了系统地论述,并比较各种方法的应用条件、优点、缺点和适用场合。Real-time active data warehouse is the most recent stage in the evolution history of data warehouses．It supports both strategic decision and tactic decision，which will bring great benefits to organizations．There are two types of data existing in real-time active data warehouses，i．e．，real-time data and non-real-time data．Accordingly，change data capture methods are classified into tWO kinds，including those supporting real-time change data capture and those not supporting real-time change data capture．Based on extensive research work in this field，those change data capture methods are systematically discussed，which may meet the requirements in real-time active data warehouses．国家自然科学基金项目(60473015)；国家“863”高技术研究发展计划基金项目(2006AAl2Z217)；HP中国实验室联合项

Research on Requirement-based Real-time Data Integration in Real-time Active Data Warehouses

本文是在北京大学数据库实验室攻读博士学位期间发表的。实时数据集成是实时主动数据仓库研究领域的一个重要问题。现有的研究成果都是从技术角度出发，而并没有考虑具体的商务应用需求。而在大型商务应用中，即使采用过滤规则只捕捉感兴趣的变化数据，也会产生大量的数据集成工作，从而导致不必要的沉重系统开销，同时还很有可能出现系统响应缓慢和用户需求无法得到满足等情况。本文从应用角度出发，提出了实时主动数据仓库中面向需求的实时数据集成方法，包括被频繁请求的数据的实时集成、满足突发请求的实时数据集成和由用户决定的实时数据集成。针对不同的商务需求，采用不同的数据集成策略，可以很好地满足不同类型的应用需求。Real-time data integration is a very important aspect in the field of real-time active data warehouse. Almost all the available research work now is from a technological point of view instead of an application angle. While in the real-world business application, a large amount of real-time data integration needs to be done even with the help of change data capture technology to integrate only the interesting part of the data from the data source, which will usually lead to the deteriorated system performance and fail to satisfy the business requirement in some cases. From an application angle, we here propose three requirement-based real-time data integration methods, including: real-time integration for the frequently requested data, real-time integration for the suddenly arising requirement and user-decided real-time integration. By adopting the appropriate method for the specific application occasion, we can better satisfy the various business requirements.国家自然科学基金项目(60473015)；国家“863”高技术研究发展计划基金项目(2006AAl2Z217)；HP中国实验室联合项

Materialized Views Selection of Multi-Dimensional Data in Real-Time Active Data Warehouses

通过基于主动决策引擎日志的数据挖掘来找到分析规则的CUBE 使用模式,从而为多维数据实视图选择算法提供重要依据;在此基础上设计了3A 概率模型,并给出考虑CUBE 受访概率分布的视图选择贪婪算法PGreedy(probability greedy), 以及结合视图挽留原则的视图动态调整算法. 实验结果表明, 在实时主动数据仓库环境下,PGreedy 算法比BPUS(benefit per unit space)算法具有更好的性能. In this paper, data mining based on the log of active decision engine is introduced to find the CUBE using pattern of analysis rules, which can be used as important reference information for materialized views selection. Based on it, a 3A probability model is designed, and the greedy algorithm, called PGreedy (probability greedy), is proposed, which takes into account the probability distribution of CUBE. Also view keeping rule is adopted to achieve better performance for dynamic view adjusting. Experimental results show that PGreedy algorithm can achieve better performance than BPUS (benefit per unit space) algorithm in real-time active data warehouses environment.Supported by the National Natural Science Foundation of China under Grant No.60473051 (国家自然科学基金); the China HP Co. and Peking University Joint Project (北京大学-惠普(中国)合作项目

Dealing with Query Contention Issue in Real-time Data Warehouses by Dynamic Multi-level Caches

The issue of query contention and scalability is the most difcult issue facing organizations deploying real-time data warehouse s olutions. The contention between complex se-lects and continuous inserts tends to severely limit the scal-ability of the data warehouses. I n this paper, we present a new method called dynamic multi-level caches, to effec-tively deal with the problem of query contention and scal-ability in real-time data warehouses. We differentiate be-tween queries with various data freshness requirements, and use multi-level caches to satisfy these different require-ments. Every query arriving at the system will be automat-ically redirected to the corresponding cache to access the required data, which means that the query loads are dis-tributed to multi-level caches instead of becoming blocked in the only one cache due to the contention between query and update operations. Extensive experiments on s everal real datasets s how that our method can effectively balance the query loads among multi-level caches and achieve desirable system performance

