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.

Exploring features for automatic identification of news queries through query logs

Purpose:Existing researches of predicting queries with news intents have tried to extract the classification features from external knowledge bases,this paper tries to present how to apply features extracted from query logs for automatic identification of news queries without using any external resources.Design/methodology/approach:First,we manually labeled 1,220 news queries from Sogou.com.Based on the analysis of these queries,we then identified three features of news queries in terms of query content,time of query occurrence and user click behavior.Afterwards,we used 12 effective features proposed in literature as baseline and conducted experiments based on the support vector machine(SVM)classifier.Finally,we compared the impacts of the features used in this paper on the identification of news queries.Findings:Compared with baseline features,the F-score has been improved from 0.6414 to0.8368 after the use of three newly-identified features,among which the burst point(bst)was the most effective while predicting news queries.In addition,query expression(qes)was more useful than query terms,and among the click behavior-based features,news URL was the most effective one.Research limitations:Analyses based on features extracted from query logs might lead to produce limited results.Instead of short queries,the segmentation tool used in this study has been more widely applied for long texts.Practical implications:The research will be helpful for general-purpose search engines to address search intents for news events.Originality/value:Our approach provides a new and different perspective in recognizing queries with news intent without such large news corpora as blogs or Twitter.

VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

Embedding-based approximate query for knowledge graph

To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes.This classification transforms the query problem into three constraints,from which approximate information is extracted.Second,candidates are generated by calculating the similarity between embeddings.Finally,a deep neural network model is designed,incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance.This model identifies the distance between nodes using their embeddings and constructs a score function.k nodes are returned as the query results.The results show that the proposed method can return both exact results and approximate matching results.On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse),this method exhibits superior performance in terms of precision and recall,returning results in 0.10 and 0.03 s,respectively.This indicates greater efficiency compared to PathSim and other comparative methods.

Learned Distributed Query Optimizer:Architecture and Challenges

The query processing in distributed database management systems(DBMS)faces more challenges,such as more operators,and more factors in cost models and meta-data,than that in a single-node DMBS,in which query optimization is already an NP-hard problem.Learned query optimizers(mainly in the single-node DBMS)receive attention due to its capability to capture data distributions and flexible ways to avoid hard-craft rules in refinement and adaptation to new hardware.In this paper,we focus on extensions of learned query optimizers to distributed DBMSs.Specifically,we propose one possible but general architecture of the learned query optimizer in the distributed context and highlight differences from the learned optimizer in the single-node ones.In addition,we discuss the challenges and possible solutions.

A Systematic Review of Automated Classification for Simple and Complex Query SQL on NoSQL Database

A data lake(DL),abbreviated as DL,denotes a vast reservoir or repository of data.It accumulates substantial volumes of data and employs advanced analytics to correlate data from diverse origins containing various forms of semi-structured,structured,and unstructured information.These systems use a flat architecture and run different types of data analytics.NoSQL databases are nontabular and store data in a different manner than the relational table.NoSQL databases come in various forms,including key-value pairs,documents,wide columns,and graphs,each based on its data model.They offer simpler scalability and generally outperform traditional relational databases.While NoSQL databases can store diverse data types,they lack full support for atomicity,consistency,isolation,and durability features found in relational databases.Consequently,employing machine learning approaches becomes necessary to categorize complex structured query language(SQL)queries.Results indicate that the most frequently used automatic classification technique in processing SQL queries on NoSQL databases is machine learning-based classification.Overall,this study provides an overview of the automatic classification techniques used in processing SQL queries on NoSQL databases.Understanding these techniques can aid in the development of effective and efficient NoSQL database applications.

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.

基于XQuery的GML查询语言研究

随着GML规范的不断完善及GIS软件厂商的广泛支持,越来越多的空间数据以GML格式存储,GML空间数据的查询已成为GIS研究的热点问题。传统的关系数据库查询语言SQL是针对平面的二维关系数据而设计的,并不适合XML/GML半结构化数据的查询;商品化GIS软件的查询系统只能查询自身的空间数据而无法查询其它GIS系统的空间数据;XML查询的研究为GML查询奠定了一定的基础。首先针对GML查询存在的问题,提出了扩展XQuery是GML查询语言实现的最佳选择;结合XML查询语言和空间数据查询语言,提出了GML查询语言的特征和GML查询语言系统框架;并根据GML空间数据的特点,以XML标准查询语言XQuery为基础,提出了XQuery空间扩展的内容;开发了GML空间数据查询语言GMLXQL,实现了GML空间数据的本原查询。

用Java来设计组件重用的Query方法

文章所探讨的组件重用的Query方法是利用Java反射技术和Java数据库连接技术来完成叶数据库的查询操作。它将查询数据库的公共操作封装于一个组件中,使编程工作者在具体编程工作中能方便地重复使用它们,以求简化编程工作。

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.

