A Grammar Analysis Model for the Unified Multimedia Query Language

The unified multimedia query language （UMQL） is a powerful general-purpose multimedia query language, and it is very suitable for multimedia information retrieval. The paper proposes a grammar analysis model to implement an effective grammatical processing for the language. It separates the grammar analysis ofa UMQL query specification into two phases： syntactic analysis and semantic analysis, and then respectively uses Backus-Naur form （EBNF） and logical algebra to specify both restrictive grammar rules. As a result, the model can present error guiding information for a query specification which owns incorrect grammar. The model not only suits well the processing of UMQL queries, but aLso has a guiding significance for other projects concerning query processings of descriptive query languages.

Large Language Model Based Semantic Parsing for Intelligent Database Query Engine

With the rapid development of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. These models have great potential to enhance database query systems, enabling more intuitive and semantic query mechanisms. Our model leverages LLM’s deep learning architecture to interpret and process natural language queries and translate them into accurate database queries. The system integrates an LLM-powered semantic parser that translates user input into structured queries that can be understood by the database management system. First, the user query is pre-processed, the text is normalized, and the ambiguity is removed. This is followed by semantic parsing, where the LLM interprets the pre-processed text and identifies key entities and relationships. This is followed by query generation, which converts the parsed information into a structured query format and tailors it to the target database schema. Finally, there is query execution and feedback, where the resulting query is executed on the database and the results are returned to the user. The system also provides feedback mechanisms to improve and optimize future query interpretations. By using advanced LLMs for model implementation and fine-tuning on diverse datasets, the experimental results show that the proposed method significantly improves the accuracy and usability of database queries, making data retrieval easy for users without specialized knowledge.

Query Optimization Framework for Graph Database in Cloud Dew Environment

The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of resources.The challenge of query optimization for relational database systems is a combinatorial optimization problem,which renders exhaustive search impossible as query sizes rise.Increases in CPU performance have surpassed main memory,and disk access speeds in recent decades,allowing data compression to be used—strategies for improving database performance systems.For performance enhancement,compression and query optimization are the two most factors.Compression reduces the volume of data,whereas query optimization minimizes execution time.Compressing the database reduces memory requirement,data takes less time to load into memory,fewer buffer missing occur,and the size of intermediate results is more diminutive.This paper performed query optimization on the graph database in a cloud dew environment by considering,which requires less time to execute a query.The factors compression and query optimization improve the performance of the databases.This research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.

Query Languages for Temporal Database

In this paper, an interval-gap-based 1NF temporal tuple calculus language and the corresponding temporal relation algebra are established on the basis of considering the trouble of stack operations in both S. Gadia’s TCAL and temporal tuple calculus due to their NINF.

A Database Querying Language for Formulating Relational Queries on Small Devices

For small devices like the PDAs and mobile phones, formulation of relational database queries is not as simple as using conventional devices such as the personal computers and laptops. Due to the restricted size and resources of these smaller devices, current works mostly limit the queries that can be posed by users by having them predetermined by the developers. This limits the capability of these devices in supporting robust queries. Hence, this paper proposes a universal relation based database querying language which is targeted for small devices. The language allows formulation of relational database queries that uses minimal query terms. The formulation of the language and its structure will be described and usability test results will be presented to support the effectiveness of the language.

Multi - weighted tree based query optimization method for parallel relational database systems

The author investigates the query optimization problem for parallel relational databases. A multi - weighted tree based query optimization method is proposed. The method consists of a multi - weighted tree based parallel query plan model, a cost model for parallel qury plans and a query optimizer. The parallel query plan model is the first one to model all basic relational operations, all three types of parallelism of query execution, processor and memory allocation to operations, memory allocation to the buffers between operations in pipelines and data redistribution among processors. The cost model takes the waiting time of the operations in pipelining execution into consideration and is computable in a bottom - up fashion. The query optimizer addresses the query optimization problem in the context of Select - Project - Join queries that are widely used in commercial DBMSs. Several heuristics determining the processor allocation to operations are derived and used in the query optimizer. The query optimizer is aware of memory resources in order to generate good - quality plans. It includes the heuristics for determining the memory allocation to operations and buffers between operations in pipelines so that the memory resourse is fully exploit. In addition, multiple algorithms for implementing join operations are consided in the query optimizer. The query optimizer can make an optimal choice of join algorithm for each join operation in a query. The proposed query optimization method has been used in a prototype parallel database management system designed and implemented by the author.

