面向电子政务的MongoDB与MySQL混合存储策略

针对传统电子政务平台所采用的关系型数据库在存储非结构化数据时的缺陷,利用MongoDB存储海量非结构化数据的优势,提出MongoDB与MySQL混合存储的策略。MongoDB负责存储图片、视频等非结构化数据,MySQL负责存储用户注册信息、工资表等结构化数据。二者协同提供政务数据的云存储服务和查询操作,从而降低MySQL数据库服务器的负载,节约MySQL的存储空间,增强电子政务后台数据库的扩展性。实验结果表明,该混合策略能大大提高电子政务平台的查询效率。     

An integration approach of hybrid databases based on SQL in cloud computing environment

As the applications with big data in cloud computing environment grow, many existing systems expect to expand their service to support the dramatic increase of data, and modern software development for services computing and cloud computing software systems is no longer based on a single database but on existing multidatabases and this convergence needs new software architecture design. This paper proposes an integration approach to support hybrid database architecture, including MySQL, MongoDB, and Redis, to make it possible of allowing users to query data simultaneously from both relational SQL systems and NoSQL systems in a single SQL query. Two mechanisms are provided for constructing Redis's indexes and semantic transforming between SQL and MongoDB API to add the SQL feature for these NoSQL databases. With the proposed approach, hybrid database systems can be performed in a flexible manner, ie, access can be either relational database or NoSQL, depending on the size of data. The approach can effectively reduce development complexity and improve development efficiency of the software systems with multidatabases. This is the result of further research on the related topic, which fills the gap ignored by relevant scholars in this field to make a little contribution to the further development of NoSQL technology.     

一种对话机器人开发技术综述与系统架构实现

对话机器人是使用自然语言处理与生成技术,模拟人类对话逻辑并与人进行交流的计算机程序。作为新一代人工智能产品的人机交互主要入口,逻辑实现方式与交互是对话机器人设计的关键。本文结合对话机器人的技术特点,在实现过程中采用基于Python语言的Django微服务Web应用开发框架,将对话算法模型与逻辑处理过程进行微服务化API封装,使其他应用能够请求对话服务的接口进行功能的二次开发。为了提高对话响应速度,本文采用了关系型数据库MySQL与基于内存的非关系型数据库Redis结合的方式,减少算法模型对硬盘的读取次数,优化了用户体验。     

支持MongoDB的事务管理方案研究

MongoDB作为一个基于分布式文件存储的数据库,强大的单表查询语言,以及可扩展的高性能数据存储受很用户喜爱,但其没有对事务的完全支持,使得用户对MongoDB的使用处于被动状态.为改善MongoDB对事务管理方面的兼容性,提出一种支持MongoDB事务管理,完善MongoDB功能的方案.利用MQ与守护进程间的消息通信,使守护进程对事务提交或者回滚后的脏数据进行清理,保证了MongoDB在事务管理方面的可用性与安全性.     

QQL: A DB&;IR Query Language

Traditional database query languages are based on set theory and crisp first order logic. However, many applications require retrieval-like queries which return result objects associated with a degree of being relevant to the query. Historically, retrieval systems estimate relevance by exploiting hidden object semantics whereas query processing in database systems relies on matching select-conditions with attribute values. Thus, different mechanisms were developed for database and information retrieval systems. In consequence, there is a lack of support for queries involving both retrieval and database search terms. In this work, we introduce the quantum query language (QQL). Its underlying unifying theory is based on the mathematical formalism of quantum mechanics and quantum logic. Van Rijsbergen already discussed the strong relation between the formalism of quantum mechanics and information retrieval. In this work, we interrelate concepts from database query processing to concepts from quantum mechanics and logic. As result, we obtain a common theory which allows us to incorporate seamlessly retrieval search into traditional database query processing.     

基于标记图的Web数据模型

本文详细探讨了一种新的Ｗｅｂ数据模型－标记图,给类格的形式化描述。     

RBAC模型在基于Web新型考试系统中应用的设计

本论文为研发一个增强数据安全的基于Web新型考试系统,提高WebDBMS的安全级别。从数据库访问权限的管理、应用系统功能层访问权限的管理、敏感数据加密保护和身份认证方面讨论基于角色的访问控制模型(RBAC)在基于Web新型考试系统中应用设计。     

PostgreSQL与MongoDB处理非结构化数据性能比较

互联网技术的发展产生的海量非结构化数据在传统关系型数据库中难以被高速有效地进行存储和处理,各类NoSQL数据库可以有效存储处理非结构化数据,但是对关系运算功能的弱化难以满足应用场景的需求。具备非结构化数据处理能力的新型关系型数据库提供了适用多种应用场景的高效存储方式。为了能够定量地比较关系型数据库和面向文档的NoSQL数据库的数据存储与处理能力,比较了PostgreSQL的hstore数据类型和MongoDB的内嵌文档对非结构化数据的储存方式,并通过非结构化数据的批量加载、磁盘占用、主键查询、非主键查询、地理空间坐标查询等方面的对比来以分析性能特征与适用场景。     

EXODuS: Exploratory OLAP over Document Stores

OLAP has been extensively used for a couple of decades as a data analysis approach to support decision making on enterprise structured data. Now, with the wide diffusion of NoSQL databases holding semi-structured data, there is a growing need for enabling OLAP on document stores as well, to allow non-expert users to get new insights and make better decisions. Unfortunately, due to their schemaless nature, document stores are hardly accessible via direct OLAP querying. In this paper we propose EXODuS, an interactive, schema-on-read approach to enable OLAP querying of document stores in the context of self-service BI and exploratory OLAP. To discover multidimensional hierarchies in document stores we adopt a data-driven approach based on the mining of approximate functional dependencies; to ensure good performances, we incrementally build local portions of hierarchies for the levels involved in the current user query. Users execute an analysis session by expressing well-formed multidimensional queries related by OLAP operations; these queries are then translated into the native query language of MongoDB, one of the most popular document-based DBMS. An experimental evaluation on real-world datasets shows the efficiency of our approach and its compatibility with a real-time setting.     

