Optimal Design of Object-Oriented Databases in Automatic Information-Control Systems. I. Analysis of Problem Domains

Design of object-oriented databases for automatic information control systems is studied. A formal method of analysis and design of optimal structures for object-oriented databases is developed on the basis of sequential interconnected formalizations of user problem domains, clustering of user information and functional requirements, design and analysis of object models for user requirements and object canonical structure of databases and design of optimal logical structures for object-oriented databases. Methods of analysis of problem domains of database users and construction of effective object canonical database structures in the form of classes, objects, and their relations and satisfying principles of abstraction, encapsulation, modularity, and hierarchy are described.     

Dealing with fuzziness in active mobile database systems

Current needs of industry required the development of advanced database models like active mobile database systems. An active mobile database system can be designed by incorporation of triggering rules into a mobile computing environment in which the users are able to access a collection of database services using mobile and non-mobile computers at any location. Fuzzy concepts are adapted to the field of databases in order to deal with ambiguous, uncertain data. Fuzziness comes into picture in active mobile databases especially with spatial queries on moving objects. Incorporating fuzziness into rules would also improve the effectiveness of active mobile databases as it provides much flexibility in defining rules for the supported application. In this paper we present some methods to adapt the concepts developed for fuzzy systems to active mobile databases.     

可扩展的云关系型数据库的研究

为解决用户使用目前的云数据库服务时他们必须对应用做大量的修改的这种问题,在分析和研究现有的云数据库服务的基础上,提出了一种新型的云关系型数据库平台,它支持多语言开发的SDK机制,同时对数据的垂直、水平分布机制和算法进行了描述.描述了云关系型数据库的可靠性,包括数据存储管理和各种保护机制.仿真性能测试结果表明,在大海量数据量下,该云关系型数据库具有很好的可靠性、可扩展性和易用性.     

View Definition and Generalization for Database Integration in a Multidatabase System

Access to a heterogeneous distributed collection of databases can be simplified by providing users with a logically integrated interface or global view. There are two aspects to database integration. Firstly, the local schemas may model objects and relationships differently and, secondly, the databases may contain mutually inconsistent data. This paper identifies several kinds of structural and data inconsistencies that might exist. It describes a versatile view definition facility for the functional data model and illustrates the use of this facility for resolving inconsistencies. In particular, the concept of generalization is extended to this model, and its importance to database integration is emphasized. The query modification algorithm for the relational model is extended to the semantically richer functional data model with generalization.     

基于人工智能方法的数据库智能诊断

数据库是一种非常重要和基础的计算机系统软件,随着数据库在各行各业的广泛应用,越来越多的人开始关注数据库运行的稳定性.由于各种各样内部或是外部作用的影响,数据库在实际运行的过程中会出现性能异常,而这可能会带来巨大的经济损失.人们大多通过观察监控指标信息来进行数据库异常诊断,但是关于数据库监控指标有数百个,普通的数据库使用者根本无法提取出有价值的信息.一些传统的公司会聘用专业的人员管理数据库,而这种成本会是很多公司难以接受的.因此,如何用较低的成本完成对数据库的自动监控和诊断是一个具有挑战性的问题.现有的OLTP数据库自动异常诊断方法往往存在着监控信息收集成本过高、适用范围小抑或是稳定性较差等问题.在这篇论文里我们提出了一种智能的数据库异常诊断框架AutoMonitor,提供了数据库异常监测、异常指标提取和根因分析这三个模块,这三个模块分别使用了基于LSTM的时间序列异常诊断模型、Kolmogorov-Smirnov检验、和优化的K近邻算法.整个框架分成离线训练和在线诊断这两个阶段.我们将提出的系统部署在PostgreSQL数据库,通过实验表明该框架对于异常诊断具有较高的精确程度,并且不会对系统性能造成太大的影响.     