Processing and optimization of UMQL-based multimedia queries

Through the mapping from UMQL （ unified multimedia query language） conditional expressions to UMQA （unified multimedia query algebra） query operations, a translation algorithm from a UMQL query to a UMQA query plan is put forward, which can generate an equivalent UMQA internal query plan for any UMQL query. Then, to improve the execution costs of UMQA query plans effectively, equivalent UMQA translation formulae and general optimization strategies are studied, and an optimization algorithm for UMQA internal query plans is presented. This algorithm uses equivalent UMQA translation formulae to optimize query plans, and makes the optimized query plans accord with the optimization strategies as much as possible. Finally, the logic implementation methods of UMQA plans, i.e., logic implementation methods of UMQA operators, are discussed to obtain useful target data from a muifirnedia database. All of these algorithms are implemented in a UMQL prototype system. Application results show that these query processing techniques are feasible and applicable.

A solution of spatial query processing and query optimization for spatial databases

Recently, attention has been focused on spatial query language which is used to query spatial databases. A design of spatial query language has been presented in this paper by extending the standard relational database query language SQL. It recognizes the significantly different requirements of spatial data handling and overcomes the inherent problems of the application of conventional database query languages. This design is based on an extended spatial data model, including the spatial data types and the spatial operators on them. The processing and optimization of spatial queries have also been discussed in this design. In the end, an implementation of this design is given in a spatial query subsystem.

Semantic-based query processing for relational data integration

To solve the query processing correctness problem for semantic-based relational data integration,the semantics of SAPRQL（simple protocol and RDF query language） queries is defined.In the course of query rewriting,all relative tables are found and decomposed into minimal connectable units.Minimal connectable units are joined according to semantic queries to produce the semantically correct query plans.Algorithms for query rewriting and transforming are presented.Computational complexity of the algorithms is discussed.Under the worst case,the query decomposing algorithm can be finished in O（n2） time and the query rewriting algorithm requires O（nm） time.And the performance of the algorithms is verified by experiments,and experimental results show that when the length of query is less than 8,the query processing algorithms can provide satisfactory performance.

A Distributed DBMS Based Dynamic Programming Method for Query Optimization

Dynamic programming(DP) is an effective query optimization approach to select an appropriate join order for relational database management system(RDBMS) in multi-table joins. This method was extended and made available in distributed DBMS(D-DBMS). The structure of this optimal solution was firstly characterized according to the distributing status of tables and data, and then the recurrence relations between a problem and its sub-problems were recursively defined. DP in D-DBMS has the same time-complexity with that in centralized DBMS, while it has the capability to solve a much more sophisticated optimal problem of multi-table join in D-DBMS. The effectiveness of this optimal strategy has been proved by experiments.

A Shallow Parsing Approach to Natural Language Queries of a Database

The performance and reliability of converting natural language into structured query language can be problematic in handling nuances that are prevalent in natural language. Relational databases are not designed to understand language nuance, therefore the question why we must handle nuance has to be asked. This paper is looking at an alternative solution for the conversion of a Natural Language Query into a Structured Query Language (SQL) capable of being used to search a relational database. The process uses the natural language concept, Part of Speech to identify words that can be used to identify database tables and table columns. The use of Open NLP based grammar files, as well as additional configuration files, assist in the translation from natural language to query language. Having identified which tables and which columns contain the pertinent data the next step is to create the SQL statement.

