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.

基于改进K-NN变电运维智能巡检关键技术研究

为了进一步提高变电站的安全性和可靠性,本文对变电站智能运维中的图像识别技术展开了探究。结果表明,本文设计的特征提取方式的特征提取准确率高达0.943,特征点重复率高达0.938,对图像的识别分类准确率达96.47%,同时具备较高的计算效率,计算耗时达5.89s。本文提升了变电站的运行效率与管理水平,有助于推进智能变电站发展进程,保证变电站的运行安全。

基于k-NN和Landsat数据的小面积统计单元森林蓄积估测方法

基于吉林省一个试验区的森林资源一类清查固定样地数据、Landsat TM数据和土地利用数据,采用精度交叉评价方法研究了k-最近邻(k-NN)法用于小面积统计单元森林蓄积估计的有效性。结果表明:k-NN方法对样地覆盖区影像像元单位面积蓄积量的估测平均误差在1.5 m3.hm2之内,相对均方根误差(RMSE′)低于传统的基于绿度指数的线性方程估测方法;采用k-NN方法可以实现县市级统计单元的参数估计,估测效果优于只利用固定样地数据的传统成数估计方法。

面向双层传感网的隐私保护k-NN查询处理协议

无线传感器网络作为物联网感知层的核心组成部分,具有广阔的应用前景.然而,隐私泄露问题严重阻碍了传感器网络的发展.目前,传感器网络隐私保护技术已成为研究热点,其中隐私保护κ-NN(κ-Nearest Neighbor)查询协议是富有挑战性的问题.文中提出了面向双层传感器网络的高效的隐私保护κ-NN查询协议.首先,为提升查询效率,基于定向存储策略给出了适用于双层传感网的κ-NN查询架构.其次,针对管理节点俘获攻击,提出了一种新颖的隐私保护数据编码机制,通过为真实数据附加编码的方式,保证在不泄露数据隐私的同时精确地完成查询处理.再次,针对节点共谋攻击,设计了基于代理节点的单向数据隐藏机制,通过破坏普通节点与管理节点间数据的关联性实现抵御共谋攻击的目标.理论分析和仿真实验验证了协议的安全性和有效性.

一种基于k-NN的案例相似度权重调整算法

对于CBR中的案例检索问题,结合经典案例相似度计算方法,对目前在各实际系统中应用最为广泛的k-NN算法进行改进。经过特征约简,在假设时间因素对历史案例可采纳程度有显著影响基础上,提出了一种小规模的基于时序的案例特征权重多阶段调整算法。该算法适用于数值型特征项相似度计算。

一种无线传感器网络中的多维K-NN查询优化算法(英文)

提出了一种基于过滤器的无线传感器网络多维K-NN查询优化算法PREDICTOR.过滤器是设置在节点端的取值分布区间,用来屏蔽节点发送属于区间内的数据,从而节省节点能耗.在服务器端保存有各节点的历史样本数据,根据K-NN查询请求和样本数据的分布范围为节点定义过滤器.提出了3种优化策略:(1)过滤器覆盖区间大小分配策略的动态调整方法,使得进入最终查询结果可能性小的节点拥有较大的覆盖区间;(2)节点间过滤器共享方法,使得历史样本数据相近的节点使用相同的过滤器;(3)过滤器压缩传输方法,减少为不同K-NN查询更新过滤器的代价.通过实验评价,验证了PREDICTOR算法的能量有效性,与朴素算法相比,极大地降低了数据传输量.

一种k-NN文本分类器的改进方法

自动文本分类是提高信息利用效率和质量的有效方法。训练文本分布的不均匀会对分类的效果产生负面影响，而在实际中，很难使训练文本的分布达到均匀。针对这一问题，提出了一种改进的k-NN文本分类方法。通过在英文和中文两个文本集的实验表明，改进后的方法不仅分类的准确性有了提高，而且表现出较好的稳定性。

基于k-NN算法的叶面积指数遥感反演

叶面积指数(Leaf Area Index,LAI)作为植被冠层结构的重要描述参数之一,能体现植被光合、蒸腾和呼吸作用的能力。借助GPS和LAI-2200冠层分析仪在攸县黄丰桥林场开展LAI测量。利用ENVI软件对Geo Eye-1数据进行了辐射定标,大气校正和正射校正。通过研究LAI与Geo Eye-1影像波段及其衍生指数的相关性,筛选出2组估算LAI的指数因子(6个指数因子和10个指数因子)。应用k-NN进行叶面积指数反演,同时将反演结果与多元线性回归模型结果进行比较。结果表明:利用2组指数因子进行多元线性回归模型反演LAI中,6个指数因子的模型决定系数R2为0.386,10个指数因子的模型决定系数R2为0.498。从回归模拟的角度分析,10个指数因子得到的模拟结果要优于6个指数因子的模拟结果。利用2组指数因子通过设置4个不同的k值(k=3,5,7,10)得到8个k-NN反演结果中,以10个指数因子得到的k-NN反演结果较好,其中在k=3时效果最好,其决定系数R2为0.733,精度为85.4%。建模精度分析表明选用10个指数因子进行LAI的反演优于选用6个指数因子,其中k-NN方法的反演结果优于多元线性回归模型,说明利用k-NN方法进行LAI的反演是可行的。

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。

k-nn方法在热带气旋路径预报中的应用

引入k nn方法 ,结合经过预报实践检验、应用证明效果较好的预报因子 ,制作热带气旋路径预报系统 ,运行效果良好 ,可以应用到日常预报业务中。

一种优化的k-NN文本分类算法

k-NN是经典的文本分类算法之一,在解决概念漂移问题上尤其具有优势,但其运行速度低下的缺点也非常严重,为此它通常借助特征选择降维方法来避免维度灾难、提高运行效率。但特征选择又会引起信息丢失等问题,不利于分类系统整体性能的提高。从文本向量的稀疏性特点出发,对传统的k-NN算法进行了诸多优化。优化算法简化了欧氏距离分类模型,大大降低了系统的运算开销,使运行效率有了质的提高。此外,优化算法还舍弃了特征选择预处理过程,从而可以完全避免因特征选择而引起的诸多不利问题,其分类性能也远远超出了普通k-NN。实验显示,优化算法在性能与效率双方面都有非常优秀的表现,它为传统的k-NN算法注入了新的活力,并可以在解决概念漂移等问题上发挥更大的作用。

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.