User-oriented Materialized View Selection

The problem of materialized view selection has been long researched, and many approaches have been proposed to deal with this issue. However, all the methods proposed to date strive toward improving the overall query performance, instead of being user-oriented. In this paper, we propose a new user-oriented method, called SOMES (uSerOriented Materialized viEw Selection), aiming at achieving better performance for view selection problem. SOMES takes into account query characteristics of different users, in which, users are classified into different groups according to their query characteristics, and various user groups are provided with their own windows, user view windows containing the views involved in their own query process. Experimental results show that our method can achieve desirable performance improvements over other methods such as BPUS and FPUS

中国物理海洋学研究70年：发展历程、学术成就概览

本文概略评述新中国成立70年来物理海洋学各分支研究领域的发展历程和若干学术成就。中国物理海洋学研究起步于海浪、潮汐、近海环流与水团,以及以风暴潮为主的海洋气象灾害的研究。随着国力的增强,研究领域不断拓展,涌现了大量具有广泛影响力的研究成果,其中包括:提出了被国际广泛采用的"普遍风浪谱"和"涌浪谱",发展了第三代海浪数值模式;提出了"准调和分析方法"和"潮汐潮流永久预报"等潮汐潮流的分析和预报方法;发现并命名了"棉兰老潜流",揭示了东海黑潮的多核结构及其多尺度变异机理等,系统描述了太平洋西边界流系;提出了印度尼西亚贯穿流的南海分支(或称南海贯穿流);不断完善了中国近海陆架环流系统,在南海环流、黑潮及其分支、台湾暖流、闽浙沿岸流、黄海冷水团环流、黄海暖流、渤海环流,以及陆架波方面均取得了深刻的认识;从大气桥和海洋桥两个方面对太平洋–印度洋–大西洋洋际相互作用进行了系统的总结;发展了浅海水团的研究方法,基本摸清了中国近海水团的分布和消长特征与机制,在大洋和极地水团分布及运动研究方面也做出了重要贡献;阐明了南海中尺度涡的宏观特征和生成机制,揭示了中尺度涡的三维结构,定量评估了其全球物质与能量输运能力;基本摸清了中国近海海洋锋的空间分布和季节变化特征,提出了地形、正压不稳定和斜压不稳定等锋面动力学机制;构建了"南海内波潜标观测网",实现了对内波生成–演变–消亡全过程机理的系统认识;发展了湍流的剪切不稳定理论,提出了海流"边缘不稳定"的概念,开发了海洋湍流模式,提出了湍流混合参数化的新方法等;在海洋内部混合机制和能量来源方面取得了新的认识,并阐述了混合对海洋深层环流、营养物质输运等过程的影响;研发了全球浪–潮–流耦合模式,推出一系列海洋与气候模式;发展了可同化主要海洋观测数据的海洋数据同化系统和用于ENSO预报的耦合同化系统;建立了达到国际水准的非地转(水槽/水池)和地转(旋转平台)物理模型实验平台;发展了ENSO预报的误差分析方法,建立了海洋和气候系统年代际变化的理论体系,揭示了中深层海洋对全球气候变化的响应;初步建成了中国近海海洋观测网;持续开展南北极调查研究;建立了台风、风暴潮、巨浪和海啸的业务化预报系统,为中国气象减灾提供保障;突破了国外的海洋技术封锁,研发了万米水深的深水水听器和海洋光学特性系列测量仪器;建立了溢油、危险化学品漂移扩散等预测模型,为伴随海洋资源开发所带来的风险事故的应急处理和预警预报提供科学支撑。文中引用的大量学术成果文献(每位第一作者优选不超过3篇)显示,经过70年的发展,中国物理海洋学研究培养了一支实力雄厚的科研队伍,这是最宝贵的成果。这支队伍必将成为中国物理海洋学研究攀登新高峰的主力军