对SQL查询结果分组的过程分析与设计

对SQL中的查询结果进行分组情况下DBMS的执行过程进行分析,并对查询结果分组进行设计。     

对SQL查询结果分组的过程分析与设计

对SQL中的查询结果进行分组情况下DBMS的执行过程进行分析，并对查询结果分组进行设计。     

面向数据流离线分析的无共享并行查询中间件研究

通过分析网络监控离线分析处理的负载特征,给出了一种面向数据流离线分析处理的并行多策略查询中间件,并利用多策略及DBMS实现了局部结果的汇总,对需后处理查询的系统扩展性、不需后处理查询的系统扩展性分别进行了评价分析。评价分析结果表明:提出的基于面向数据流离线分析处理的并行多策略查询中间件,不但做到了中间件的轻量级,实现了查询内部的并行化;同时,能利用多策略及DBMS实现子节点间并行查询,能提高查询的响应时间,从而在多节点时保持较好的扩展比,避免了系统过载,提高了资源利用率。     

XML 数据流上Top-K 关键字查询处理

利用关键字可以在模式未知的情况下对XML数据进行查询.在当前的XML数据流上的关键字查询处理中,打分函数往往不能都满足各种用户不同的需求.提出了一种基于skyline的XML数据流上的Top-K关键字查询.对于这种查询,不需要考虑影响结果与查询相关性的复杂因素,只需利用skyline挑选与查询最相关的结果.提出了两种XML数据流上的有效的基于skyline的Top-K关键查询处理算法,包括对单查询和多查询的处理算法.通过扩展实验对两种算法的有效性和可扩展性进行了验证.经过实验验证,所提出的查询处理算法的效率几乎不受关键字个数、查询结果数量、查询数量等参数的影响,运行时间和文档大小大致呈线性关系.     

XML数据流上的有序XPath查询处理

XML数据流上的查询处理是最近研究工作的一个热点,如何高效地处理XML数据流上的XPath查询是其中的核心问题.之前的相关工作主要考虑了无序XPath查询处理的情况,而在股票信息监控、新闻信息订阅等很多的XML数据流应用中常常需要对有序XPath查询进行有效的支持.对于有序XPath查询的处理,之前的方法需要将查询进行分解,然后通过连接将分解后的子查询得到的中间结果合并.针对有序XPath查询自身的特点,提出了在查询树上引入顺序和位置标记,记录查询结点之间的顺序关系,并在此基础上提出了一种创新的XML数据流上的XPath查询处理算法OrderedXP.相比之前的工作,OrderedXP能够大量地减少缓存的中间结果数目,而且不需要分解原来的查询,避免了额外的连接操作.详细的实验数据验证了OrderedXP能够显著地提高有序XPath查询在XML数据流上的执行效率.     

一种数据库加密及密钥管理方法

数据庠中敏感数据的防窃取和防篡改问题是信息安全的一个重要方面,在数据库管理系统（DBMS）中加入安全特性已迫在眉睫。提出一种方法,对DBMS实现进行修改,在不影响DBMS功能的情况下使其支持数据库加密,并且对DBMS性能的影响也很小。给出了一个与之相关的密钥管理体系结构,使得密钥的管理既安全又高效。最后,在TPC—H数据集上对数据库加密后的性能进行了评估。     

能效查询处理与优化可行性初探

随着数据管理需求的不断增长,降低与控制数据中心的能耗成为一个挑战性问题. DBMS 是数据中心核心软件,能效查询处理与优化是其中一个重要议题. 本文提出了新型的能耗代价评估模型,通过评估查询计划的时间和能耗代价,考察了不同优化目标在不同硬件条件下对查询处理的影响. 实验表明,传统硬件下面向性能的优化与面向能耗的优化结果是一致的;在新硬件条件下,两者结果则不同,可以改进数据库系统能效.     

Web数据库技术和实例分析

Internet尤其是WWW提供了在Internet和Intranets上可获得各种数据和信息的有效途径。但是,所获得的信息作成HTML静态网页就不太适于已存在于DBMS中庞大的数据。潜在的巨大数据量必须双重管理,数据更新难于处理,并且查询能力有限。另一局限是标准的WWW技术不允许把从不同服务器的部分结果合为单一的结果页面。     

两层传感器网络中安全Top-k查询协议

在两层结构传感器网络中,存储节点收集传感器采集的数据,负责处理Sink的查询.在敌对环境中,存储节点可能会被攻击者妥协而泄露传感器所采集的敏感数据以及向Sink返回不完整的或虚假的查询结果.为此,提出了一种安全Top-k查询协议:SecTQ,SecTQ在保证存储节点正确执行查询的同时能有效防止敏感数据的泄露.为了保护数据的隐私性,首先将不同传感器采集的数据之间的直接比较转换成传感器采集的数据与Sink提供的查询比较值进行比较,并提出了一种基于扰动多项式函数的隐私保护方案.该方案利用扰动函数对传感器采集的数据和Sink提供的查询比较值进行编码,保证存储节点在不知道数据和查询比较值真实内容的情况下正确地执行查询处理.为了保护查询结果的完整性,提出了一种称之为水印链的方案,该方案能有效检测查询结果的完整性.     