水文预报业务非结构化数据库选型探索

如何更加高效地存储和处理海量非结构化数据,已成为水利行业信息化建设亟待解决的难题,水文预报业务作为水利行业的核心业务,选择合适的数据库是首要任务。对目前国产主流数据库进行梳理,将非结构化数据库分为数据库结合NAS、数据库大字段、多模数据库3种类型,针对每种类型,分析原理、特点、数据处理时效性、支持的数据量、数据查询效率等多个方面,对部分类型进行压力测试,获取量化性能指标,使选型者可以结合自身应用场景进行比较分析选择合适类型,并基于水文预报业务数据库选型经验,提出水利非结构化数据库选型建议。通过分析认为不同的业务需求适合不同的数据库类型,对于水文预报业务这类海量非结构化数据存储管理、且数据处理能力要求高的场景建议选用数据库大字段或多模数据库。     

强化计算机数据库管理技术的有益分析

伴随科技的进步,计算机在日常生产生活中的运用也日益广泛,丰富了人们的业余生活,为工作提供了便利。信息管理中应用计算机数据库可提高管理信息的现代性与科学性,提升管理信息的效率。计算机数据库在自身发展的同时为各用户提供了贴心的服务,但不可否认安全问题依然存在。强化管理技术,提高计算机数据库的各项性能,方可更好的为计算机用户服务。     

An Interface Agent Approach to Personalize Users' Interaction with Databases

Making queries to a database system through a computer application can become a repetitive and time-consuming task for those users who generally make similar queries to get the information they need to work with. We believe that interface agents could help these users by personalizing the query-making and information retrieval tasks. Interface agents are characterized by their ability to learn users' interests in a given domain and to help them by making suggestions or by executing tasks on their behalf. Having this purpose in mind we have developed an agent, named QueryGuesser, to assist users of computer applications in which retrieving information from a database is a key task. This agent observes a user's behavior while he is working with the database and builds the user's profile. Then, QueryGuesser uses this profile to suggest the execution of queries according to the user's habits and interests, and to provide the user information relevant to him by making time-demanding queries in advance or by monitoring the events and operations occurring in the database system. In this way, the interaction between database users and databases becomes personalized while it is enhanced.     

网站内容管理及个性化网页系统的研究与实现

针对现有内容管理系统在数据存储方面的问题,介绍了一个基于原生XML数据库和关系数据库的内容管理系统,这种方式充分利用了两种数据库的优势,提高了效率.同时应用数据挖掘技术,采用协同过滤算法,提供个性化网页服务,有助于赢得更多客户.     

商用数据库的统一管理设计

在计算机软件开发中,当多个相关产品都用到了商用数据库时,由于不同数据库驱动和不同数据库的配置,且不同数据库在操作语法上不尽相同,从数据库的选型到应用层接口可能都不统一。为解决这个问题,本文设计开发了统一商用数据库,根据不同的平台给出不同的实现,对外提供统一访问接口。通过实现对业务表的管理,不涉及具体业务,实现了跨系统使用,并且为避免大量的数据库连接时导致服务不堪负荷,影响访问效率,采用数据库连接池的方式实现数据库的连接库的连接访问,使用户可以专注于业务实现。     

Probabilistic databases for decision analysis

A probabilistic database is defined in a previous article [R. Cavallo and M. Pittarelli, Proc. 13th Int. Conf. on Very Large Databases (VLDB); 1987; see Ref. 9] as a collection of probability distributions over Cartesian products of finite variable domains. the concept is extended here to accommodate interval-valued probabilities. Algebraic operations for both real- and interval-valued probabilities databases analogous to those for relational databases are defined. Techniques for making inferences regarding joint distributions on subsets of the variables over which a probabilistic database is defined are developed. These are illustrated through application to a problem of decision analysis under partial uncertainty. Connections between the probabilistic database formalism and other forms of data representation are discussed.     

Pronto: High availability for standard off-the-shelf databases

Enterprise applications typically store their state in databases. If a database fails, the application is unavailable while the database recovers. Database recovery is time consuming because it involves replaying the persistent transaction log. To isolate end users from database failures we introduce Pronto, a protocol to orchestrate the transaction processing by multiple, standard databases so that they collectively implement the illusion of a single, highly available database. Pronto is a novel replication protocol that handles non-determinism without relying on perfect failure detection, does not require any modifications in existing applications and databases, and allows databases from different providers to be part of the replicated compound.     

A space efficient solution to the frequent string mining problem for many databases

The frequent string mining problem is to find all substrings of a collection of string databases which satisfy database specific minimum and maximum frequency constraints. Our contribution improves the existing linear-time algorithm for this problem in such a way that the peak memory consumption is a constant factor of the size of the largest database of strings. We show how the results for each database can be stored implicitly in space proportional to the size of the database, making it possible to traverse the results in lexicographical order. Furthermore, we present a linear-time algorithm which calculates the intersection of the results of different databases. This algorithm is based on an algorithm to merge two suffix arrays, and our modification allows us to also calculate the LCP table of the resulting suffix array during the merging.     