WEB半结构化数据查询

当前许多大的Web站点的信息和数据呈现结构化或半结构化的特点，因而可经抽象，作为类似关系数据库或面向对象数据库并加以处理，以提高操作效率，特别是在此基础上的查询操作。采用数据模型Araneus的一个子集作为数据模型，并采用连接约束、包含约束、范围约束，提出一种半结构化查询重写的方法，该方法在保证算法正确性和完备性的基础上，利用半结构化数据特点和查询子目标之间的关系，极大地降低了算法的代价。     

基于Top-k查询约束的深网增量爬取*

深网数据源的动态性、自治性和体量使第三方应用难以完全爬取所有Web数据.文中研究查询类型(仅允许Top-k查询)和查询资源约束下深网数据源爬取问题,提出基于Top-k查询约束的深网增量爬取方法,结合历史数据和领域知识,优化总体数据质量.首先基于查询树获得有效查询,利用历史数据和领域知识估计查询变化和查询代价.然后,基于估计的查询代价和数据质量,近似选择最优的查询子集最大化总体数据质量.实验表明文中方法较好地提高动态Web数据库爬取的效率和数据质量.     

Performance evaluation of NoSQL big-data applications using multi-formalism models

Starting with the birth of Web 2.0, the quantity of data managed by large-scale web services has grown exponentially, posing new challenges and infrastructure requirements. This has led to new programming paradigms and architectural choices, such as map-reduce and NoSQL databases, which constitute two of the main peculiarities of the specialized massively distributed systems referred to as Big Data architectures. The underlying computer infrastructures usually face complexity requirements, resulting from the need for efficiency and speed in computing over huge evolving data sets. This is achieved by taking advantage from the features of new technologies, such as the automatic scaling and replica provisioning of Cloud environments. Although performances are a key issue for the considered applications, few performance evaluation results are currently available in this field. In this work we focus on investigating how a Big Data application designer can evaluate the performances of applications exploiting the Apache Hive query language for NoSQL databases, built over a Apache Hadoop map-reduce infrastructure.This paper presents a dedicated modeling language and an application, showing first how it is possible to ease the modeling process and second how the semantic gap between modeling logic and the domain can be reduced, by means of vertical multiformalism modeling.     

一种Web应用软件安全脆弱性测试模型

研究使用错误注入的方法进行web应用软件脆弱性测试的技术,提出环境与状态错误模型(EAS模型),并提出基于EAS模型的web应用软件安全性测试方法.使用EAS模型对来自OWASP的漏洞数据库进行了分类,并与使用EAI模型分类的结果做了对比.使用EAS模型测试了WEB应用软件PEGames,发现了其中的6个漏洞.实验表明,EAS模型具有良好的漏洞覆盖能力和漏洞揭示能力.     

Separating adaptable persistence attributes through computational reflection

To receive persistence capabilities, applications usually must explicitly access database management systems, such as object-oriented databases or object-relational mapping products. The usual way of building an application is tangling its functional code with explicit structured query language (SQL) or object query language (OQL) persistence statements. This tangling has several drawbacks: deficiencies in source code legibility, maintainability, and portability; lack of persistence functionality reuse; and low adaptability of persistence attributes. Separating crosscutting concerns such as persistence from the main application code can reduce software complexity and facilitate concerns reuse. A prototype persistence system shows how to use computational reflection to completely separate and adapt application persistence attribute at runtime.     

Learning to crawl deep web

Deep web or hidden web refers to the hidden part of the Web (usually residing in structured databases) that remains unavailable for standard Web crawlers. Obtaining content of the deep web is challenging and has been acknowledged as a significant gap in the coverage of search engines. The paper proposes a novel deep web crawling framework based on reinforcement learning, in which the crawler is regarded as an agent and deep web database as the environment. The agent perceives its current state and selects an action (query) to submit to the environment (the deep web database) according to Q-value. While the existing methods rely on an assumption that all deep web databases possess full-text search interfaces and solely utilize the statistics (TF or DF) of acquired data records to generate the next query, the reinforcement learning framework not only enables crawlers to learn a promising crawling strategy from its own experience, but also allows for utilizing diverse features of query keywords. Experimental results show that the method outperforms the state of art methods in terms of crawling capability and relaxes the assumption of full-text search implied by existing methods.     

用Struts和Hibernate构筑Web应用开发策略

当今越来越多的Web应用是基于MVC设计模式的.此种设计模式提高了应用系统的可维护性、可扩展性和组件的可复用性.Apache开源组织提供的Struts框架充分体现了MVC架构.Hibernate实际上是一个用对象编程思维来操纵数据库的解决方案.提出了一种基于Struts和Hibernate架构的Web应用开发策略.MVC架构中,模型、视图和控制器3个部分中的模型部分（数据持久层）用Hibernate实现,视图和控制器的实现依托于Struts框架.这种策略真正实现了层间的松散耦合.     

Using a relational database to support explanation in aknowledge-based system

In knowledge-based system (KBS) applications where the number of facts in the knowledge base is very large, the amount of information that is needed for effective explanation support can become too voluminous to be stored in main memory. The authors present an approach to modeling and managing the information needed for explanation rising a relational database. It is shown how different types of explanation can then be produced by appropriate queries on this database. The authors formulate representative queries for some major types of explanation using ESQL, an extension of structured query language (SQL)