GPU数据库实现技术发展演进

爆炸式增长的数据对存储和处理数据提出了更高的需求,GPU数据库作为新硬件数据库的一个重要分支,在大容量和高性能处理方面有其独特的优势.GPU数据库作为高性能数据库的代表,在最近几年受到学术界和产业界的关注,一批具有代表性的研究成果和标志性的实际产品已经出现.GPU数据库的技术发展按照GPU加速型和GPU内存型两种技术路线展开.两种技术路线都有相应的原型系统或产品出现.虽然两种GPU数据库的发展路线在实现上有所不同,但GPU数据库最基本的功能部分和核心技术是相似的,都有查询编译、查询优化、查询执行以及存储管理等功能.当前主流的数据传输方案除了PCIe之外,NVLink、RDMA和CXL等传输方案也为不同处理器之间的数据传输提供了更多的可能性.大多数GPU数据库使用列存储模型来存储数据,少数GPU数据库(如PG-Strom)对两种存储模型都支持.在列存储模型上利用压缩技术能减少数据的存储空间和传输时延.在GPU数据库上进行的压缩和解压的时间应该在整个数据处理的过程中占比很少.在GPU数据库上建立和维护索引不应该有很大的系统开销.JIT编译时间短、编译效率高,是GPU数据库编译的主流.操作符对数据库查询性能的影响非常明显,连接操作、分组聚集和OLAP运算符是目前研究最多的三个类型.目前大多数的研究中,连接和分组聚集算子通常结合在一起研究.在连接算子执行的过程中还和表的连接顺序结合在一起进行考虑.OLAP算子是GPU数据库中的又一个被大量研究的算子,GPU数据库在OLAP算子和模型方面持续受到研究者的关注.GPU数据库有三种查询处理模型,即行处理、列处理和向量化处理.向量化处理和列处理在实际系统中应用较多.由于GPU加速型数据库技术的发展,CPU-GPU协同处理模型上的查询方案与查询引擎也有一定数量的研究成果出现.当前GPU数据库的查询优化研究主要有三部分:多表连接顺序、查询重写和代价模型.然而,GPU数据库的代价评估模型在目前还没有很好的解决方案,GPU数据库的查询优化在未来仍有很大的研究空间.事务在GPU数据库中没有得到很好的研究,尽管有单独的原型系统,但目前的研究还没有取得重大进展.本文总结了GPU数据库各种关键技术已有的研究成果,指出GPU数据库当前存在的问题和面临的挑战,对未来的研究方向进行了展望.

面向分布式数据库的算子并行优化策略

随着网络技术的不断发展,数据规模呈现爆发式增长,使得传统的单机数据库逐步被分布式数据库所取代。分布式数据库采用节点协同工作方式解决了大规模数据存储问题,但由于增加了节点间通信开销,查询效率却不如单机数据库。分布式架构下,存储节点的数据仅用作多备份的冗余,为系统故障时提供数据恢复,并未被利用起来改善查询效率。针对上述问题,提出了一种面向分布式数据库的算子并行优化策略,通过对关键物理算子进行拆分,将拆分后的子请求均匀分配到存储层多个节点,由多个节点并行处理,从而减少查询响应时间。上述策略已经在分布式数据库CBase上进行了应用,实验表明,提出的并行优化策略可显著缩短SQL请求查询时间,并提高系统资源利用率。

面向Select和Sort的数据库算子缓存的设计与实现

缓存是数据库中提高查询性能的一种常用技术.目前,现有数据库缓存主要有两个方向:查询结果缓存和存储层块缓存.查询结果缓存是利用数据库查询执行的最终结果或中间结果(如子查询),而存储层块缓存则缓存查询涉及的底层数据块.本文从另外一个角度“缓存中含有的计算量”来重新审视缓存在查询优化中的应用,并以此为基础进一步划分数据库缓存方式.在查询执行过程中,数据库查询被转换成一系列操作(例如选择、排序等)的集合,而算子对应操作.查询处理中算子输出的数据为中间结果,含有部分计算量,我们将这部分数据进行缓存并加以利用.我们将这种缓存部分计算量的缓存方式称为算子缓存,即缓存每个操作执行后的结果.由于不同查询之间可能会存在相同算子,对相近数据执行相同计算,因此利用算子缓存加速查询执行性能具有相当大的潜力.本文的新颖之处在于从缓存含有的计算量角度出发,提出并研究算子缓存如何在查询优化中应用.本文以Filter、Sort算子为例,针对缓存复用提出了一种基于语义树的匹配算法,用于快速匹配缓存中的结果集.同时,针对复用缓存可能劣化查询性能的情况,提出使用基于成本的代价优化器防止使用缓存劣化查询性能.最后,本文基于开源分析型数据库ClickHouse实现了Filter、Sort算子缓存的原型,并对提出的算子缓存方案进行了大量的实验测试.结果表明,相比块缓存、物化视图方式,本文提出的算子缓存方案在本地SSD部署下最大能够分别提升9倍以及1.5倍的查询响应速度,在云环境下部署能够分别提升30倍以及2倍的查询响应速度.

