针对战场态势时空数据的中文查询技术研究

战场态势时空数据引擎是基于时空数据库技术实现的,面向战场时空数据查询的战场态势辅助分析系统.针对时空数据库查询语言的复杂性,设计了战场态势时空数据引擎的中文查询系统,研究了中文查询任务到时空查询语句的映射过程,设计了时空查询词库,研究了分词算法,查询文法以及STQL生成方法.通过原型系统验证了中文查询技术的可行性,提高了时空数据库查询的可用性和易用性.     

基于CouchDB的SPARQL查询引擎实现

传统的SPARQL查询引擎在处理查询时以三元组模式为基本单位做查询优化处理,在三元组模式较多时存在着过多的连接操作,开销比较大。文中基于文档数据库的存储和查询特点,提出一种利用主语分类的方式来存储RDF数据的方法,将不同的RDF三元组按主语分成不同的类,并存入文档数据库的文档中。在处理SPARQL查询时将三元组模式也按照主语分类,构成以主语相关块为单位的查询图,并提出一种基于属性相关性的选择度估计方法来优化查询执行计划。文中利用文档数据库CouchDB实现了新的SPARQL查询引擎,实验证明文中的方法能够提高SPARQL基本图模式查询的效率。     

基于Coreseek+Python的分布式全文检索方法

将Coreseek全文检索引擎应用到分布式数据检索引擎的构建中,利用Python数据源程序接口对多种类型的数据库进行连接,使检索引擎能够同时对不同类型的数据库进行检索。该方法较好地解决了海量数据查询系统中,单极查询性能有限、数据融合复杂问题,提高了系统的数据检索速度和对不同数据库服务器的兼容性。     

基于kd树的多维索引在数据库中的运用

该文突破在数据库查询中建立传统索引进行数据的查询,而是针对微型数据库引擎的设计中要实现的嵌套查询和多表连接查询引进了kd树,kd树在B 树的基础上进行了改进,从而来加快查询速度.该文介绍了kd树的结构、kd树的操作以及其实现的查询.     

基于OGSA的分布式查询处理研究

探讨了在以服务为中心的网格环境中分布式查询的原理及其实现机制,介绍了传统的数据库技术在网格环境中部署和使用的方法,提出了分布式查询引擎需要处理的问题及目前的解决方案。     

Sybase新一代数据库ASE15简介（二）——查询引擎（QPE）

我们使用数据库的最终目的是查询、检索数据。我们继续向您介绍ASE15“聪明的大脑”—查询引擎(QueryProcessingEngine)。对数据库查询引擎的新要求在当今的应用环境中,数据库既要做好交易处理,还要成为用户的决策支持系统(DSS)平台。这种使用上的新趋势给数据库产品带来了新的挑战。原先针对OLTP中简单查询的数据库引擎,现在不但要能处理包含大量复杂的聚集操作、数据分类和多表关联操作的DSS查询,还要能根据不同的应用需求,做出有针对性的优化。ASE15查询引擎的功能灵活的优化策略ASE15共有三种优化目标。第一种是对整个结果集进…     

多核处理器上列数据库复杂查询的原语级并行性开发

提升复杂数据库查询性能是数据库研究的重要方向.提出了一个基于向量原语操作的列数据库查询引擎,通过在多核处理器上开发向量原语之间的并行性来提高数据库查询的性能.介绍了该查询引擎中的向量原语设计、列数据文件存储布局、查询的向量操作依赖图、向量操作动态调度器和内存管理器等关键部件.对于TPC-H 4个典型复杂查询的测试结果表...     

多源异构数据融合与高性能图数据库查询引擎设计

文章针对多源异构数据整合及高性能图数据库查询引擎设计问题,提出创新技术解决方案。通过深度融合实现数据整合与高效存储,并引入先进图数据库技术,以快速响应复杂查询请求。实验证明,该系统在多源异构数据融合和图数据库查询方面具有明显的性能优势,为实际应用提供了可行解决方案。     

向你推荐——MINISQL

MINISQL 或MSQL是一个小数据库引擎,它用来满足对低容量内存数据的快速访问。如其名字一样,MSQL 提供了一套标准 SQL子集的查询界面。尽管它只支持 SQL的子集(无视图,无子查询等),但凡所支持的均与ANSI SQL保持一致,MSQL 软件包包括一个数据库引擎、一个终端监视器、一个数据库管理器、一个方案观察器(Schema Viewer)和一组C语言的API函数。数据库引擎与API函数可以工作在由TCP/IP 网络构成的 Client/Server 环境中。这一迷人之处,在当今流行的 Internet/Intranet 中正好有用武之地。在用C语言编写的CGI中,嵌入它的API函数,就可以完全实现访问数据库的功能。MSQL 的开发者为澳大利亚的 David     

