Evolution of a data network

A star network centred around ICL equipment was built in 1972 at Plessey, UK. The development of the network from a system providing dial-up access for 37 terminals, to one supporting over 300 is described. The network has progressed from the use of BPO-supplied modems, to higher-speed modems and time-division multiplexers, and further to statistical multiplexers. The growth of transaction processing and online user data entry facilities have meant the introduction of multichannel modems. Network integrity, diagnostics and fault-finding features are discussed. The next major development stage will be the provision of a routing/switching capability for all connected terminal devices.     

Temporal data management

A wide range of database applications manage time-varying information. Existing database technology currently provides little support for managing such data. The research area of temporal databases has made important contributions in characterizing the semantics of such information and in providing expressive and efficient means to model, store, and query temporal data. This paper introduces the reader to temporal data management, surveys stale-of-the-art solutions to challenging aspects of temporal data management, and points to research directions     

云数据管理的新数据模型

为解决云计算中海量数据的存储管理问题,分析了关系数据模型和NoSQL数据模型各自的特点,提出了一种新的数据模型。该模型根据数据本身的特点将数据横向切分为一组实体的集合,不同的数据实体负责处理不同的数据应用,结合了关系数据模型的可用性与NoSQL数据模型的可扩展性。通过详细定义该模型的数据结构、约束条件以及数据操作,保证了数据模型的完整性。通过一个原型系统运行实例,验证了该模型的有效性,为云数据管理提供了可行的解决途径。     

Crowdsourcing for data management

The discovery of informative itemsets is a fundamental building block in data analytics and information retrieval. While the problem has been widely studied, only few solutions scale. This is particularly the case when (1) the data set is massive, calling for large-scale distribution, and/or (2) the length k of the informative itemset to be discovered is high. In this paper, we address the problem of parallel mining of maximally informative k-itemsets (miki) based on joint entropy. We propose PHIKS (Parallel Highly Informative \(\underline{K}\)-ItemSet), a highly scalable, parallel miki mining algorithm. PHIKS renders the mining process of large-scale databases (up to terabytes of data) succinct and effective. Its mining process is made up of only two efficient parallel jobs. With PHIKS, we provide a set of significant optimizations for calculating the joint entropies of miki having different sizes, which drastically reduces the execution time, the communication cost and the energy consumption, in a distributed computational platform. PHIKS has been extensively evaluated using massive real-world data sets. Our experimental results confirm the effectiveness of our proposal by the significant scale-up obtained with high itemsets length and over very large databases.     

NoSQL data management systems

In the last decade, a new class of data management systems collectively called NoSQL systems emerged and are now intensively developed. The main feature of these systems is that they abandon the relational data model and the SQL, do not fully support ACID transactions, and use distributed architecture (even though there are non-distributed NoSQL systems as well). As a result, such systems outperform the conventional SQL-oriented DBMSs in some applications; in addition, such systems are highly scalable under increasing workloads and huge amounts of data, which is important, in particular, for Web applications. Unfortunately, the absence of transactional semantics imposes certain constraints on the class of applications where NoSQL systems can be effectively used and the choice of a particular system significantly depends on the application. In this paper, a review of the main classes of NoSQL data management systems is given and examples of systems and applications where they can be used are discussed.     

Active management of scientific data

Sophisticated data-distribution schemes and recent developments in sensors and instruments that can monitor the lower kilometers of the atmosphere at high levels of resolution have rapidly expanded the quantity of information available to mesoscale meteorology. The myLEAD personalized information-management tool helps geoscience users make sense of this vastly expanded information space. MyLEAD extends the general globus metadata catalog service and leverages a well-known general and extensible schema. Its orientation makes it an active player in large-scale distributed computation environments characterized by interacting grid and Web services.     

基于数据仓库的JMS中数据管理方法的研究

在分析JMS数据管理的目标的基础上,重点研究了系统数据和用户数据的管理模型.对系统数据,重点研究了集群配置数据和日志数据的管理方法,提出使用OGSA的分布式消息结构来管理JMS中的分布式日志.对用户数据,研究了作业和作业网络及其实例的数据管理以及用户数据传输方法,提出了使用数据仓库来分析作业执行历史,设计了JMS的数据仓库模式,该模式能满足用户的多种查询和分析要求.有效地解决了JMS中的数据存储、查询、分析和共享等问题.     

Automation of data normalization for implementing master data management systems

Data normalization is a laborious and costly process taking place in master data management soft-ware development in enterprises. We analyze the subtasks of the normalization and propose an approach to automating the most laborious of these subtasks. Also, we describe a software system implementing the proposed approach and automatically learning the expert skills.     

Model-driven coordinated management of data centers

Management of computing infrastructure in data centers is an important and challenging problem, that needs to: (i) ensure availability of services conforming to the Service Level Agreements (SLAs); and (ii) reduce the Power Usage Effectiveness (PUE), i.e. the ratio of total power, up to half of which is attributed to data center cooling, over the computing power to service the workloads. The cooling energy consumption can be reduced by allowing higher-than-usual thermostat set temperatures while maintaining the ambient temperature in the data center room within manufacturer-specified server redline temperatures for their reliable operations. This paper proposes: (i) a Coordinated Job, Power, and Cooling Management (JPCM) policy, which performs: (a) job management so as to allow for an increase in the thermostat setting of the cooling unit while meeting the SLA requirements, (b) power management to reduce the produced thermal load, and (c) cooling management to dynamically adjust the thermostat setting; and (ii) a Model-driven coordinated Management Architecture (MMA), which uses a state-based model to dynamically decide the correct management policy to handle events, such as new workload arrival or failure of a cooling unit, that can trigger an increase in the ambient temperature. Each event is associated with a time window, referred to as the window-of-opportunity, after which the temperature at the inlet of one or more servers can go beyond the redline temperature if proper management policies are not enforced.This window-of-opportunity monotonically decreases with increase in the incoming workload. The selection of the management policy depends on their potential energy benefits and the conformance of the delays in their actuation to the window-of-opportunity. Simulations based on actual job traces from the ASU HPC data center show that the JPCM can achieve up to 18% energy-savings over separated power or job management policies. However, high delay to reach a stable ambient temperature (in case of cooling management through dynamic thermostat setting) can violate the server redline temperatures. A management decision chart is developed as part of MMA to autonomically employ the management policy with maximum energy-savings without violating the window-of-opportunity, and hence the redline temperatures. Further, a prototype of the JPCM is developed by configuring the widely used Moab cluster manager to dynamically change the server priorities for job assignment.     

基于区块链的数据安全管理系统

数据作为21世纪最重要的资源财富,能为人们创造巨大的商业价值,而数据共享能使收益最大化.针对数据共享存在的隐私泄露、第三方信任危机、数据确权等问题,提出一种基于区块链场景的数据安全管理系统.利用区块链共识协议实现去中心化的数据共享,隐私安全是数据共享的关键,采用签密和基于LWE的BGN加密方案解决隐私泄露问题,采用区块...     

TDM:工程数据管理平台

工程试验产生的数据组织分散、模式复杂而多变,从而要求工程数据管理系统能够统一管理数据,并具有良好的用户定义特征和数据扩展能力.给出了一个工程数据管理平台TDM,综述了TDM的主要功能和关键技术(元数据驱动的软件架构、面向对象的数据库模型、虚拟数据库技术),阐述了试验数据管理的几个重要问题.在此基础上提出了工程试验数据管理的框架和以后可能研究的方向.