基于结构化查询语言的企业管理系统数据库设计与实现

在当今的商业环境中,企业管理系统(Enterprise Management System,EMS)扮演着至关重要的角色,能帮助组织有效地管理其日常运营和长期战略规划。本文全面探讨了基于结构化查询语言(Structured Query Language,SQL)的企业管理系统数据库的设计与实现,详细介绍了数据库模型的设计过程,包括实体-联系(Entity-relationship model,ER)模型的创建、数据库的逻辑设计,以及如何使用SQL语言实现这一设计,为企业管理系统的运行提供保障。

基于机器学习的基数估计技术综述

基数估计是数据库关系系统查询优化器的基础和核心.随着人工智能技术的发展,其在数据处理、提取数据之间的关系等方面显现出优越的性能.近年来,基于机器学习的基数估计技术取得了显著的进展,受到了学术界的广泛关注.首先总结了基于机器学习的技术估计技术的发展现状,其次给出了基数估计的相关概念及其特征编码技术.接着建立了基数估计技术的分类体系.在此基础上,进一步将基于机器学习的基数估计技术细分为查询驱动、数据驱动和混合模型这3类基数估计技术.然后重点分析了每一类技术的建模流程、典型技术和模型特点,并对其在SQL和NoSQL中的应用进行了分析和总结.最后讨论了基于机器学习的基数估计技术面临的挑战和未来的研究方向。

A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics

A new secured database management system architecture using intrusion detection systems(IDS)is proposed in this paper for organizations with no previous role mapping for users.A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm.A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’profiles.Then,queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is malicious.The IDS will stop query execution or report the threat to the responsible person if the query is malicious.A simple classifier based on the Euclidean distance is used and the issued query is transformed to the proposed simple representation using a classifier,where the Euclidean distance between the centers and the profile’s issued query is calculated.A synthetic data set is used for our experimental evaluations.Normal user access behavior in relation to the database is modelled using the data set.The false negative(FN)and false positive(FP)rates are used to compare our proposed algorithm with other methods.The experimental results indicate that our proposed method results in very small FN and FP rates.

基于TreeLSTM的查询基数估计

针对传统的数据库管理系统无法很好地学习谓词之间的交互以及无法准确地估计复杂查询的基数问题,提出了一种树形结构的长短期记忆神经网络(Tree Long Short Term Memory, TreeLSTM)模型建模查询,并使用该模型对新的查询基数进行估计.所提出的模型考虑了查询语句中包含的合取和析取运算,根据谓词之间的操作符类型将子表达式构建为树形结构,根据组合子表达式向量来表示连续向量空间中的任意逻辑表达式.TreeLSTM模型通过捕捉查询谓词之间的顺序依赖关系从而提升基数估计的性能和准确度,将TreeLSTM与基于直方图方法、基于学习的MSCN和TreeRNN方法进行了比较.实验结果表明:TreeLSTM的估算误差比直方图、MSCN、TreeRNN方法的误差分别降低了60.41%,33.33%和11.57%,该方法显著提高了基数估计器的性能.

SQL-to-text模型的组合泛化能力评估方法

数据库的结构化查询语言(SQL)到自然语言的翻译(SQL-to-text)能提高关系数据库的易用性。近年来该领域主要使用机器学习的方法进行研究并已取得一定进展,然而现有翻译模型的能力仍不足以投入实际应用。由于组合泛化能力是SQL-to-text模型在实际应用中提升翻译效果的必要能力,且目前缺少对此类模型组合泛化能力的研究,因此提出一种SQL-to-text模型的组合泛化能力评估方法。基于现有的SQL-to-text数据集生成大量SQL和对应的自然语言翻译(SQL-自然语言对),并按SQL-自然语言对所含SQL子句的个数将其划分为训练数据与测试数据,使测试数据中的SQL子句皆以不同的组合方式在训练数据中出现,从而得到可评估模型组合泛化能力的新数据集。评估结果表明,该方法对查询知识的使用程度较高,划分数据的方式更加合理,所得数据集符合评估组合泛化能力的需求且贴近模型的实际应用场景,受到原始数据集的限制程度更低,并证实现有模型的组合泛化能力仍需提升,其中针对SQL-to-text任务设计的关系感知图转换器模型组合泛化能力最弱,表明原有的SQL-to-text数据集对组合泛化能力的考察存在欠缺。

SQL语言在数据库实践课程中的应用

本文介绍了SQL语言的主要含义,给出了SQL语言在数据库实践课程中的一些应用,如查询单表的某公司职员综合信息表、多表的某高校十一人制男子组足球比赛赛事数据分析表,依托这些实例从基础的单表查询操作到能够进行多表之间的数据查询分析,掌握SQL语言的SELECT功能及其有关查询关键字的用法。