改进的基于小枝模式的匹配算法——cTwigStack

给定一个小枝模式查询,如何快速地在XML数据集中找到所有感兴趣的信息,已成为当前研究的热点。针对TwigStack算法在处理含有父子节点的情况下会产生大量的中间结果等问题,通过栈来对非叶子节点缓存和对叶子节点延迟输出的思想,提出了一种改进的小枝模式匹配算法--cTwigStack。采用Treebank数据集进行测验,结果表明该算法不仅仅在处理祖孙/后继节点时能使输出结果的准确性达到最优,而且在处理父子节点时,相对目前提出的算法,也是非常高效的。     

Comparing columnar,row and array DBMSs to process recursive queries on graphs

Analyzing graphs is a fundamental problem in big data analytics, for which DBMS technology does not seem competitive. On the other hand, SQL recursive queries are a fundamental mechanism to analyze graphs in a DBMS, whose processing and optimization are significantly harder than traditional SPJ queries. Columnar DBMSs are a new faster class of database system, with significantly different storage and query processing mechanisms compared to row DBMSs, still the dominating technology. With that motivation in mind, we study the optimization of recursive queries on a columnar DBMS focusing on two fundamental and complementary graph problems: transitive closure and adjacency matrix multiplication. From a query processing perspective we consider the three fundamental relational operators: selection, projection and join (SPJ), where projection subsumes SQL group-by aggregation. We present comprehensive experiments comparing recursive query processing on columnar, row and array DBMSs to analyze large graphs with different shape and density. We study the relative impact of query optimizations and we compare raw speed of DBMSs to evaluate recursive queries on graphs. Results confirm classical query optimizations that keep working well in a columnar DBMS, but their relative impact is different. Most importantly, a columnar DBMS with tuned query optimization is uniformly faster than row and array systems to analyze large graphs, regardless of their shape, density and connectivity. On the other hand, there is no clear winner between the row and array DBMSs.     

一种并行XML数据分片算法的设计与实现

随着XML在各个领域的广泛应用，XML数据规模急剧增加，查询处理也日趋复杂。集中式环境由于单机存在的I／O瓶颈问题，在处理日益复杂的查询操作时已渐渐不能满足应用需求。因此，高性能的并行数据库在XML处理方面的研究，必将为XML数据库的。存储和查询处理带来新的曙光。本文讨论了一种对XML文档的基于节点轮循法进行分片的设计原理及实现算法。实验结果证明该方法具有很好的加速比和缩放比性能。     

PostgreSQL数据库在不同调节器中的能效比较

近年来,能效数据库系统成为数据库领域的一个研究议题.CPU动态电压频率调节(DVFS)是一种有效的动态功率节能技术.探寻PostgreSQL数据库在ACPI不同调节器下查询操作的性能、能耗、功率之间潜在联系,发现动态功耗管理与数据库系统的能效关系,通过运行TPC-H测试基准生成的数据库与相应22个查询,总结出调节器对数据库查询处理各种操作的影响.实验结果表明,DVFS可以对DBMS进行动态功耗管理是有效的,查询处理的不同操作具有各自特性,利用这些特性来设计效率更高的调节器是颇有前途的.     

PARADISE: Big data analytics using the DBMS tightly integrated with the distributed file system