Optimization of RDF link traversal based query execution

Aiming at the problem that only some types of SPARQL （ simple protocal and resource description framework query language） queries can be answered by using the current resource description framework link traversal based query execution （RDF-LTE） approach, this paper discusses how the execution order of the triple pattern affects the query results and cost based on concrete SPARQL queries, and analyzes two properties of the web of linked data, missing backward links and missing contingency solution. Then three heuristic principles for logic query plan optimization, namely, the filtered basic graph pattern （FBGP） principle, the triple pattern chain principle and the seed URIs principle, are proposed. The three principles contribute to decrease the intermediate solutions and increase the types of queries that can be answered. The effectiveness and feasibility of the proposed approach is evaluated. The experimental results show that more query results can be returned with less cost, thus enabling users to develop the full potential of the web of linked data.

Query Expansion for Chinese Information Retrieval by Using a Decaying Co-occurrence Model

Query expansion with thesaurus is one of the useful techniques in modern information retrieval (IR). In this paper, a method of query expansion for Chinese IR by using a decaying co-occurrence model is proposed and realized. The model is an extension of the traditional co-occurrence model by adding a decaying factor that decreases the mutual information when the distance between the terms increases. Experimental results on TREC-9 collections show this query expansion method results in significant improvements over the IR without query expansion.

AnyQuery:网格环境下基于服务的分布式查询处理系统

AnyQuery是一种适用于网格环境、基于服务的分布式查询处理系统,使用统一的数据格式(可扩展标记语言)来表示和传输数据,所有组成模块以网格服务的形式实现,网格服务之间通过标准的接口进行通信.这种接口和实现分离技术屏蔽了不同数据库及其运行节点的差异,为用户提供一致的虚拟关系数据视图和分布式数据查询支持.在分析和研究已有的并行数据库和分布式数据库查询技术的基础上,AnyQuery提出和实现了一种适用于动态网格环境的分布式查询计划生成和执行算法,该算法能够根据网格环境中各节点及其连接网络的实际情况确定查询计划,支持跨数据库的连接查询.基于Globus3.0开发了AnyQuery的原型系统,性能测试表明该系统具有较好的可扩展性,尤其适合计算密集型的复杂查询.

XQuery——一种全新的XML查询语言

随着XML被广泛接受和使用 ,如何从XML数据源中准确有效地查询所需信息 ,也就变得越来越重要。本文介绍了一种全新XML查询语言—XQuery ,阐述了XQuery的结论、应用范围。

基于JQuery的自动完成功能的实现

当客户在利用Goolge或Baidu搜索引擎在网上搜索信息时,自动完成功能给大家带来了很大的便利。随着Ajax技术的迅速发展,自动完成功能在不同的框架中都得到了一定程度的实现。以某精密机械有限公司ERP系统的制定年业务计划模块为例,详细介绍通过JQuery-aucomplete插件和基于文本的数据交换格式JSON实现基于JQuery的自动完成功能的方法。

企业信息集成中XQuery引擎的查询优化

通过分析研究已有的XQuery引擎和XQ uery查询优化的方法,从减少编译时间和执行时间角度出发,提出了一种基于XQuery语句自身优化的新型综合性优化方法,经过XQuery中某常见查询进行仿真实验,可以说明该新型综合优化方法有较强的优势,可以大大地提高XQuery引擎的查询效率。

APPROXIMATE QUERY AND CALCULATION OF RNN_k BASED ON VORONOI CELL

Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.

XML查询语言XQuery的研究与实现

XQuery是一种对XML结构的文档和数据进行查询的语言。在对该查询语言规范体系分析、理解和研究的基础上,提出了支持W3C的XQuery语言的查询处理引擎的体系结构。针对各个输入输出和处理模块按数据流的方式逐一进行分析,对整个系统的运行状态做了一个总体的介绍。按照这个体系结构,一个XQuery查询处理引擎已经被实现。

XQuery语言的实现

首先分析了万维网联合会(W3C)提出的XML查询语言XQuery语言的特点,提出了支持W3C的XQuery语言查询处理引擎的体系结构。简要介绍了基于该体系结构所实现的XQuery处理引擎的词法/语法解析、语法转换、类型检查和分析、赋值计算等四个处理阶段涉及的主要内容。

基于XQuery查询优化的研究

XML已经成为网络上信息描述和信息交换的标准,随着XML应用得越来越广泛,人们提出了多种XML数据的查询方法。但是,很多查询方法都有各自的局限性。利用有意义的最小公共实体结构EntityInMLCAS(Entity In Meaning Lowest Common An- cestor Structure)原理,提出一种优化查询方法。同时采用堆栈技术的方法对XML文档结点树进行自顶向下的遍历,该方法大大提高了优化查询的速度。