基于SPEECH SDK的中文学习系统

介绍了Microsoft Speech SDK的功能以及相关的一系列API函数,分别利用其语音识别（Speech Recognition）引擎和语音合成（Textto Speech）引擎实现英文语音识别和中文文本语音合成功能,采用Access 2003数据库的查询操作完成翻译功能,进而实现整个英文到中文的学习系统,该系统具有很高的商业价值。     

Partial evaluation of queries for bit-sliced signature files

Our research extends the bit-sliced signature organization by introducing a partial evaluation approach for queries. The partial evaluation approach minimizes the response time by using a subset of the on-bits of the query signature. A new signature file optimization method, Partially evaluated Bit-Sliced Signature File (P-BSSF), for multi-term query environments using the partial evaluation approach is introduced. The analysis shows that, with 14% increase in space overhead, P-BSSF provides a query processing time improvement of more than 85% for multi-term query environments with respect to the best performance of the bit-sliced signature file (BSSF) method. Under the sequentiality assumption of disk blocks, P-BSSF provides a desirable response time of 1 second for a database size of one million records with a 28% space overhead. Due to partial evaluation, the desirable response time is guaranteed for queries with several terms.     

Partial evaluation of queries in deductive databases

This paper presents some applications of partial evaluation method to a query optimization in deductive database. A Horn clause transformation is used for the partial evaluation of a query in an intensional database, and its application to multiple query processing is discussed. Three strategies are presented for the compatible case, ordered case and crossed case. In each case, partial evaluation is used to preprocess the intensional database in order to obtain subqueries which direct access to an extensional database.     

分形技术图像比对搜索模型

传统的搜索引擎只能搜索文字型的资料，显然无法满足用户想通过搜索引擎来取得与图像相关的信息的需求．提出一种图像比对搜索引擎，利用分形图像处理和索引技术来建立图像特征数据库．当用户输入查询图像时，系统对于用户输入的图像也采用与分形图像处理相同的方式取得特征值，然后再与图像特征数据库的特征矢量作比对，达到数据搜索的目的．实验表明，图像比对搜索引擎除了可以找出用户输入的相似图像外，对于查询图像的旋转、模糊或噪声，图像比对搜索引擎也能够找出正确的图像，证明文中方法对图像的容错性和适应性好。     

Incremental Evaluation of Sliding-Window Queries over Data Streams

Two research efforts have been conducted to realize sliding-window queries in data stream management systems, namely, query revaluation and incremental evaluation. In the query reevaluation method, two consecutive windows are processed independently of each other. On the other hand, in the incremental evaluation method, the query answer for a window is obtained incrementally from the answer of the preceding window. In this paper, we focus on the incremental evaluation method. Two approaches have been adopted for the incremental evaluation of sliding-window queries, namely, the input-triggered approach and the negative tuples approach. In the input-triggered approach, only the newly inserted tuples flow in the query pipeline and tuple expiration is based on the timestamps of the newly inserted tuples. On the other hand, in the negative tuples approach, tuple expiration is separated from tuple insertion where a tuple flows in the pipeline for every inserted or expired tuple. The negative tuples approach avoids the unpredictable output delays that result from the input-triggered approach. However, negative tuples double the number of tuples through the query pipeline, thus reducing the pipeline bandwidth. Based on a detailed study of the incremental evaluation pipeline, we classify the incremental query operators into two classes according to whether an operator can avoid the processing of negative tuples or not. Based on this classification, we present several optimization techniques over the negative tuples approach that aim to reduce the overhead of processing negative tuples while avoiding the output delay of the query answer. A detailed experimental study, based on a prototype system implementation, shows the performance gains over the input-triggered approach of the negative tuples approach when accompanied with the proposed optimizations     