There has been a lot of research on MapReduce for big data analytics. This new class of systems sacrifices DBMS functionality such as query languages, schemas, or indexes in order to maximize scalability and parallelism. However, as high functionality of the DBMS is considered important for big data analytics as well, there have been a lot of efforts to support DBMS functionality in MapReduce. HadoopDB is the only work that directly utilizes the DBMS for big data analytics in the MapReduce framework, taking advantage of both the DBMS and MapReduce. However, HadoopDB does not support sharability for the entire data since it stores the data into multiple nodes in a shared-nothing manner—i.e., it partitions a job into multiple tasks where each task is assigned to a fragment of data. Due to this limitation, HadoopDB cannot effectively process queries that require internode communication. That is, HadoopDB needs to re-load the entire data to process some queries (e.g., 2-way joins) or cannot support some complex queries (e.g., 3-way joins). In this paper, we propose a new notion of the DFS-integrated DBMS where a DBMS is tightly integrated with the distributed file system (DFS). By using the DFS-integrated DBMS, we can obtain sharability of the entire data. That is, a DBMS process in the system can access any data since multiple DBMSs are run on an integrated storage system in the DFS. To process big data analytics in parallel, our approach use the MapReduce framework on top of a DFS-integrated DBMS. We call this framework PARADISE. In PARADISE, we employ a job splitting method that logically splits a job based on the predicate in the integrated storage system. This contrasts with physical splitting in HadoopDB. We also propose the notion of locality mapping for further optimization of logical splitting. We show that PARADISE effectively overcomes the drawbacks of HadoopDB by identifying the following strengths. (1) It has a significantly faster (by up to 6.41 times) amortized query processing performance since it obviates the need to re-load data required in HadoopDB. (2) It supports query types more complex than the ones supported by HadoopDB.     

ARAXA: Storing and managing Active XML documents

Active XML (AXML) documents combine extensional XML data with intentional data defined through Web service calls. The dynamic properties of these documents pose challenges to both storage and data materialization techniques. In this paper, we present ARAXA, a non-intrusive approach to store and manage AXML documents. We also define a methodology to materialize AXML documents at query time. The storage approach of ARAXA is based on plain relational tables and user-defined functions of Object-Relational DBMS to trigger the service calls. By using a DBMS we benefit from efficient storage tools and query optimization. Approaches without DBMS support have to process XML in main memory or provide for virtual memory solutions. One of the main advantages of ARAXA is that AXML documents do not need to be loaded into main memory at query processing time. This is crucial when dealing with large documents. The experimental results with ARAXA prototype show that our approach is scalable and capable of dealing with large AXML documents.     

A foundation for conventional and temporal query optimizationaddressing duplicates and ordering

Most real-world databases contain substantial amounts of time-referenced, or temporal, data. Recent advances in temporal query languages show that such database applications may benefit substantially from built-in temporal support in the DBMS. To achieve this, temporal query representation, optimization, and processing mechanisms must be provided. This paper presents a foundation for query optimization that integrates conventional and temporal query optimization and is suitable for both conventional DBMS architectures and ones where the temporal support is obtained via a layer on top of a conventional DBMS. This foundation captures duplicates and ordering for all queries, as well as coalescing for temporal queries, thus generalizing all existing approaches known to the authors. It includes a temporally extended relational algebra to which SQL and temporal SQL queries may be mapped, six types of algebraic equivalences, concrete query transformation rules that obey different equivalences, a procedure for determining which types of transformation rules are applicable for optimizing a query, and a query plan enumeration algorithm     

移动对象数据库模型、查询语言及实时交通流分析

提出一种移动对象数据库模型——Dynamic Transportation Network Based Moving Objects Database(简称DTNMOD),并给出了DTNMOD中基于移动对象时空轨迹的网络实时动态交通流分析方法.在DTNMOD中,交通网络被表示成动态的时空网络,可以描述交通状态、拓扑结构以及交通参数随时间的变化过程;网络受限的移动对象则用网络移动点表示.DTNMOD 模型包含了完整的数据类型和查询操作的定义,因此可以在任何可扩充数据库(如PostgreSQL 或SECONDO)中实现,从而得到完整的数据库模型和查询语言.为了对相关模型的性能进行比较与分析,基于PostgreSQL 实现了一个原型系统并进行了一系列的实验.实验结果表明,DTNMOD 提供了良好的区域查询及连接查询性能.     

VISIONARY: a Viewpoint-based Visual Language for Querying Relational Databases

The adoption of a visual interface can simplify the query formulation process in DBMSs by enabling naive users to interact with a friendly environment. In this work, we propose a visual query language based on a diagrammatic paradigm, used for both data and query representation. The external data model is called vision and is made up of the visual primitives of concept and association. The external query model is based on the definition of a viewpoint, which is a perspective for accessing data defined dynamically by selecting a concept of primary interest. Internally, the data model is relational and the query language is SQL. An intermediate graph-based model ensures consistent mapping between the visual and the relational worlds. Our language has been implemented within a tool which can be mounted on top of any relational DBMS supporting ODBC. The system has been tested with naive users; the results of the experiment are reported and compared with those obtained with other visual languages.