Detecting anomalous access patterns in relational databases

A considerable effort has been recently devoted to the development of Database Management Systems (DBMS) which guarantee high assurance and security. An important component of any strong security solution is represented by Intrusion Detection (ID) techniques, able to detect anomalous behavior of applications and users. To date, however, there have been few ID mechanisms proposed which are specifically tailored to function within the DBMS. In this paper, we propose such a mechanism. Our approach is based on mining SQL queries stored in database audit log files. The result of the mining process is used to form profiles that can model normal database access behavior and identify intruders. We consider two different scenarios while addressing the problem. In the first case, we assume that the database has a Role Based Access Control (RBAC) model in place. Under a RBAC system permissions are associated with roles, grouping several users, rather than with single users. Our ID system is able to determine role intruders, that is, individuals while holding a specific role, behave differently than expected. An important advantage of providing an ID technique specifically tailored to RBAC databases is that it can help in protecting against insider threats. Furthermore, the existence of roles makes our approach usable even for databases with large user population. In the second scenario, we assume that there are no roles associated with users of the database. In this case, we look directly at the behavior of the users. We employ clustering algorithms to form concise profiles representing normal user behavior. For detection, we either use these clustered profiles as the roles or employ outlier detection techniques to identify behavior that deviates from the profiles. Our preliminary experimental evaluation on both real and synthetic database traces shows that our methods work well in practical situations. This material is based upon work supported by the National Science Foundation under Grant No. 0430274 and the sponsors of CERIAS.     

An approach to mining the multi-relational imbalanced database

The class imbalance problem is an important issue in classification of Data mining. For example, in the applications of fraudulent telephone calls, telecommunications management, and rare diagnoses, users would be more interested in the minority than the majority. Although there are many proposed algorithms to solve the imbalanced problem, they are unsuitable to be directly applied on a multi-relational database. Nevertheless, many data nowadays such as financial transactions and medical anamneses are stored in a multi-relational database rather than a single data sheet. On the other hand, the widely used multi-relational classification approaches, such as TILDE, FOIL and CrossMine, are insensitive to handle the imbalanced databases. In this paper, we propose a multi-relational g-mean decision tree algorithm to solve the imbalanced problem in a multi-relational database. As shown in our experiments, our approach can more accurately mine a multi-relational imbalanced database.     

关系数据库的关键词查询性能优化

关系数据库上的关键词查询使得用户不必了解SQL语法或者数据库模式即可方便进行检索,并利用关系表的连接来保证查询的完整性。由于关键词集合查询的信息通常与不同数据类型的字段相关,并且分散在多个表中,而关系数据库缺乏对这类信息有效的索引,从而导致查询效率和准确率比较低。针对关系数据库的结构化模式,提出了一种基于层次树的数据提取方法,将关系数据库模型化为一棵层次树,关键词查询问题转化为树的遍历问题,并从关键词数据类型的分类、属性值之间的相关度、查询结果评分排序等方面设计了一个综合的优化方法。通过实验验证,该方法可以实现以较高的效率和较低的计算代价完成关键词查询。     

The product information system and databases in mechanical CAE

The paper gives an introduction to the different databases required in mechanical CAE, who their users are, and what the data model for each database may look like. The importance of establishing a product structure model and product information management system (PIMS) are stressed, before setting up a CAE database management system. Further, additional topics for research and development are identified.     

数据库关键字查询技术研究

随着互联网技术与数据库技术的不断发展和相互结合,越来越多的用户需要访问在线数据库来获取各种信息,在该过程中,用户要对数据库中的数据进行查询。因此,用户就必需要掌握一定的结构化查询语言SQL,而且还要对数据库模式有一个认识和了解。但事实上,多数用户并不会使用结构化查询语言,也不了解数据库模式。所以,便产生了一个很自然的用户需求——数据库支持基于关键字的查询,文章主要就是对数据库支持基于关键字的查询进行简要的分析和探讨。     

一种CDN中的动态数据存储方案——UbDP

CDN对于动态Web应用的加速通常采用数据缓存或复制技术。针对论坛、博客服务提供商等为注册用户提供个人信息发布平台的网站,提出了一种基于用户的数据分割方法：将数据按所属注册用户进行分割,分布到离该用户最近的数据库系统中。将数据库UID操作分散到多个数据库系统,消除了单个数据库系统的I/O瓶颈。