RDF查询语言到SQL语言的转换原理及其实现方法

RDF查询语言的优点是具有语义性，缺点是对于海量信息的存储和查找的效率都很低．而关系数据库对海量信息的存储和查找的效率皆很高，但是其查询语言SQL却缺乏语义信息．为了使信息查询既有RDF的语义性又有关系数据库的高性能，提出将RDF查询语言到SQL语言的转换原理，并在此基础上实现一个对用户透明的、建立在关系数据库之上的RDF查询引擎．其优点是：可以利用关系数据库来存储和查询RDF信息，提高其海量存储和查找效率；对存储在不同的关系数据库中的关系数据，能够利用RDF的查找特性进行异质数据库之间的信息交换及信息融合．     

Database querying under changing preferences

We present here a formal foundation for an iterative and incremental approach to constructing and evaluating preference queries. Our main focus is query modification: a query transformation approach which works by revising the preference relation in the query. We provide a detailed analysis of the cases where the order-theoretic properties of the preference relation are preserved in the revision. We consider a number of different revision operators: union, prioritized and Pareto composition. We also formulate algebraic laws that enable incremental evaluation of preference queries. Finally, we consider two variations of the basic framework: finite restrictions of preference relations and weak-order extensions of strict partial order preference relations.      

A Graphical Navigator for viewing Databases

This paper describes the architecture and implementation of Graphical Navigator, a system for visualizing and querying databases. The schema of a database is represented by classes and associations between the classes. Thus, a database is viewed as a labeled graph. A graphical query notation was developed to query databases. The query notation allows formulation of recursive and predicated queries. The system facilitates incremental querying by way of workspaces. The formulation, execution and incremental querying are all done in a highly interactive and visual environment.     

Continuous Query Processing of Spatio-Temporal Data Streams in PLACE

The tremendous increase in the use of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous query processor designed specifically for highly dynamic environments (e.g., location-aware environments). We implemented the proposed continuous query processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server extends data streaming management systems to support location-aware environments. These environments are characterized by the wide variety of continuous spatio-temporal queries and the unbounded spatio-temporal streams. The proposed continuous query processor includes: (1) New incremental spatio-temporal operators to support a wide variety of continuous spatio-temporal queries, (2) Extended semantics of sliding window queries to deal with spatial sliding windows as well as temporal sliding windows, and (3) A shared-execution framework for scalable execution of a set of concurrent continuous spatio-temporal queries. Experimental evaluation shows promising performance of the continuous query processor of the PLACE server. This work was supported in part by the National Science Foundation under Grants IIS-0093116, IIS-0209120, and 0010044-CCR.     

Similarity Joins: Their implementation and interactions with other database operators

Similarity Joins are extensively used in multiple application domains and are recognized among the most useful data processing and analysis operations. They retrieve all data pairs whose distances are smaller than a predefined threshold ε. While several standalone implementations have been proposed, very little work has addressed the implementation of Similarity Joins as physical database operators. In this paper, we focus on the study, design, implementation, and optimization of a Similarity Join database operator for metric spaces. We present DBSimJoin, a physical database operator that integrates techniques to: enable a non-blocking behavior, prioritize the early generation of results, and fully support the database iterator interface. The proposed operator can be used with multiple distance functions and data types. We describe the changes in each query engine module to implement DBSimJoin and provide details of our implementation in PostgreSQL. We also study ways in which DBSimJoin can be combined with other similarity and non-similarity operators to answer more complex queries, and how DBSimJoin can be used in query transformation rules to improve query performance. The extensive performance evaluation shows that DBSimJoin significantly outperforms alternative approaches and scales very well when important parameters like ε, data size, and number of dimensions increase.     

Interaction-aware scheduling of report-generation workloads

The typical workload in a database system consists of a mix of multiple queries of different types that run concurrently. Interactions among the different queries in a query mix can have a significant impact on database performance. Hence, optimizing database performance requires reasoning about query mixes rather than considering queries individually. Current database systems lack the ability to do such reasoning. We propose a new approach based on planning experiments and statistical modeling to capture the impact of query interactions. Our approach requires no prior assumptions about the internal workings of the database system or the nature and cause of query interactions, making it portable across systems. To demonstrate the potential of modeling and exploiting query interactions, we have developed a novel interaction-aware query scheduler for report-generation workloads. Our scheduler, called QShuffler, uses two query scheduling algorithms that leverage models of query interactions. The first algorithm is optimized for workloads where queries are submitted in large batches. The second algorithm targets workloads where queries arrive continuously, and scheduling decisions have to be made online. We report an experimental evaluation of QShuffler using TPC-H workloads running on IBM DB2. The evaluation shows that QShuffler, by modeling and exploiting query interactions, can consistently outperform (up to 4x) query schedulers in current database systems.